PATENT DOCUMENT

Publication Number: US-9041819-B2
Application Number: US-201113298337-A
Country: US
Kind Code: B2

Title: Method for stabilizing a digital video

Abstract:
A method for stabilizing an input digital video. Input camera positions are determined for each of the input video frames, and an input camera path is determined representing input camera position as a function of time. A smoothing operation is applied to the input camera path to determine a smoothed camera path, and a corresponding sequence of smoothed camera positions. A stabilized video frame is determined corresponding to each of the smoothed camera positions by: selecting an input video frame having a camera position near to the smoothed camera position; warping the selected input video frame responsive to the input camera position; warping a set of complementary video frames captured from different camera positions than the selected input video frame; and combining the warped input video frame and the warped complementary video frames to form the stabilized video frame.

Claims:
The invention claimed is: 
     
       1. A method for stabilizing a digital video, the method implemented at least in part by a data processing system and comprising:
 receiving an input digital video captured with a digital video camera including a temporal sequence of input video frames; 
 determining input camera positions for each of the input video frames, each input camera position including a three-dimensional location and a pointing direction; 
 determining an input camera path for the input digital video representing one of the input camera positions as a function of time; 
 applying a smoothing operation to the input camera path to determine a first smoothed camera path; 
 determining a first sequence of smoothed camera positions corresponding to the first smoothed camera path; 
 determining a stabilized video frame corresponding to each of the smoothed camera positions, each stabilized video frame being determined by:
 selecting an input video frame having a camera position near to the smoothed camera position; 
 warping the selected input video frame responsive to the input camera position for the selected input video frame and the smoothed camera position to form a warped input video frame; 
 warping a set of complementary video frames captured from different camera positions than the selected input video frame responsive to the respective camera positions and the smoothed camera position to form warped complementary video frames; and 
 combining the warped input video frame and the warped complementary video frames to form the stabilized video frame; 
 
 forming a stabilized digital video corresponding to a sequence of stabilized video frames; and 
 storing the stabilized digital video in a processor-accessible memory. 
 
     
     
       2. The method of  claim 1  wherein range maps are determined for the input video frames and the process of determining the warped input video frames and the warped complementary video frames is also responsive to the corresponding range maps. 
     
     
       3. The method of  claim 1  wherein at least some of the warped input video frames include one or more holes corresponding to scene content that was occluded in the corresponding selected input video frame, and wherein the warped input video frame and the warped complementary video frames are combined by filling the one or more holes in the warped input video frame using pixel values from corresponding pixel locations in the warped complementary video frames. 
     
     
       4. The method of  claim 1  wherein the input camera positions are determined by automatically analyzing the sequence of input video frames. 
     
     
       5. The method of  claim 1  wherein the input camera positions are determined using position sensors in the digital video camera. 
     
     
       6. The method of  claim 1  wherein the selected input video frame is the input video frame having a camera position that is closest to the smoothed camera position. 
     
     
       7. The method of  claim 1  further including adding random variations to the smoothed camera path. 
     
     
       8. The method of  claim 7  wherein an amplitude and a temporal frequency content of the random variations are selected to be consistent with a hand-held video. 
     
     
       9. The method of  claim 1  wherein the warped input video frame and the warped complementary video frames are determined using a pixel-level depth-based projection algorithm. 
     
     
       10. The method of  claim 1  further including:
 determining an input magnification value for each of the input video frames;
 determining a smoothed magnification value for each stabilized video frame; and 
 adjusting the magnification of each stabilized video frame according to the corresponding smoothed magnification value. 
 
 
     
     
       11. The method of  claim 1  wherein the stabilized digital video corresponds to a first-eye video sequence for a stereoscopic digital video that includes a first-eye video sequence and a second-eye video sequence, and further including:
 determining a second smoothed camera path for the second-eye video sequence, wherein the second smoothed camera path includes a second sequence of smoothed camera positions; 
 determining stabilized video frames for the second-eye video sequence corresponding to each of the smoothed camera positions in the second smoothed camera path; 
 forming the stereoscopic digital video by combining the first-eye digital video and the second-eye digital video; and 
 storing the stereoscopic digital video in a processor-accessible memory. 
 
     
     
       12. The method of  claim 11  wherein camera locations associated with the smoothed camera positions for the second smoothed camera path are shifted by a predefined spatial increment relative to camera locations associated with the corresponding smoothed camera positions for the first smoothed camera path. 
     
     
       13. The method of  claim 11  wherein pointing directions associated with the smoothed camera positions for the second smoothed camera path are equal to pointing directions for corresponding smoothed camera positions for the first smoothed camera path. 
     
     
       14. An electronic device, comprising:
 an image sensor; 
 memory coupled to the image sensor; 
 display unit coupled to the memory; 
 one or more processors operatively coupled to the image sensor, memory, and display unit and engineered to execute computer instructions stored in the memory to cause the electronic device to—
 receive an input digital video captured from the image sensor, wherein the input digital video includes a temporal sequence of input video frames; 
 determine input positions of the electronic device for each of the input video frames, each input position including a three-dimensional location and a pointing direction; 
 determine an input path of the electronic device for the input digital video representing one of the input positions as a function of time; 
 apply a smoothing operation to the input path to determine a first smoothed path of the electronic device; 
 determine a first sequence of smoothed positions corresponding to the first smoothed path; 
 determine a stabilized video frame corresponding to each of the smoothed positions, each stabilized video frame being determined by computer instructions stored in the memory that, when executed by the one or more processors, cause the electronic device to—
 select an input video frame having an input position near the smoothed position; 
 warp the selected input video frame responsive to the input position for the selected input video frame and the smoothed position to form a warped input video frame; 
 warp a set of complementary video frames captured from different positions than the selected input video frame responsive to the respective positions and the smoothed position to form warped complementary video frames; and 
 combine the warped input video frame and the warped complementary video frames to form the stabilized video frame; 
 
 form a stabilized digital video corresponding to a sequence of stabilized video frames; and 
 store the stabilized digital video in the memory. 
 
 
     
     
       15. The electronic device of  claim 14  wherein at least some of the warped input video frames include one or more holes corresponding to scene content that was occluded in the corresponding selected input video frame, and wherein the warped input video frame and the warped complementary video frames are combined by filling the one or more holes in the warped input video frame using pixel values from corresponding pixel locations in the warped complementary video frames. 
     
     
       16. The electronic device of  claim 14  wherein the computer instructions to determine input positions comprise computer instructions to determine input positions by automatically analyzing the sequence of input video frames. 
     
     
       17. The electronic device of  claim 14  further comprising computer instructions to:
 determine an input magnification value for each of the input video frames; 
 determine a smoothed magnification value for each stabilized video frame; and 
 adjust the magnification of each stabilized video frame according to the corresponding smoothed magnification value. 
 
     
     
       18. The electronic device of  claim 14  wherein the stabilized digital video corresponds to a first-eye video sequence for a stereoscopic digital video that includes a first-eye video sequence and a second-eye video sequence, and further comprising computer instructions to:
 determine a second smoothed path for the second-eye video sequence, wherein the second smoothed path includes a second sequence of smoothed positions; 
 determine stabilized video frames for the second-eye video sequence corresponding to each of the smoothed positions in the second smoothed path; 
 form the stereoscopic digital video by combining the first-eye digital video and the second-eye digital video; and 
 store the stereoscopic digital video in the memory. 
 
     
     
       19. A non-transitory program storage device comprising instructions stored thereon to cause one or more processors to:
 receive an input digital video that includes a temporal sequence of input video frames; 
 determine input positions of an electronic device that captured the input digital video for each of the input video frames, each input position including a three-dimensional location and a pointing direction; 
 determine an input path of the electronic device that captured the input digital video for the input digital video representing one of the input positions as a function of time; 
 apply a smoothing operation to the input path to determine a first smoothed path of the electronic device that captured the input digital video; 
 determine a first sequence of smoothed positions corresponding to the first smoothed path; 
 determine a stabilized video frame corresponding to each of the smoothed positions, each stabilized video frame being determined by computer instructions to cause the one or more processors to—
 select an input video frame having an input position near the smoothed position; 
 warp the selected input video frame responsive to the input position for the selected input video frame and the smoothed position to form a warped input video frame; 
 warp a set of complementary video frames captured from different positions than the selected input video frame responsive to the respective positions and the smoothed position to form warped complementary video frames; and 
 combine the warped input video frame and the warped complementary video frames to form the stabilized video frame; 
 
 form a stabilized digital video corresponding to a sequence of stabilized video frames; and 
 display the stabilized digital video on a display unit. 
 
