Patent Publication Number: US-11030436-B2

Title: Object recognition

Description:
BACKGROUND 
     Object recognition and retrieval is an area of computer vision and image processing. In one application, a physical object can be converted into digital representations of the object, and the digital representation of the object can be converted into a physical article. The digital-to-physical transformation can be achieved via two-dimensional printing of data files using color or monotone printers or via three-dimensional printing or additive manufacturing of data files using three-dimensional printers. Object recognition can be used to retrieve data files and information associated with the object as well as for other content interaction. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating an example of method of object recognition. 
         FIG. 2  is a schematic diagram illustrating example system for applying the example method of  FIG. 1 . 
         FIG. 3  is a block diagram illustrating an example implementation of the example method of  FIG. 1 . 
         FIG. 4  is a block diagram illustrating an example feature of the example method of  FIG. 1 . 
         FIG. 5  is a block diagram illustrating another example feature of the example method of  FIG. 1 . 
     
    
    
     DETAILED DESCRIPTION 
     Object recognition is a basis for many advanced applications including augmented reality, human-computer interaction, three-dimensional printing and others. Object recognition systems can learn different object classes or instances from a training dataset, where the training information includes a variety of examples of objects to be recognized. In cases of a new unlabeled query, the system can return an object class or name of a specific object or a notice that the object was not found. 
     Two-dimensional image instance recognition systems typically attempt to recognize a two-dimensional image from a set of known two-dimensional images. Object instance recognition is trained on images of the objects as seen from different viewing angles. For a single, three-dimensional object, these images can be different. Further, the number of positions of the object for training depends on the shape of the object and can be vast. Another limitation of two-dimensional object recognition is that mapping or projecting an object to a two-dimensional image causes information loss. 
     Three-dimensional depth data gathered with depth sensors provide rich object data in the form of real-time color-depth images as point clouds. Performance, however, depends on the quality of the data including noise level, resolution, and precision. In contrast to image sensors, different color-depth sensors have a large variation of characteristics. Three-dimensional color-depth sensors have difficulty in processing small objects, or objects that are black or shiny. Further, processing three-dimensional depth data can be computationally expensive relative to processing two-dimensional images. 
     An example system for object recognition includes a two-dimensional image pipeline and a three-dimensional point cloud pipeline. In one example, the pipelined operations are performed generally concurrently and a confidence score is assigned to each result. In one example, a fixed-sensor system performs a geometry calibration and a white balance correction including depth-based object segmentation to provide a color representation. Color representation can be applied in both pipelines. 
       FIG. 1  illustrates a method  100  of recognizing an object. In one example, the object is compared against candidates in a dataset to determine a matching candidate. In this example, the dataset can include multiple candidates each having a three-dimensional point cloud and a two-dimensional image. A three-dimensional point cloud of the object is compared to a three-dimensional point cloud of a candidate from a dataset to determine a first confidence score at  102 . The point cloud of the object includes a color appearance calibrated from a white balance image. The three-dimensional comparison at  102  includes comparing the color appearance of the three-dimensional point cloud of the object with the three-dimensional point cloud of the candidate. A two-dimensional image of the object is compared to a two-dimensional image of a candidate from the dataset to determine a second confidence score at  104 . The two-dimensional comparison at  104  includes comparing the color metrics of the two-dimensional image of the object with the two-dimensional image of the candidate. In one example, the comparison at  102  and the comparison at  104  are performed generally concurrently and include a comparison of color metrics. If the highest-scoring candidate from the two-dimensional comparison at  104  matches the highest-scoring candidate from the three-dimensional comparison at  102 , the likelihood is very high the object has been identified. If the highest-scoring candidate from the two-dimensional comparison at  104  does not match the highest-scoring candidate from the three-dimensional comparison at  102 , however, one of the first and second confidence scores is selected to determine which of the three-dimensional candidate or the two-dimensional candidate corresponds with the object at  106 . If the selected confidence score does not at least meet a threshold, the object has not been found in the candidate set. 