     
     
       20. The non-transitory program storage device of  claim 19  wherein the stabilized digital video corresponds to a first-eye video sequence for a stereoscopic digital video that includes a first-eye video sequence and a second-eye video sequence, the non-transitory program storage device further comprising instructions stored thereon to cause the one or more processors to:
 determine a second smoothed path for the second-eye video sequence, wherein the second smoothed path includes a second sequence of smoothed positions; 
 determine stabilized video frames for the second-eye video sequence corresponding to each of the smoothed positions in the second smoothed path; 
 form the stereoscopic digital video by combining the first-eye digital video and the second-eye digital video; and 
 store the stereoscopic digital video in a processor-accessible memory.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     Reference is made to, co-pending U.S. patent application Ser. No. 13/004,207, entitled “Forming 3D models using periodic illumination patterns” to Kane et al.; commonly assigned, co-pending U.S. patent application Ser. No. 13/298,328, entitled: “Range map determination for a video frame”, by Wang et al.; to, co-pending U.S. patent application Ser. No. 13/298,332, entitled: “Modifying the viewpoint of a digital image”, by Wang et al.; and to commonly assigned, co-pending U.S. patent application Ser. No. 13/298,334, entitled: “Forming a stereoscopic image using range map” by Wang et al., each of which is incorporated herein by reference. 
     FIELD OF THE INVENTION 
     This invention pertains to the field of digital imaging and more particularly to a method for stabilizing a digital video. 
     BACKGROUND OF THE INVENTION 
     Stereoscopic videos are regarded as the next prevalent media for movies, TV programs, and video games. Three-dimensional (3-D) movies have achieved great successes in providing extremely vivid visual experiences. The fast developments of stereoscopic display technologies and popularization of 3-D television has inspired people&#39;s desires to record their own 3-D videos and display them at home. However, professional stereoscopic recording cameras are very rare and expensive. Meanwhile, there is a great demand to perform 3-D conversion on legacy two-dimensional (2-D) videos. Unfortunately, specialized and complicated interactive 3-D conversion processes currently required, which has prevented the general public from converting captured 2-D videos to 3-D videos. Thus, it is a significant goal to develop an approach to automatically synthesize stereoscopic video from a casual monocular video. 
     Much research has been devoted to 2-D to 3-D conversion techniques for the purposes of generating stereoscopic videos, and significant progress has been made in this area. Fundamentally, the process of generating stereoscopic videos involves synthesizing the synchronized left and right stereo view sequences based on an original monocular view sequence. Although it is an ill-posed problem, a number of approaches have been designed to address it. Such approaches generally involve the use of human-interaction or other priors. According to the level of human assistance, these approaches can be categorized as manual, semiautomatic or automatic techniques. Manual and semiautomatic methods typically involve an enormous level of human annotation work. Automatic methods utilize extracted 3-D geometry information to synthesis new views for virtual left-eye and right-eye images. 
     Manual approaches typically involve manually assigning different disparity values to pixels of different objects, and then shifting these pixels horizontally by their disparities to produce a sense of parallax. Any holes generated by this shifting operation are filled manually with appropriate pixels. An example of such an approach is described by Harman in the article “Home-based 3-D entertainment—an overview” (Proc. International Conference on Image Processing, Vol., 1, pp. 1-4, 2000). These methods generally require extensive and time-consuming human interaction. 
     Semi-automatic approaches only require the users to manually label a sparse set of 3-D information (e.g., with user marked scribbles or strokes) for some a subset of the video frames for a given shot (e.g., the first and last video frames, or key-video frames) to obtain the dense disparity or depth map. Examples of such techniques are described by Guttmann et al. in the article “Semi-automatic stereo extraction from video footage” (Proc. IEEE 12th International Conference on Computer Vision, pp. 136-142, 2009) and by Cao et al. in the article “Semi-automatic 2-D-to-3-D conversion using disparity propagation” (IEEE Trans. on Broadcasting, Vol. 57, pp. 491-499, 2011). The 3-D information for other video frames is propagated from the manually labeled frames. However, the results may degrade significantly if the video frames in one shot are not very similar. Moreover, these methods can only apply to the simple scenes, which only have a few depth layers, such as foreground and background layers. Otherwise, extensive human annotations are still required to discriminate each depth layer. 
     Automatic approaches can be classified into two categories: non-geometric and geometric methods. Non-geometric methods directly render new virtual views from one nearby video frame in the monocular video sequence. One method of the type is the time-shifting approach described by Zhang et al. in the article “Stereoscopic video synthesis from a monocular video” (IEEE Trans. Visualization and Computer Graphics, Vol. 13, pp. 686-696, 2007). Such methods generally require the original video to be an over-captured images set. They also are unable to preserve the 3-D geometry information of the scene. 
     Geometric methods generally consists of two main steps: exploration of underline 3-D geometry information and synthesis new virtual view. For some simple scenes captured under stringent conditions, the full and accurate 3-D geometry information (e.g., a 3-D model) can be recovered as described by Pollefeys et al. in the article “Visual modeling with a handheld camera” (International Journal of Computer Vision, Vol. 59, pp. 207-232, 2004). Then, a new view can be rendered using conventional computer graphics techniques. 
     In most cases, only some of the 3-D geometry information can be obtained from monocular videos, such as a depth map (see: Zhang et al., “Consistent depth maps recovery from a video sequence,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 31, pp. 974-988, 2009) or a sparse 3-D scene structure (see: Zhang et al., “3D-TV content creation: automatic 2-D-to-3-D video conversion,” IEEE Trans. on Broadcasting, Vol. 57, pp. 372-383, 2011). Image-based rendering (IBR) techniques are then commonly used to synthesize new views (for example, see the article by Zitnick entitled “Stereo for image-based rendering using image over-segmentation” International Journal of Computer Vision, Vol. 75, pp. 49-65, 2006, and the article by Fehn entitled “Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV,” Proc. SPIE, Vol. 5291, pp. 93-104, 2004). 
     With accurate geometry information, methods like light field (see: Levoy et al., “Light field rendering,” Proc. SIGGRAPH &#39;96, pp. 31-42, 1996), lumigraph (see: Gortler et al., “The lumigraph,” Proc. SIGGRAPH &#39;96, pp. 43-54, 1996), view interpolation (see: Chen et al., “View interpolation for image synthesis,” Proc. SIGGRAPH &#39;93, pp. 279-288, 1993) and layered-depth images (see: Shade et al., “Layered depth images,” Proc. SIGGRAPH &#39;98, pp. 231-242, 1998) can be used to synthesize reasonable new views by sampling and smoothing the scene. However, most IBR methods either synthesize a new view from only one original frame using little geometry information, or require accurate geometry information to fuse multiple frames. 
     Existing Automatic approaches unavoidably confront two key challenges. First, geometry information estimated from monocular videos are not very accurate, which can&#39;t meet the requirement for current image-based rendering (IBR) methods. Examples of IBR methods are described by Zitnick et al. in the aforementioned article “Stereo for image-based rendering using image over-segmentation,” and by Fehn in the aforementioned article “Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV.” Such methods synthesize new virtual views by fetching the exact corresponding pixels in other existing frames. Thus, they can only synthesize good virtual view images based on accurate pixel correspondence map between the virtual views and original frames, which needs precise 3-D geometry information (e.g., dense depth map, and accurate camera parameters). While the required 3-D geometry information can be calculated from multiple synchronized and calibrated cameras as described by Zitnick et al. in the article “High-quality video view interpolation using a layered representation” (ACM Transactions on Graphics, Vol. 23, pp. 600-608, 2004), the determination of such information from a normal monocular video is still quite error-prone. 
     Furthermore, the image quality that results from the synthesis of virtual views is typically degraded due to occlusion/disocclusion problems. Because of the parallax characteristics associated with different views, holes will be generated at the boundaries of occlusion/disocclusion objects when one view is warped to another view in 3-D. Lacking accurate 3-D geometry information, hole filling approaches are not able to blend information from multiple original frames. As a result, they ignore the underlying connections between frames, and generally perform smoothing-like methods to fill holes. Examples of such methods include view interpolation (See the aforementioned article by Chen et al. entitled “View interpolation for image synthesis”), extrapolation techniques (see: the aforementioned article by Cao et al. entitled “Semi-automatic 2-D-to-3-D conversion using disparity propagation”) and median filter techniques (see: Knorr et al., “Super-resolution stereo- and multi-view synthesis from monocular video sequences,” Proc. Sixth International Conference on 3-D Digital Imaging and Modeling, pp. 55-64, 2007). Theoretically, these methods cannot obtain the exact information for the missing pixels from other frames, and thus it is difficult to fill the holes correctly. In practice, the boundaries of occlusion/disocclusion objects will be blurred greatly, which will thus degrade the visual experience. 
     SUMMARY OF THE INVENTION 
     The present invention represents a method for stabilizing a digital video, the method implemented at least in part by a data processing system and comprising:
         receiving an input digital video captured with a digital video camera including a temporal sequence of input video frames;   determining input camera positions for each of the input video frames, each input camera position including a three-dimensional location and a pointing direction;   determining an input camera path for the input digital video representing input camera position as a function of time;   applying a smoothing operation to the input camera path to determine a smoothed camera path;   determining a sequence of smoothed camera positions corresponding to the smoothed camera path;   determining a stabilized video frame corresponding to each of the smoothed camera positions, each stabilized video frame being determined by:
           selecting an input video frame having a camera position near to the smoothed camera position;   warping the selected input video frame responsive to the input camera position for the selected input video frame and the smoothed camera position to form a warped input video frame;   warping a set of complementary video frames captured from different camera positions than the selected input video frame and the smoothed camera position to form warped complementary video frames; and   combining the warped input video frame and the warped complementary video frames to form the stabilized video frame;   
           forming a stabilized digital video corresponding to a sequence of stabilized video frames; and   storing the stabilized digital video in a processor-accessible memory.       

     This invention has the advantage that a stabilized digital video can be formed from an original unstable digital video, such as one captured on a video camera without an image stabilization feature. 
     It has the additional advantage that frames for the stabilized digital video are generated by efficiently blending multiple frames with inaccurate 3-D geometry information, and provide more accurate and natural views than other prior art methods. 
     It has the further advantage that it can be used to form stabilized stereoscopic videos. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a high-level diagram showing the components of a system for processing digital images according to an embodiment of the present invention; 
         FIG. 2  is a flow chart illustrating a method for determining range maps for frames of a digital video; 
         FIG. 3  is a flowchart showing additional details for the determine disparity maps step of  FIG. 2 ; 
         FIG. 4  is a flowchart of a method for determining a stabilized video from an input digital video; 
         FIG. 5  shows a graph of a smoothed camera path; 
         FIG. 6  is a flow chart of a method for modifying the viewpoint of a main image of a scene; 
         FIG. 7  shows a graph comparing the performance of the present invention to two prior art methods; and 
         FIG. 8  is a flowchart of a method for forming a stereoscopic image from a monoscopic main image and a corresponding range map. 
     
    
    
     It is to be understood that the attached drawings are for purposes of illustrating the concepts of the invention and may not be to scale. 
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following description, some embodiments of the present invention will be described in terms that would ordinarily be implemented as software programs. Those skilled in the art will readily recognize that the equivalent of such software may also be constructed in hardware. Because image manipulation algorithms and systems are well known, the present description will be directed in particular to algorithms and systems forming part of, or cooperating more directly with, the method in accordance with the present invention. Other aspects of such algorithms and systems, together with hardware and software for producing and otherwise processing the image signals involved therewith, not specifically shown or described herein may be selected from such systems, algorithms, components, and elements known in the art. Given the system as described according to the invention in the following, software not specifically shown, suggested, or described herein that is useful for implementation of the invention is conventional and within the ordinary skill in such arts. 
     The invention is inclusive of combinations of the embodiments described herein. References to “a particular embodiment” and the like refer to features that are present in at least one embodiment of the invention. Separate references to “an embodiment” or “particular embodiments” or the like do not necessarily refer to the same embodiment or embodiments; however, such embodiments are not mutually exclusive, unless so indicated or as are readily apparent to one of skill in the art. The use of singular or plural in referring to the “method” or “methods” and the like is not limiting. It should be noted that, unless otherwise explicitly noted or required by context, the word “or” is used in this disclosure in a non-exclusive sense. 
       FIG. 1  is a high-level diagram showing the components of a system for processing digital images according to an embodiment of the present invention. The system includes a data processing system  110 , a peripheral system  120 , a user interface system  130 , and a data storage system  140 . The peripheral system  120 , the user interface system  130  and the data storage system  140  are communicatively connected to the data processing system  110 . 
     The data processing system  110  includes one or more data processing devices that implement the processes of the various embodiments of the present invention, including the example processes described herein. The phrases “data processing device” or “data processor” are intended to include any data processing device, such as a central processing unit (“CPU”), a desktop computer, a laptop computer, a mainframe computer, a personal digital assistant, a Blackberry™, a digital camera, cellular phone, or any other device for processing data, managing data, or handling data, whether implemented with electrical, magnetic, optical, biological components, or otherwise. 
     The data storage system  140  includes one or more processor-accessible memories configured to store information, including the information needed to execute the processes of the various embodiments of the present invention, including the example processes described herein. The data storage system  140  may be a distributed processor-accessible memory system including multiple processor-accessible memories communicatively connected to the data processing system  110  via a plurality of computers or devices. On the other hand, the data storage system  140  need not be a distributed processor-accessible memory system and, consequently, may include one or more processor-accessible memories located within a single data processor or device. 
     The phrase “processor-accessible memory” is intended to include any processor-accessible data storage device, whether volatile or nonvolatile, electronic, magnetic, optical, or otherwise, including but not limited to, registers, floppy disks, hard disks, Compact Discs, DVDs, flash memories, ROMs, and RAMs. 
     The phrase “communicatively connected” is intended to include any type of connection, whether wired or wireless, between devices, data processors, or programs in which data may be communicated. The phrase “communicatively connected” is intended to include a connection between devices or programs within a single data processor, a connection between devices or programs located in different data processors, and a connection between devices not located in data processors at all. In this regard, although the data storage system  140  is shown separately from the data processing system  110 , one skilled in the art will appreciate that the data storage system  140  may be stored completely or partially within the data processing system  110 . Further in this regard, although the peripheral system  120  and the user interface system  130  are shown separately from the data processing system  110 , one skilled in the art will appreciate that one or both of such systems may be stored completely or partially within the data processing system  110 . 
     The peripheral system  120  may include one or more devices configured to provide digital content records to the data processing system  110 . For example, the peripheral system  120  may include digital still cameras, digital video cameras, cellular phones, or other data processors. The data processing system  110 , upon receipt of digital content records from a device in the peripheral system  120 , may store such digital content records in the data storage system  140 . 
     The user interface system  130  may include a mouse, a keyboard, another computer, or any device or combination of devices from which data is input to the data processing system  110 . In this regard, although the peripheral system  120  is shown separately from the user interface system  130 , the peripheral system  120  may be included as part of the user interface system  130 . 
     The user interface system  130  also may include a display device, a processor-accessible memory, or any device or combination of devices to which data is output by the data processing system  110 . In this regard, if the user interface system  130  includes a processor-accessible memory, such memory may be part of the data storage system  140  even though the user interface system  130  and the data storage system  140  are shown separately in  FIG. 1 . 
     As discussed in the background of the invention, one of the problems in synthesizing a new view of an image are holes that result from occlusions when an image frame is warped to form the new view. Fortunately, a particular object generally shows up in a series of consecutive video frames in a continuously captured video. As a result, a particular 3-D point in the scene will generally be captured in several consecutive video frames with similar color appearances. To get a high quality synthesized new view, the missing information for the holes can therefore be found in other video frames. The pixel correspondences between adjacent frames can be used to form a color consistency constraint. Thus, various 3-D geometric cures can be integrated to eliminate ambiguity in the pixel correspondences. Accordingly, it is possible to synthesize a new virtual view accurately even using error-prone 3-D geometry information. 
     In accordance with the present invention a method is described to automatically generate stereoscopic videos from casual monocular videos. In one embodiment three main processes are used. First, a Structure from Motion algorithm such as that described Snavely et al. in the article entitled “Photo tourism: Exploring photo collections in 3-D” (ACM Transactions on Graphics, Vol. 25, pp. 835-846, 2006) is employed to estimate the camera parameters for each frame and the sparse point clouds of the scene. Next, an efficient dense disparity/depth map recovery approach is implemented which leverages aspects of the fast mean-shift belief propagation proposed by Park et al., in the article “Data-driven mean-shift belief propagation for non-Gaussian MRFs” (Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 3547-3554, 2010). Finally, new virtual views synthesis is used to form left-eye/right-eye video frame sequences. Since previous works require either accurate 3-D geometry information to perform image-based rendering, or simply interpolate or copy from neighborhood pixels, satisfactory new view images have been difficult to generate. The present method uses a color consistency prior based on the assumption that 3-D points in the scene will show up in several consecutive video frames with similar color texture. Additionally, another prior is used based on the assumption that the synthesized images should be as smooth as a natural image. These priors can be used to eliminate ambiguous geometry information, and improve the quality of synthesized image. A Bayesian-based view synthesis algorithm is described that incorporates estimated camera parameters and dense depth maps of several consecutive frames to synthesize a nearby virtual view image. 
     Aspects of the present invention will now be described with reference to  FIG. 2  which shows a flow chart illustrating a method for determining range maps  250  for video frames  205  (F 1 -F N ) of a digital video  200 . The range maps  250  are useful for a variety of different applications including performing various image analysis and image understanding processes, forming warped video frames corresponding to different viewpoints, forming stabilized digital videos and forming stereoscopic videos from monoscopic videos. Table 1 defines notation that will be used in the description of the present invention. 
     
       
         
           
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Notation 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                 F i   
                 Input video frame sequence, i = 1 to N 
               
               
                 C i   
                 Estimated camera parameters for F i  (includes both intrinsic 
               
               
                   
                 and extrinsic camera parameters) 
               
               
                 R i   
                 Range map for F i   
               
               
                 V T   
                 target viewpoint 
               
               
                 SF v   
                 Synthesized frame for target viewpoint V T   
               
               
                 (x; y) 
                 Subscript, which indicates the pixel location in an image or a 
               
               
                   
                 depth map (e.g., F i,(x,y)  refers the pixel at coordinate (x, y) 
               
               
                   
                 in frame F i , and R i,(x,y)  is the corresponding 
               
               
                   
                 depth value) 
               
               
                 fC(W, F) 
                 shows the pixel correspondences from a warped frame W to 
               
               
                   
                 the original frame F. (e.g., fC(SF v , F i ) shows the 
               
               
                   
                 correspondence map from SF v  to F i , and fC(SF v,(x,y) , F i ) 
               
               
                   
                 indicates the corresponding pixel in F i  for SF v,(x,y) ) 
               
               
                   
               
            
           
         
       
     