     The example method  100  can be implemented to include a combination of one or more hardware devices and computer programs for controlling a system, such as a computing system having a processor and memory, to perform method  100  to recognize the object. Method  100  can be implemented as a computer readable medium or computer readable device having set of executable instructions for controlling the processor to perform the method  100 . Computer storage medium, or non-transitory computer readable medium, includes RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, universal serial bus (USB) flash drive, flash memory card, or other flash storage devices, or any other storage medium that can be used to store the desired information and that can be accessed by a computing system. Accordingly, a propagating signal by itself does not qualify as storage media. 
       FIG. 2  illustrates an example system  200  that can apply method  100  to recognize object  202 . Object  202  is placed against a working surface  204  and imaged with a color camera  206  and a color-depth camera  208 . In one example, the working surface  204  is a generally planar mat having a solid neutral color, such as a white background. The color camera  206  in one example can include a generally higher resolution camera having red, green, and blue sensors, or RGB camera, that can provide color information for each pixel. The color camera  206  can generate a relatively high-resolution two-dimensional color image data  210  of the object  202 . The color-depth camera  208  can include color sensors and a depth sensor to generate images of the object that are merged to form a point cloud data of the object  212 . In one example, the color-depth camera  208  can include an RGB-D camera, such an example having red, green, blue, infrared sensors, to generate color and depth data for each pixel. The point cloud data of the object  212  can be formed in the color-depth camera  208  or subsequently in processing. The color sensors of the color-depth camera  208  can produce a color image of a relatively lower resolution than that with the color camera  206 . In one example, the system  200  may include multiple color-depth cameras  208 , which can reduce training workload and enhance matching confidence. Cameras  206 ,  208  and working surface  204 , in one example, can have a fixed position with respect to each other and ambient lighting is generally stable or generally does not include variation during imaging. 
     The color image data of the object  210  and the point cloud data of the object  212  are provided to a computer system  220  having a processor  222  and memory  224  that are configured to implement an example method of this disclosure, such as method  100 , as a set of computer readable instructions stored in memory  224  for controlling the processor  222  to perform a method such as method  100 . In one example, the set of computer readable instructions can be implemented as a computer program  226  that can include various combinations of hardware and programming configured to operate on computing system  220 . Computer program  226  can be stored in memory  224  and executed by the processor  222  to compare the color image of the object  210  and the point cloud of the object  212  against candidates  230  in a dataset  232 . In the example, each candidate  230  includes a three-dimensional point cloud of the candidate  234  and a two-dimensional image of the candidate  236 . In one example, each candidate  230  includes one three-dimensional point cloud of the candidate  234  and multiple two-dimensional images of the candidate  236 . 
       FIG. 3  illustrates an example method  300 , which can be an example implementation of method  100  performed with system  200 . Method  300  includes receiving a two-dimensional image  302 , such as color image data  210  of the object  202 , and a three-dimensional image or point cloud  304 , such as color-depth point cloud data  212  of the object  202 , for calibration at  310 . The two-dimensional image  302  is processed with two-dimensional image recognition at  312  and the calibrated three-dimensional image  304  is processed with three-dimensional object recognition at  314 , which each separately compares the respective images  302 ,  304  against candidates in a dataset  306 , such as dataset  232 . Each recognition process at  312 ,  314  returns a candidate it considers to be the recognized object along with a corresponding confidence score. If the candidates from each recognition process at  312  and  314  are the same candidate at  316 , the candidate is returned as the recognized object at  318 . If the candidates are different at  316 , the confidence scores are compared and the dominating confidence score is selected at  320 . If the dominating confidence score meets or surpasses a threshold amount at  322 , the candidate corresponding with the dominating confidence score is returned as the recognized object at  318 . If, however, the confidence score does not meet the threshold amount, the method  300  outputs that the object was not recognized at  324 . 
     Calibration at  310  can include point cloud segmentation at  332  and white balance at  334 . Calibration at  310  can be initialized with a reference three-dimensional plane parameter  336  of the working surface  204  for point cloud segmentation at  332  and a reference two-dimensional color image  338  of the working surface  204  for white cloud balance at  334 . Point cloud segmentation  332  includes subtracting a reference working surface  336  from the three-dimensional object point cloud  304 . The reference two-dimensional image  338  is applied to perform a pixel-wise white balance on the two-dimensional image  302  of the object to generate a white-balance corrected two-dimensional image. In one example, the white-balance corrected two-dimensional image is mapped onto the three-dimensional point cloud  304  according to system  200  imaging calibration, which can include the three-dimensional transformation among the internal coordinates of the RGB-D sensors. The original two-dimensional color image  302  is provided as an input to the two-dimensional recognition  312 , and, in one example, the white-balance corrected two-dimensional image is provided as an input to the two-dimensional image recognition process  312  as a calibrated two-dimensional image of the object. The segmented, white-balanced point cloud is provided as an input to the three-dimensional object recognition process at  314  as a calibrated three-dimensional point cloud of the object. 
     Segmentation of the object from the background or working surface at  332  can reduce computation time of the three-dimensional recognition  312 . On an example of system  200 , the color-depth camera  208  position and angle with respect to the working surface  204  are fixed and the working surface  204  is a generally planar mat. In one example of developing the reference working surface  336 , the corners of the mat are detected and recorded via color-depth camera  208 . The three-dimensional points corresponding with the mat are fitted to a three-dimensional plane and the parameters of the plane are recorded. The value of the plane along the axis to the color-depth camera, or z-axis, is subtracted from the three-dimensional point cloud of the object  304  and any remaining z value lower than a set threshold is labeled as background and ignored. 
     Color appearance is included in the comparisons  102 ,  104  of method  100 . But color appearance of the object can vary under different ambient lighting conditions. In one example, the color of mat of working surface  204  is provided as white. The reference two-dimensional image  338  of the working surface  204  is captured via color camera  206  for a pixel-wise white balance. The reference two-dimensional image  338  and the reference working surface  336  can be captured at the simultaneously or separately via the color camera  206  and the color-depth camera  208 . In one example, white balance is performed in the CIE XYZ color space of the International Commission of Illumination (CIE), and a transformation from the RGB color space to the CIE XYZ color space is included, such as via CIE standards and entries. The white balanced two-dimensional image is mapped onto the three-dimensional depth image  304 . In one example, system geometric calibration data is used to include three-dimensional transformations among internal coordinates of the vision sensors of the color-depth camera  208 . 
       FIG. 4  illustrates an example method  400  of the two-dimensional recognition  312  using, for example, system  200 . Method  400  receives a two-dimensional image of the object and, in some examples, a calibrated two-dimensional image of the object at  402 . In one example, the method  400  can be based on local scale and orientation invariant feature transformation (SIFT). The received two-dimensional image and, in one example, a white-balance corrected two-dimensional image are processed to determine keypoints and descriptors at  404 . The keypoints and descriptors of input two-dimensional image are matched with the keypoints and descriptors of the two-dimensional image of the candidate  236  at  406 . In one example, keypoints are detected from grayscale versions of the two-dimensional images of the object and candidates, and descriptors are subsequently determined. The matched points are compared and a determination is made as to a color similarity between the matched keypoints at  408 . A score with geometric and descriptor distances is provided for the corresponding candidate at  410 . The matching  406 , color appearance comparison  408 , and scoring at  410  can be repeated for each two-dimensional image of the candidates in the dataset  232 , or subset of the dataset  232 , and the candidate with the highest score and its corresponding score, or confidence score, is output at  412 . 
     The color similarity determination at  408  provides additional discrimination. For each keypoint characterized by its location and a circular window, in one example, an average RGB value is computed and then converted to a CIELab (L*a*b*) value. A color difference, or CIE Delta E, such as CIE DE2000, is used to measure whether a pair of keypoints is similar in color. If the Delta E of a keypoint pair exceeds a selected threshold, the match is removed. 
     In one example of scoring at  410 , the geometric relationship of matched keypoints between the two-dimensional image of the object and a two-dimensional image of the candidate may be described via homography in two separate components on system  200 . A first component is a perspective transform applied it to all images captured by the camera  206 . A second component is an affine transform that is determined from the matched keypoints that may include imposed constraints in scaling and shearing factors to an affine transform. The geometric distance between matched keypoints may be computed and used for an additional verification. A maximum distance threshold can determine whether a match should be rejected under a given affine transform. For example, if the total number of geometric verified matching keypoint pairs is above a selected value, which is the minimum number of point pairs to compute an affine transform, a score for an image pair can be computed. 