     A determine disparity maps step  210  is used to determine a disparity map series  215  including a disparity map  220  (D 1 -D N ) corresponding to each of the video frames  205 . Each disparity map  220  is a 2-D array of disparity values that provide an indication of the disparity between the pixels in the corresponding video frame  205  and a second video frame selected from the digital video  200 . In a preferred embodiment, the second video frame is selected from a set of candidate frames according to a set of criteria that includes an image similarity criterion and a position difference criterion. The disparity map series  215  can be determined using any method known in the art. A preferred embodiment of the determine disparity maps step  210  will be described later with respect to  FIG. 3 . 
     The disparity maps  220  will commonly contain various artifacts due to inaccuracies introduced by the determine disparity maps step  210 . A refine disparity maps step  225  is used to determine a refined disparity map series  230  that includes refined disparity maps  235  (D′ 1 -D′ N ). In a preferred embodiment, the refine disparity maps step  225  applies two processing stages. A first processing stage using an image segmentation algorithm to provide spatially smooth the disparity values, and a second processing stage applies a temporal smoothing operation. 
     For the first processing stage of the refine disparity maps step  225 , an image segmentation algorithm is used to identify contiguous image regions (i.e., clusters) having image pixels with similar color and disparity. The disparity values are then smoothed within each of the clusters. In a preferred embodiment, the disparities are smoothed by determining a mean disparity value for each of the clusters, and then updating the disparity value assigned to each of the pixels in the cluster to be equal to the mean disparity value. In one embodiment, the clusters are determined using the method described with respect to  FIG. 3  in commonly-assigned U.S. Patent Application Publication 2011/0026764 to Wang, entitled “Detection of objects using range information,” which is incorporated herein by reference. 
     For the second processing stage of the refine disparity maps step  225 , the disparity values are temporally smoothed across a set of video frames  205  surrounding the particular video frame F i . Using approximately 3 to 5 video frames  205  before and after the particular video frame F i  have been found to produce good results. For each video frame  205 , motion vectors are determined that relate the pixel positions in that video frame  205  to the corresponding pixel position in the particular video frame F i . For each of the clusters of image pixels determined in the first processing stage, corresponding cluster positions in the other video frames  205  are determined using the motion vectors. The average of the disparity values determined for the corresponding clusters in the set of video frames are then averaged to determine the refined disparity values for the refined disparity map  235 . 
     Finally, a determine range maps step  240  is used to determine a range map series  245  that includes a range map  250  (R 1 -R N ) that corresponds to each of the video frames  205 . The range maps  250  are a 2-D array of range values representing a “range” (e.g., a “depth” from the camera to the scene) for each pixel in the corresponding video frames  205 . The range values can be calculated by triangulation from the disparity values in the corresponding disparity map  220  given a knowledge of the camera positions (including a 3-D location and a pointing direction determined from the extrinsic parameters) and the image magnification (determined from the intrinsic parameters) for the two video frames  205  that were used to determine the disparity maps  220 . Methods for determining the range values by triangulation are well-known in the art. 
     The camera positions used to determine the range values can be determined in a variety of ways. As will be discussed in more detail later with respect to  FIG. 3 , methods for determining the camera positions include the use of position sensors in the digital camera, and the automatic analysis of the video frames  205  to estimate the camera positions based on the motion of image content within the video frames  205 . 
       FIG. 3  shows a flowchart showing additional details of the determine disparity maps step  210  according to a preferred embodiment. The input digital video  200  includes a temporal sequence of video frames  205 . In the illustrated example, a disparity map  220  (D i ) is determined corresponding to a particular input video frame  205  (F i ). This process can be repeated for each of the video frames  205  to determine each of the disparity maps  220  the disparity map series  215 . 
     A select video frame step  305  is used to select a particular video frame  310  (in this example the i th  video frame F i ). A define candidate video frames step  335  is used to define a set of candidate video frames  340  from which a second video frame will be selected that is appropriate for forming a stereo image pair. The candidate video frames  340  will generally include a set of frames that occur near to the particular video frame  310  in the sequence of video frames  205 . For example, the candidate video frames  340  can include all of the neighboring video frames that occur within a predefined interval of the particular video frame (e.g., +/−10 to 20 frames). In some embodiments, only a subset of the neighboring video frames are included in the set of candidate video frames  340  (e.g., every second frame or every tenth frame). This can enable including candidate video frames  340  that span a larger time interval of the digital video  200  without requiring the analysis of an excessive number of candidate video frames  340 . 
     A determine intrinsic parameters step  325  is used to determine intrinsic parameters  330  for each video frame  205 . The intrinsic parameters are related to a magnification of the video frames. In some embodiments, the intrinsic parameters are determined responsive to metadata indicating the optical configuration of the digital camera during the image capture process. For example, in some embodiments, the digital camera has a zoom lens and the intrinsic parameters include a lens focal length setting that is recorded during the capturing the of digital video  200 . Some digital cameras also include a “digital zoom” capability whereby the captured images are cropped to provide further magnification. This effectively extends the “focal length” range of the digital camera. There are various ways that intrinsic parameters can be defined to represent the magnification. For example, the focal length can be recorded directly. Alternately, a magnification factor relative to reference focal length, or an angular extent can be recorded. In other embodiments, the intrinsic parameters  330  can be determined by analyzing the digital video  200 . For example, as will be discussed in more detail later, the intrinsic parameters  330  can be determined using a “structure from motion” (SFM) algorithm. 
     A determine extrinsic parameters step  315  is used to analyze the digital video  200  to determine a set of extrinsic parameters  320  corresponding to each video frame  205 . The extrinsic parameters provide an indication of the camera position of the digital camera that was used to capture the digital video  200 . The camera position includes both a 3-D camera location and a pointing direction (i.e., an orientation) of the digital camera. In a preferred embodiment, the extrinsic parameters  320  include a translation vector (T i ) which specifies the 3-D camera location relative to a reference location and a rotation matrix (M i ) which relates to the pointing direction of the digital camera. 
     The determine extrinsic parameters step  315  can be performed using any method known in the art. In some embodiments, the digital camera used to capture the digital video  200  may include position sensors (location sensors and orientation sensors) that directly sense the position of the digital camera (either as an absolute camera position or a relative camera position) at the time that the digital video  200  was captured. The sensed camera position information can then be stored as metadata associated with the video frames  205  in the file used to store the digital video  200 . Types of position sensors used in digital cameras commonly include gyroscopes, accelerometers and global positioning system (GPS) sensors. 
     In other embodiments, the camera positions can be estimated by analyzing the digital video  200 . In a preferred embodiment, the camera positions can be determined using a so called “structure from motion” (SFM) algorithm (or some other type of “camera calibration” algorithm). SFM algorithms are used in the art to extract 3-D geometry information from a set of 2-D images of an object or a scene. The 2-D images can be consecutive frames taken from a video, or pictures taken with an ordinary camera from different directions. In accordance with the present invention, an SFM algorithm can be used to recover the camera intrinsic parameters  330  and extrinsic parameters  320  for each video frame  205 . Such algorithms can also be used to reconstruct 3-D sparse point clouds. The most common SFM algorithms involve key-point detection and matching, forming consistent matching tracks and solving camera parameters. 
     An example of an SFM algorithm that can be used to determine the intrinsic parameters  330  and the extrinsic parameters  320  in accordance with the present invention is described in the aforementioned article by Snavely et al. entitled “Photo tourism: Exploring photo collections in 3-D.” In a preferred embodiment, two modifications to the basic algorithms are made. 1) Since the input are an ordered set of 2-D video frames  205 , key-points from only certain neighborhood frames are matched to save computational cost. 2) To guarantee enough baselines and reduce the numerical errors in solving camera parameters, some key-frames are eliminated according to an elimination criterion. The elimination criterion is to guarantee large baselines and a large number of matching points between two consecutive key frames. The camera parameters for these key-frames are used as initial values for a second run using the entire sequence of video frames  205 . 
     A determine similarity scores step  345  is used to determine image similarity scores  350  providing an indication of the similarity between the particular video frame  310  and each of the candidate video frames. In some embodiments, larger image similarity scores  350  correspond to a higher degree of image similarity. In other embodiments, the image similarity scores  350  are representations of image differences. In such cases, smaller image similarity scores  350  correspond to smaller image differences, and therefore to a higher degree of image similarity. 
     Any method for determining image similarity scores  350  known in the art can be used in accordance with the present invention. In a preferred embodiment, the image similarity score  350  for a pair of video frames is computed by determining SIFT features for the two video frames, and determining the number of matching SIFT features that are common to the two video frames. Matching SIFT features are defined to be those that are similar to within a predefined difference. In some embodiments, the image similarity score  350  is simply set to be equal to the number of matching SIFT features. In other embodiments, the image similarity score  350  can be determined using a function that is responsive to the number of matching SIFT features. The determination of SIFT features are well-known in the image processing art. In a preferred embodiment, the SIFT features are determined and matched using methods described by Lowe in the article entitled “Object recognition from local scale-invariant features” (Proc. International Conference on Computer Vision, Vol. 2, pp. 1150-1157, 1999), which is incorporated herein by reference. 
     A select subset step  355  is used to determine a subset of the candidate video frames  340  that have a high degree of similarity to the particular video frame, thereby providing a video frames subset  360 . In a preferred embodiment, the image similarity scores  350  are compared to a predefined threshold (e.g.,  200 ) to select the video frame subset. In cases where larger image similarity scores  350  correspond to a higher degree of image similarity, those candidate video frames  340  having image similarity scores  350  that exceed the predefined threshold are included in the video frames subset  360 . In cases where smaller image similarity scores  350  correspond to a higher degree of image similarity, those candidate video frames  340  having image similarity scores that are less than the predefined threshold are included in the video frames subset  360 . In some embodiments, the threshold is determined adaptively based on the distribution of image similarity scores. For example, the threshold can be set so that a predefined number of candidate video frames  340  having the highest degree of image similarity with the particular video frame  310  are included in the video frames subset  360 . 
     Next, a determine position difference scores step  365  is used to determine position difference scores  370  relating to differences between the positions of the digital video camera for the video frames in the video frames subset  360  and the particular video frame  310 . In a preferred embodiment, the position difference scores are determined responsive to the extrinsic parameters  320  associated with the corresponding video frames. 
     The position difference scores  370  can be determined using any method known in the art. In a preferred embodiment, the position difference scores include a location term as well as an angular term. The location term is proportional to a Euclidean distance between the camera locations for the two video frames (D L =((x 2 −x 1 ) 2 +(y 2 −y 1 ) 2 +(z 2 −z 1 ) 2 ) 0.5 , where (x 1 , y 1 , z 1 ) and (x 2 , y 2 , z 2 ) are the camera locations for the two frames). The angular term is proportional to the angular change in the camera pointing direction for the two video frames (D A =arccos(P 1 ·P 2 /|P 1 ·P 2 |, where P 1  and P 2  are pointing direction vectors for the two video frames). The location term and the angular term can then be combined using a weighted average to determine the position difference scores  370 . In other embodiments, the “3D quality criterion” described by Gaël in the technical report entitled “Depth maps estimation and use for 3DTV” (Technical Report 0379, INRIA Rennes Bretagne Atlantique, 2010) can be used as the position difference scores  370 . 
     A select video frame step  375  is used to select a selected video frame  38  from the video frames subset  360  responsive to the position difference scores  370 . It is generally easier to determine disparity values from image pairs having larger camera location differences. In a preferred embodiment, the select video frame step  375  selects the video frame in the video frames subset  360  having the largest position difference. This provides the selected video frame  380  having the largest degree of disparity relative to the particular video frame  310 . 
     A determine disparity map step  385  is used to determine the disparity map  220  (D i ) having disparity values for an array of pixel locations by automatically analyzing the particular video frame  310  and the selected video frame  380 . The disparity values represent a displacement between the image pixels in the particular video frame  310  and corresponding image pixels in the selected video frame  380 . 
     The determine disparity map step  385  can use any method known in the art for determining a disparity map  220  from a stereo image pair can be used in accordance with the present invention. In a preferred embodiment, the disparity map  220  is determined by using an “optical flow algorithm” to determine corresponding points in the stereo image pair. Optical flow algorithms are well-known in the art. In some embodiments, the optical flow estimation algorithm described by Fleet et al. in the book chapter “Optical Flow Estimation” (chapter 15 in Handbook of Mathematical Models in Computer Vision, Eds., Paragios et al., Springer, 2006) can be used to determine the corresponding points. The disparity values to populate the disparity map  220  are then given by the Euclidean distance between the pixel locations for the corresponding points in the stereo image pair. An interpolation operation can be used to fill any holes in the resulting disparity map  220  (e.g., corresponding to occlusions in the stereo image pair). In some embodiments, a smoothing operation can be used to reduce noise in the estimated disparity values. 
     While the method for determining the disparity map  220  in the method of  FIG. 3  was described with reference to a set of video frames  205  for a digital video  200 , one skilled in the art will recognize that it can also be applied to determining a range map for a digital still image of a scene. In this case, the digital still image is used for the particular video frame  310 , and a set of complementary digital still images of the same scene captured from different viewpoints are used for the candidate video frames  340 . The complementary digital still images can be images captured by the same digital camera (where it is repositioned to change the viewpoint), or can even be captured by different digital cameras. 
       FIG. 4  shows a flowchart of a method for determining a stabilized video  440  from an input digital video  200  that includes a sequence of video frames  205  and corresponding range maps  250 . In a preferred embodiment, the range maps  250  are determined using the method that was described above with respect to  FIGS. 2 and 3 . A determine input camera positions step  405  is used to determine input camera positions  410  for each video frame  205  in the digital video  200 . In a preferred embodiment, the input camera positions  410  include both 3-D locations and pointing directions of the digital camera. As was discussed earlier with respect to the determine extrinsic parameters step  315  in  FIG. 3 , there are a variety of ways that camera positions can be determined. Such methods include directly measuring the camera positions using position sensors in the digital camera, and using an automatic algorithm (e.g., a structure from motion algorithm) to estimate the camera positions by analyzing the video frames  205 . 
     A determine input camera path step  415  is used to determine an input camera path  420  for the digital video  200 . In a preferred embodiment, the input camera path  420  is a look-up table (LUT) specifying the input camera positions  410  as a function of a video frame index.  FIG. 5  shows an example of an input camera path graph  480  showing a plot showing two dimensions of the input camera path  420  (i.e., the x-coordinate and the y-coordinate of the 3-D camera location). Similar plots could be made for the other dimension of the 3-D camera location, as well as the dimensions of the camera pointing direction. 
     Returning to a discussion of  FIG. 4 , a determine smoothed camera path step  425  is used to determine a smoothed camera path  430  by applying a smoothing operation to the input camera path  420 . Any type of smoothing operation known in the art can be used to determine the smoothed camera path  430 . In a preferred embodiment, the smoothed camera path  430  is determined by fitting a smoothing spline (e.g., a cubic spline having a set of knot points) to the input camera path  420 . Smoothing splines are well-known in the art. The smoothness of the smoothed camera path  430  can typically be controlled by adjusting the number of knot points in the smoothing spline. In other embodiments, the smoothed camera path  430  can be determined by convolving the LUT for each dimension of the input camera path  420  with a smoothing filter (e.g., a low-pass filter).  FIG. 5  shows an example of a smoothed camera path graph  485  that was formed by applying a smoothing spline to the input camera path  420  corresponding to the input camera path graph  480 . 
     In some embodiments random variations can be added to the smoothed camera path  430  so that the stabilized video  440  retains a “hand-held” look. The characteristics (amplitude and temporal frequency content) of the random variations are preferably selected to be typical of high-quality consumer videos. 
     In some embodiments, a user interface can be provided to enable a user to adjust the smoothed camera path  430 . For example, the user can be enabled to specify modifications to the camera location, the camera pointing direction and the magnification as a function of time. 
     A determine smoothed camera positions step  432  is used to determine smoothed camera positions  434 . The smoothed camera positions  434  will be used to synthesize a series of stabilized video frames  445  for a stabilized video  440 . In a preferred embodiment, the smoothed camera positions  434  are determined by uniformly sampling the smoothed camera path  430 . For the case where the smoothed camera path  430  is represented using a smoothed camera position LUT, the individual LUT entries can each be taken to be smoothed camera positions  434  for corresponding stabilized video frames  445 . For the case where the smoothed camera path  430  is represented using a spline representation, the spline function can be sampled to determine the smoothed camera positions  434  for each of the stabilized video frames  445 . 
     A determine stabilized video step  435  is used to determine a sequence of stabilized video frames  445  for the stabilized video  440 . The stabilized video frames  445  are determined by modifying the video frames  205  in the input digital video  200  to synthesize new views of the scene having viewpoints corresponding to the smoothed camera positions  434 . In a preferred embodiment, each stabilized video frame  445  is determined by modifying the video frame  205  having the input camera position that is nearest to the desired smoothed camera position  434 . 
     Any method for modifying the viewpoint of a digital image known in the art can be used in accordance with the present invention. In a preferred embodiment, the determine stabilized video step  435  synthesizes the stabilized video frames  445  using the method that is described below with respect to  FIG. 6 . 
     In some embodiments, an input magnification value for each of the input video frames  205  in addition to the input camera positions  410 . The input magnification values are related to the zoom setting of the digital video camera. Smoothed magnification values can then be determined for each stabilized video frame  445 . The smoothed magnification values provide smoother transitions in the image magnification. The magnification of each stabilized video frame  445  is then adjusted according to the corresponding smoothed magnification value. 
     In some applications, it is desirable to form a stereoscopic video from a monocular input video. The above-described method can easily be extended to produce a stabilized stereoscopic video  475  using a series of optional steps (shown with dashed outline). The stabilized stereoscopic video  475  includes two complete videos, one corresponding to each eye of an observer. The stabilized video  440  is displayed to one eye of the observer, while a second-eye stabilized video  465  is displayed to the second eye of the observer. Any method for displaying stereoscopic videos known in the art can be used to display the stabilized stereoscopic video  475 . For example, the two videos can be projected onto a screen using light having orthogonal polarizations. The observer can then view the screen using glasses having corresponding polarizing filters for each eye. 
     To determine the second-eye stabilized video  465 , a determine second-eye smoothed camera positions  450  is used to determine second-eye smoothed camera positions  455 . In a preferred embodiment, the second-eye smoothed camera positions  455  have the same pointing directions as the corresponding smoothed camera positions  434 , and the camera location is shifted laterally relative to the pointing direction by a predefined spatial increment. To form a stabilized stereoscopic video  475  having realistic depth, the predefined spatial increment should correspond to the distance between the left and right eyes of a typical observer (i.e., about 6-7 cm). The amount of depth perception can be increased or decreased by adjusting the size of the spatial increment accordingly. 
     A determine second-eye stabilized video step  460  is used to form the stabilized video frames  470  by modifying the video frames  205  in the input digital video  200  to synthesize new views of the scene having viewpoints corresponding to the second-eye smoothed camera positions  455 . This step uses an identical process to that used by the determine stabilized video step  435 . 
       FIG. 6  shows a flow chart of a method for modifying the viewpoint of a main image  500  of a scene captured from a first viewpoint (V i ). The method makes use of a set of complementary images  505  of the scene including one or more complementary images  510  captured from viewpoints that are different from the first viewpoint. This method can be used to perform the determine stabilized video step  435  and the determine second-eye stabilized video step  460  discussed earlier with respect to  FIG. 4 . 
     In the illustrated embodiment, the main image  500  corresponds to a particular image frame (F i ) from a digital video  200  that includes a time sequence of video frames  205  (F 1 -F N ). Each video frame  205  is captured from a corresponding viewpoint  515  (V 1 -V N ) and has an associated range map  250  (R 1 -R N ). The range maps  250  can be determined using any method known in the art. In a preferred embodiment, the range maps  250  are determined using the method described earlier with respect to  FIGS. 2 and 3 . 
     The set of complementary images  505  includes one or more complementary image  510  corresponding to image frames that are close to the main image  500  in the sequence of video frames  205 . In one embodiment, the complementary images  510  include one or both of the image frames that immediately precede and follow the main image  500 . In other embodiments, the complementary images can be the image frames occurring a fixed number frames away from the main image  500  (e.g., 5 frames). In other embodiments, the complementary images  510  can include more than two image frames (e.g., video frames F i−10 , F i−5 , F i+5  and F i+10 ). In some embodiments, the image frames that are selected to be complementary images  510  are determined based on their viewpoints  515  to ensure that they have a sufficiently different viewpoints from the main image  500 . 
     A target viewpoint  520  (V T ) is specified, which is to be used to determine a synthesized output image  550  of the scene. A determine warped main image step  525  is used to determine a warped main image  530  from the main image  500 . The warped main image  530  corresponds to an estimate of the image of the scene that would have been captured from the target viewpoint  520 . In a preferred embodiment the determine warped main image step  525  uses a pixel-level depth-based projection algorithm; such algorithms are well-known in the art and generally involve using a range map that provides depth information. Frequently, the warped main image  530  will include one or more “holes” corresponding to scene content that was occluded in the main image  500 , but would be visible from the target viewpoint. 
     The determine warped main image step  525  can use any method for warping an input image to simulate a new viewpoint that is known in the art. In a preferred embodiment, the determine warped main image step  525  uses a Bayesian-based view synthesis approach as will be described below. 
     Similarly, a determine warped complementary images step  535  is used to determine a set of warped complementary images  540  corresponding again to the target viewpoint  520 . In a preferred embodiment, the warped complementary images  540  are determined using the same method that was used by the determine warped main image step  525 . The warped complementary images  540  will be have the same viewpoint as the warped main image  530 , and will be spatially aligned with the warped main image  530 . If the complementary images  510  have been chosen appropriately, one or more of the warped complementary images  540  will contain image content in the image regions corresponding to the holes in the warped main image  530 . A determine output image step  545  is used to determine an output image  550  by combining the warped main image  530  and the warped complementary images  540 . In a preferred embodiment, the determine output image step  545  determines pixel values for each of the image pixels in the one or more holes in the warped main image  530  using pixel values at corresponding pixel locations in the warped complementary images  540 . 
     In some embodiments, the pixel values of the output image  550  are simply copied from the corresponding pixels in the warped main image  530 . Any holes in the warped main image  530  can be filled by copying pixel values from corresponding pixels in one of the warped complementary images  540 . In other embodiments, the pixel values of the output image  550  are determined by forming a weighted combination of corresponding pixels in the warped main image  530  and the warped complementary images  540 . For cases where the warped main image  530  or one or more of the warped complementary images  540  have holes, only pixels values from pixels that are not in (or near) holes should preferably be included in the weighted combination. In some embodiments, only output pixels that are in (or near) holes in the warped main image  530  are determined using the weighted combination. As will be described later, in a preferred embodiment, pixel values for the output image  550  are determined using the Bayesian-based view synthesis approach. 
     While the method for warping the main image  500  to determine the output image  550  with a modified viewpoint was described with reference to a set of video frames  205  for a digital video  200 , one skilled in the art will recognize that it can also be applied to adjust the viewpoint of a main image that is a digital still image captured with a digital still camera. In this case, the complementary images  510  are images of the same scene captured from different viewpoints. The complementary images  510  can be images captured by the same digital still camera (where it is repositioned to change the viewpoint), or can even be captured by different digital still cameras. 
     A Bayesian-based view synthesis approach that can be used to simultaneously perform the determine warped main image step  525 , the determine warped complementary images step  535 , and the determine output image step  545  according to a preferred embodiment will now be described. Given a sequence of video frames  205  F i  (i=1−N), together with corresponding range information R i  and camera parameters C i  that specify the camera viewpoints V i , the goal is to synthesize the output image  550  (SF v ) at the specified target viewpoint  520  (V T ). The camera parameters for frame i can be denoted as C i ={K i , M i , T i }, where K i  is a matrix including intrinsic camera parameters (e.g., parameters related to the lens magnification), and M i  and T i  are extrinsic camera parameters specifying a camera position. In particular, M i  is a rotation matrix and T i  is a translation vector, which specify a change in camera pointing direction and camera location, respectively, relative to a reference camera position. Taken together, M i  and T i  define the viewpoint V i  for the video frame F i . The range map R i  provides information about a third dimension for video frame F i , indicating the “z” coordinate (i.e., “range” or “depth”) for each (x,y) pixel location and thereby providing 3-D coordinates relative to the camera coordinate system. 
     It can be shown that the pixels in one image frame (with known camera parameters and range map) can be mapped to corresponding pixel positions in another virtual view using the following geometric relationship:
 
 p   v   =R   i ( p   i ) K   v   M   v   T   M   i   K   i   −1   p   i   +K   v   M   v   T ( T   i   −T   v )  (1)
 
where K i , M i  and T i  are the intrinsic camera parameters, rotation matrix, and translation vector, respectively, specifying the camera position for an input image frame F i , K v , M v  and T v  are the intrinsic camera parameters, rotation matrix, and translation vector, respectively, specifying a camera position for a new virtual view, p i  is the 2-D point in the input image frame, R i (p i ) is the range value for the 2-D point p i , and p v  is the corresponding 2-D point in an image plane with the specified new virtual view. The superscript “T” indicates a matrix transpose operation, and the superscript “−1” indicates a matrix inversion operation.
 
     A pixel correspondence function fC i =fC(W i , F i ) can be defined using the transformation given Eq. (1) to relate the 2-D pixel coordinates in the i th  video frame F i  to the corresponding 2-D pixel coordinates in the corresponding warped image W i  with the target viewpoint  520 . 
     The goal is to synthesis the most likely rendered virtual view SF v  to be used for output image  550 . We formulate the problem as a probability problem in Bayesian framework, and wish to generate the virtual view SF v  which can maximize the joint probability:
 
 p ( SF   v   |V   T   ,{F   i   },{C   i   },{R   i }), i ∈φ)  (2)
 
where F i  is the i th  video frame of the digital video  200 , C i  and R i  are corresponding camera parameters and range maps, respectively, V T  is the target viewpoint  520 , and φ is the set of image frame indices that include the main image  500  and the complementary images  510 .
 
     To decompose the joint probability function in Eq. (2), the statistical dependencies between variable can be explored. The virtual view SF v  will be a function of the video frames {F i } and the correspondence maps {fC i }. Furthermore, as described above, the correspondence maps {fC i } can be constructed with 3-D geometry information, which includes the camera parameters (C i ) and range map (R i ) for each video frame (F i ), and the camera parameters corresponding to the target viewpoint  520  (V T ). Given these dependencies, Eq. (2) can be rewritten as:
 
 p ( SF   v   |{F   i   },{fC   i }) p ({ fC   i   }|V   T   ,{C   i   },{R   i })  (3)
 
Considering the independence of original frames, Bayes&#39; rule allows us to write this as:
 
                         ∏     i   =   1     N     ⁢           ⁢       p   ⁡     (         F   i     |     SF   v       ,     f   ⁢           ⁢     C   i         )       ·     p   ⁡     (     SF   v     )               ∏     i   =   1     N     ⁢           ⁢     p   ⁡     (     F   i     )           ⁢       ∏     i   =   1     N     ⁢           ⁢     p   ⁡     (         f   ⁢           ⁢     C   i       |     V   T       ,     C   i     ,     R   i       )                 (   4   )               
This formulation consists of four parts:
 
     1) p(F i |SF v ,fC i ) can be viewed as a “color-consistency prior,” and should reflect the fact that corresponding pixels in video frame F i  and virtual view SF v  are more likely to have similar color texture. In a preferred embodiment, this prior is defined as:
 
 p ( F   i,fC     i,(x,y)     |SF   v,(x,y)   ,fC   i,(x,y) )=exp(−β i ·ρ( F   i,fC     i,(x,y)     −SF   v,(x,y) ))  (5)
 
where SF v,(x,y)  is the pixel value at the (x,y) position of the virtual view SF v , F i ,fC i(x,y)  is the pixel value in the video frame F i  corresponding to a pixel position determined by applying the correspondence map fC i  to the (x,y) pixel position, β i  is value used to scale the color distance between F i  and SF v . In a preferred embodiment, β i  is a function of the camera position distance and is given by β i =e −kD , where k is a constant and D is the distance between the camera position for F i  and the camera position for the virtual view SF v . The function ρ(•) is a robust kernel, and in this example is the absolute distance ρ(●)=|●|. Note that the quantity F i,fC     i,(x,y)    corresponds to the warped main image  530  and the warped complementary images  540  shown in  FIG. 6 . When a particular pixel position corresponds to a hole in one of the warped images, no valid pixel position can be determined by applying the correspondence map fC i  to the (x,y) pixel position. In such cases, these pixels are not included in the calculations.
 
     2) p(SF v ) is a smoothness prior based on the synthesized virtual view SF v , and reflects the fact that the synthesized image should generally be smooth (slowly varying). In a preferred embodiment, it is defined as: 
                     p   ⁡     (     SF   v     )       =       ∏     (     x   ,   y     )       ⁢           ⁢     exp   ⁡     (       -   λ     ⁢            SF     v   ,     (     x   ,   y     )         -     AvgN   ⁡     (     SF     v   ,     (     x   ,   y     )         )                )                 (   6   )               
where AvgN(•) means the average value of all neighboring pixels in the 1-nearest neighborhood, and λ is a constant.
 
     3) p(fC i |V T ,C i ,R i ) is a correspondence confidence prior that relates to the confidence for the computed correspondences. The confidence for the computed correspondence will generally be lower when the pixel is in or near a hole in the warped image. The color-consistency prior can provide an indication of whether a pixel location is in a hole because the color in the warped image will have a large difference relative to the color of the virtual view SF v . In a preferred embodiment, we consider a neighborhood around a pixel location of the computed correspondence including the 1-nearest neighbors. The 1-nearest neighbors form a 3×3 square centering at the computed correspondence. We number the pixel locations in this square by j (j=1-9) in order of rows, so that the computed correspondence pixel corresponds to j=5. Theoretically different cases with all possible j should sum up for the objective function, however, we can approximate it by only considering the j which maximize the joint probability with color consistency prior. In one embodiment, the prior can be determined as:
 
 p ( fC   i   |V   T   ,C   i   ,R   i )= e   −α     j   | j     max     (7)
 
where:
 
                     ⅇ     -     α   j         =     {               ⅇ     -     θ   1         ,             when   ⁢           ⁢   j     =   5                 ⅇ     -     θ   2         ,         otherwise         .               (   8   )               
and j max  is the j value that maximizes the quantity e −α     j   p(F i |SF v ,fC i,j ), fC i,j  being the correspondence map for the j th  pixel in the neighborhood. It can be assumed that the computed correspondences have higher possibility to be true correspondence than its neighborhoods, so normally we choose θ 1 &lt;θ 2 . In a preferred embodiment, θ 1 =10 and θ 2 =40.
 
     4) p(F i ) is the prior on the input video frames  205 . We have no particular prior knowledge regarding the input digital video  200 , so we can assume that this probability is 1.0 and ignore this term. 
     Finally, the objective function can be written as: 
                       ∏     i   =   1     N     ⁢           ⁢       p   ⁡     (         F   i     |     SF   v       ,     f   ⁢           ⁢     C   i         )       ·     p   ⁡     (         f   ⁢           ⁢     C   i       |     V   T       ,     C   i     ,     R   i       )       ·     p   ⁡     (     SF   v     )           ≈       ∏     i   =   1     N     ⁢           ⁢       max   j     ⁢       [       exp   ⁡     (       -     β   i       ·     ρ   ⁡     (       F     i   ,     f   ⁢           ⁢     C     i   ,   j   ,     (     x   ,   y     )               -     SF     v   ,     (     x   ,   y     )           )         )       ·     ⅇ     -     α   j           ]     ·       ∏     (     x   ,   y     )       ⁢           ⁢     exp   ⁡     (       -   λ     ⁢            SF     v   ,     (     x   ,   y     )         -     AvgN   ⁡     (     SF     v   ,     (     x   ,   y     )         )                )                       (   9   )               
In the implementation, we minimize the negative log of the objective probability function, and get the following objective function:
 
                       ∑     i   =   1     N     ⁢       ∑     (     x   ,   y     )       ⁢       β   i     ⁢       min   j     ⁢     [       p   ⁡     (       F     i   ,     f   ⁢           ⁢     C     i   ,   j   ,     (     x   ,   y     )               -     SF     v   ,     (     x   ,   y     )           )       +     α   j       ]             +     λ   ⁢       ∑     (     x   ,   y     )       ⁢            SF     v   ,     (     x   ,   y     )         -     AvgN   ⁡     (     SF     v   ,     (     x   ,   y     )         )                          (   10   )               
where the constant λ can be used to determine the degree of smooth constrain that is imposed on the synthesized image.
 
     Optimization of this objective function could be directly attempted using global optimization strategies (e.g., simulated annealing). However, attaining a global optimum using such methods is time consuming, which is not desirable for synthesizing many frames for a video. Since the possibilities for each correspondence are only a few, a more efficient optimization strategy can be used. In a preferred embodiment, the objective function is optimized using a method similar to that described by Fitzgibbon et al. in the article entitled “Image-based rendering using image-based priors” (International Journal of Computer Vision, Vol. 63, pp. 141-151, 2005), which is incorporated herein by reference. With this approach, a variant of an iterated conditional modes (ICM) algorithm is used to get an approximate solution. In a preferred embodiment, the ICM algorithm uses an iterative optimization process that involves alternately optimizing the first term (a color-consistency term “V”) and the second term (a virtual view term “T”) in Eq. (10). For the initial estimation of the first term, V 0 , the most likely correspondences (j=5) is chosen for each pixel, and the synthesized results are obtained by a weighted average of correspondences from all frames (i=1−N). The initial solution for the second term, T 0 , can be obtained by using a well-known mean filter. Alternately, a median filter can be used here instead to avoid outliers and blurring sharp boundaries. The input V i   k+1  for next iteration can be set as the linear combination of the output of the previous iteration (V k  and T k ): 
                     V   i     k   +   1       =         V   k     +     λ   ⁢           ⁢     T   k           1   +   λ               (   10   )               
where k is the iteration number. Finally, after a few iterations (5 to 10 has been found to work well in most cases), the differences of outputs between iterations will converge, and thus synthesize image for the expected new virtual view. In some embodiments, a predefined number of iterations can be performed. In other embodiments a convergence criterion can be defined to determine when the iterative optimization process has converged to an acceptable accuracy.
 
     The optimization of the objective function has the effect of automatically filling the holes in the warped main image  530 . The combination of the correspondence confidence prior and the color-consistency prior has the effect of selecting the pixel values from the warped complementary images  540  that do not have holes to be the most likely pixel values to fill the holes in the warped main image  530 . 
     To evaluate the performance of the above-described methods, experiments were conducted using several challenging video sequences. Two video sequences were from publicly available data sets (in particular, the “road” and “lawn” video sequences described by Zhang et al. in the aforementioned article “Consistent depth maps recovery from a video sequence”), another two were captured using a casual video camera (“pavilion” and “stele”) and one was a clip from the movie “Pride and Prejudice” (called “pride” for short). 
     The view synthesis method described with reference to  FIG. 6  was compared to two state-of-the-art methods: an interpolation-based method described by Zhang et al. in the aforementioned article entitled “3D-TV content creation: automatic 2-D-to-3-D video conversion” that employs cubic-interpolation to fill the holes generated by parallax, and a blending method described by Zitnick et al. in the aforementioned article “Stereo for image-based rendering using image over-segmentation” that involves blending virtual views generated by the two closest camera frames to synthesize a final virtual view. 
     Since ground truth for virtual views is impossible to obtain for an arbitrary viewpoint, an existing frame from the original video sequence can be selected to use as a reference. A new virtual view with the same viewpoint can then be synthesized from a different main image and compared to the reference to evaluate the algorithm performance. For each video, 10 reference frames were randomly selected to be synthesized by all three methods. The results were quantitatively evaluated by determining peak signal-to-noise ratio (PSNR) scores representing the difference between the synthesized frame and the ground truth reference frame. 
       FIG. 7  is a graph comparing the calculated PSNR scores for the method of  FIG. 6  to those for the aforementioned prior art methods. Results are shown for each of the 5 sample videos that were described above. The data symbol shown on each line shows the average PSNR, and the vertical extent of the lines shows the range of the PSNR values across the 10 frames that were tested. It can be seen that the method of the present invention achieves substantially higher PSNR scores with comparable variance. This implies that the method of the present invention can robustly synthesize virtual views with better quality. 
     The method for forming an output image  550  with a target viewpoint  520  described with reference to  FIG. 6  can be adapted to a variety of different applications besides the illustrated example of forming of a frame for a stabilized video. One such example relates to the Kinect game console available for the Xbox 360 gaming system from Microsoft Corporation of Redmond, Wash. Users are able to interact with the gaming system without any hardware user interface controls through the use of a digital imaging system that captures real time images of the users. The users interact with the system using gestures and movements which are sensed by the digital imaging system and interpreted to control the gaming system. The digital imaging system includes an RGB digital camera for capturing a stream of digital images and a range camera (i.e., a “depth sensor”) that captures a corresponding stream of range images that are used to supply depth information for the digital images. The range camera consists of an infrared laser projector combined with a monochrome digital camera. The range camera determines the range images by projecting an infrared structured pattern onto the scene and determining the range as a function of position using parallax relationships given a known geometrical relationship between the projector and the digital camera. 
     In some scenarios, it would be desirable to be able to form a stereoscopic image of the users of the gaming system using the image data captured with the digital imaging system (e.g., at a decisive moment of victory in a game).  FIG. 8  shows a flowchart illustrating how the method of the present invention can be adapted to form a stereoscopic image  860  from a main image  800  and a corresponding main image range map  805  (e.g., captured using the Kinect range camera). The main image  800  is a conventional 2-D image that is captured using a conventional digital camera (e.g., the Kinect RGB digital camera). 
     The main image range map  805  can be provided using any range sensing means known in the art. In one embodiment, the main image range map  805  is captured using the Kinect range camera. 
     In other embodiments, the main image range map  805  can be provided using the method described in commonly-assigned, co-pending U.S. patent application Ser. No. 13/004,207 to Kane et al., entitled “Forming 3D models using periodic illumination patterns,” which is incorporated herein by reference. In other embodiments, the main image range map  805  can be provided by capturing two 2D images of the scene from different viewpoints and then determining a range map based on identifying corresponding points in the two image, similar to the process described with reference to  FIG. 2 . 
     In addition to the main image  800  and the main image range map  805 , a background image  810  is also provided as an input to the method. The background image  810  is an image of the image capture environment that was captured during a calibration process without any users in the field-of-view of the digital imaging system. Optionally, a background image range map  815  corresponding to the background image  810  can also be provided. In a preferred embodiment, the main image  800  and the background image  810  are both captured from a common capture viewpoint  802 , although this is not a requirement. 
     The main image range map  805  and the optional background image range map  815  can be captured using any type of range camera known in the art. In some embodiments, the range maps are captured using a range camera that includes an infrared laser projector and a monochrome digital camera, such as that in the Kinect game console. In other embodiments, the range camera includes two cameras that capture images of the scene from two different viewpoints and determines the range values by determining disparity values for corresponding points in the two images (for example, using the method described with reference to  FIGS. 2 and 3 ). 
     In a preferred embodiment the main image  800  is used as a first-eye image  850  for the stereoscopic image  860 , and a second-eye image  855  is formed in accordance with the present invention using a specified second-eye viewpoint  820 . In other embodiments, the first-eye image  850  can also be determined in accordance with the present invention by specifying a first-eye viewpoint that is different than the capture viewpoint and using an analogous method to adjust the viewpoint of the main image  800 . 
     A determine warped main image step  825  is used to determine a warped main image  830  responsive to the main image  800 , the main image range map  805 , the capture viewpoint  802  and the second-eye viewpoint  820 . (This step is analogous to the determine warped main image step  525  of  FIG. 6 .) A determine warped background image step  835  is used to determine a warped background image  840  responsive to the background image  810 , the capture viewpoint  802  and the second-eye viewpoint  820 . For cases where a background image range map  815  has been provided, the warping process of the determine warped background image step  835  is analogous to the determine warped complementary images step  535  of  FIG. 6 . 
     For cases where the background image range map  815  has not been provided, a number of different approaches can be used in accordance with the present invention. In some embodiments, a background image range map  815  corresponding to the background image  810  can be synthesized responsive to the background image  810 , the main image  800  and the main image range map  805 . In this case, range values from background image regions in the main image range map  805  can be used to define corresponding portions of the background image range map. The remaining holes (corresponding to the foreground objects in the main image  800 ) can be filled in using interpolation. In some cases, a segmentation algorithm can be used to segment the background image  810  into different objects so that consistent range values can be determined within the segments. 
     In some embodiments, the determine warped background image step  835  cab determine the warped background image  840  without the use of a background image range map  815 . In one such embodiment, the determination of the warped background image  840  is performed by warping the background image  810  so that background image regions in the warped main image  830  are aligned with corresponding background image regions of the warped background image  840 . For example, the background image  810  can be warped using a geometric transform that shifts, rotates and stretches the background image according to a set of parameters. The parameters can be iteratively adjusted until the background image regions are optimally aligned. Particular attention can be paid to aligning the background image regions near any holes in the warped main image  830  (e.g., by applying a larger weight during the optimization process), because these are the regions of the warped background image  840  that will be needed to fill the holes in the warped main image  830 . 
     The warped main image  830  will generally have holes in it corresponding to scene information that was occluded by foreground objects (i.e., the users) in the main image  800 . The occluded scene information will generally be present in the warped background image  840 , which can be used to supply the information needed to fill the holes. A determine second-eye image step  845  is used to determine the second-eye image  855  by combining the warped main image  830  and the warped background image  840 . 
     In some embodiments, the determine second-eye image step  845  identifies any holes in the warped main image  830  and fills them using pixel values from the corresponding pixel locations in the warped background image. In other embodiments, the Bayesian-based view synthesis approach described above with reference to  FIG. 6  can be used to combine the warped main image  830  and the warped background image  840 . 
     The stereoscopic image  860  can be used for a variety of purposes. For example, the stereoscopic image  860  can be displayed on a stereoscopic display device. Alternately, a stereoscopic anaglyph image can be formed from the stereoscopic image  860  and printed on a digital color printer. The printed stereoscopic anaglyph image can then be viewed by an observer wearing anaglyph glass to view the image, thereby providing a 3-D perception. Methods for forming anaglyph images are well-known in the art. Anaglyph glasses have two different colored filters over the left and right eyes of the viewer (e.g., a red filter over the left eye and a blue filter over the right eye). The stereoscopic anaglyph image is created so that the image content intended for the left eye is transmitted through the filter over the user&#39;s left eye and absorbed by the filter over the user&#39;s right eye. Likewise, the image content intended for the right eye is transmitted through the filter over the user&#39;s right eye and absorbed by the filter over the user&#39;s left eye. It will be obvious to one skilled in the art that the stereoscopic image  860  can similarly be printed or displayed using any 3-D image formation system known in the art. 
     A computer program product can include one or more non-transitory, tangible, computer readable storage medium, for example; magnetic storage media such as magnetic disk (such as a floppy disk) or magnetic tape; optical storage media such as optical disk, optical tape, or machine readable bar code; solid-state electronic storage devices such as random access memory (RAM), or read-only memory (ROM); or any other physical device or media employed to store a computer program having instructions for controlling one or more computers to practice the method according to the present invention. 
     The invention has been described in detail with particular reference to certain preferred embodiments thereof, but it will be understood that variations and modifications can be effected within the spirit and scope of the invention. 
     PARTS LIST 
     
         
           110  data processing system 
           120  peripheral system 
           130  user interface system 
           140  data storage system 
           200  digital video 
           205  video frame 
           210  determine disparity maps step 
           215  disparity map series 
           220  disparity map 
           225  refine disparity maps step 
           230  refined disparity map series 
           235  refined disparity map 
           240  determine range maps step 
           245  range map series 
           250  range map 
           305  select video frame step 
           310  particular video frame 
           315  determine extrinsic parameters step 
           320  extrinsic parameters 
           325  determine intrinsic parameters step 
           330  intrinsic parameters 
           335  define candidate frames step 
           340  candidate video frames 
           345  determine similarity scores step 
           350  image similarity scores 
           355  select subset step 
           360  video frames subset 
           365  determine position difference scores step 
           370  position difference scores 
           375  select video frame step 
           380  selected video frame 
           385  determine disparity map step 
           405  determine input camera positions step 
           410  input camera positions 
           415  determine input camera path step 
           420  input camera path 
           425  determine smoothed camera path step 
           430  smoothed camera path 
           432  determine smoothed camera positions step 
           434  smoothed camera positions 
           435  determine stabilized video step 
           440  stabilized video 
           445  stabilized video frames 
           450  determine second-eye smoothed camera positions step 
           455  second-eye smoothed camera positions 
           460  determine second-eye stabilized video step 
           465  second-eye stabilized video 
           470  second-eye stabilized video frames 
           475  stabilized stereoscopic video 
           480  input camera path graph 
           485  smoothed camera path graph 
           500  main image 
           505  set of complementary images 
           510  complementary image 
           515  viewpoint 
           520  target viewpoint 
           525  determine warped main image step 
           530  warped main image 
           535  determine warped complementary images step 
           540  warped complementary images 
           545  determine output image step 
           550  output image 
           800  main image 
           802  capture viewpoint 
           805  main image range map 
           810  background image 
           815  background image range map 
           820  second-eye viewpoint 
           825  determine warped main image step 
           830  warped main image 
           835  determine warped background image step 
           840  warped background image 
           845  determine second-eye image step 
           850  first-eye image 
           855  second-eye image 
           860  stereoscopic image

Metadata:
Filing Date: 20111117
Publication Date: 20150526
Grant Date: 20150526
Priority Date: 20111117
Inventors: WANG SEN
Assignee: APPLE INC
CPC Classifications: [{"code": "H04N13/0221", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N13/026", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N13/0271", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N13/0022", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N13/221", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N13/211", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N13/261", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N13/271", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N13/128", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N13/261", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N13/211", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N13/221", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N13/271", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N13/128", "inventive": true, "first": true, "tree": "[]"}]
Family ID: 48426431