       FIG. 5  illustrates an example method  500  of the three-dimensional recognition  314  using, for example, system  200 . Method  500  receives a calibrated three-dimensional point cloud of the object at  502 . In one example, each three-dimensional point cloud of each candidate are smoothed with a moving least square (MLS) filter prior to being stored in dataset  232  and the calibrated three-dimensional point cloud of the object is smoothed with the MLS filter during method  500 . The calibrated three-dimensional point cloud of the object received from calibration at  310  is processed to extract selected features at  504 . In one example, the particular selected features are included with each three-dimensional point cloud of the candidate  234  in dataset  232 . The selected features extracted at  504  can include object color appearance, three-dimensional keypoints, three-dimensional feature and local color appearance for each keypoint. 
     Object color appearance is compared with color appearance of the candidates at  506 . Object color appearance comparison at  506  can improve the throughput of the method  500  as it creates a smaller subset of candidates for three-dimensional comparison. In one example, the color comparison can be performed in the L*a*b* color space as it was less sensitive to lighting changes as others, such as hue-saturation histogram, hue histogram, and a*−b* channel histogram on segmented object points. The luminance channel was discarded and the average (a*, b*) was applied on segmented object points and Euclidean distance as the metric. 
     Three-dimensional feature matching and local color verification is performed with the point cloud of the object and the point cloud of the candidates at  508 , as on the subset of candidates. Three-dimensional point features can be classified as global or local descriptors. Global descriptors can be used for objects that have been segmented well and represent the entire object point cloud with one descriptor. Local descriptors, in contrast, are computed locally around keypoints and one object point cloud typically includes several keypoints. In an example of the present method  500  in which the three-dimensional point cloud at  502  can vary from on the objet orientation and location with respect to the background, local keypoint detectors and feature descriptors are applied. 
     In one example, ISS (Intrinsic Shape Signatures) local keypoint detection is used to extract shape keypoints from the filtered object point cloud. ISS defines an intrinsic reference frame at a basis point with a supporting radius by using the eigenanalysis of the point scatter matrix and is a generalization of the classical surface normal reference for shape feature extraction independent of view. A SHOT (Signature of Histogram OrientaTion) feature descriptor can be extracted on the detected keypoints. The descriptor is computed using a three-dimensional spherical grid centered on the keypoint then built from the subdivision of the grid structure, and is represented in bins of a three-dimensional histogram. 
     In one example, an average L*a*b* color vector is calculated in its local neighborhood for each keypoint detected, which is used to verify the keypoint matching. To determine the similarity between two point clouds, feature point matching between keypoints are performed in the SHOT feature space. Compared to image feature matching, shape features can be less distinctive, especially if the RGB-D sensor data is noisy. The local average L* a* b* color vector and CIE Delta E distance are used to verify the matching points have sufficient color similarity. 
     Matches between the candidates and the object are determined at  510 . For example, the candidates in the subset of candidates are ranked in terms of correspondence with the object. The highest ranking candidates are further processed to determine the best match along with a confidence score. In one example, a RANSAC (Random Sample Consensus) method can be applied to determine if a good transformation is found, and the subset of candidates are ranked based on the number of keypoints supporting the good transformation. In one example, an Iterative Closest Points (ICP) process is applied to further align the subset of candidates, and a selected number of top candidates, such as the top five ranking candidates, are selected. The best match can be determined from calculating the average distance of all points between the point clouds of the remaining candidates and the object point cloud. The candidate with the highest score and its corresponding score, or confidence score, is output at  512 . 
     Methods  400  and  500  can be performed separately and preferably concurrently. If both methods  400 ,  500  return valid results and the candidates are different, such as method  400  returns first candidate result image i r1  and method  500  returns second candidate result image ire, a confidence score SC(i q , i r ) is calculated between the image of the object i q  and result image from each method. In one example, a confidence score SC(i q , i r ) can be determined as: 
     
       
         
           
             
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     where N m  is the number of matched keypoints between image i q  and i r  that passed geometric verification/consistency in two-dimensional and three-dimensional approaches of methods  400 ,  500 , K q  and K r  are the number of keypoints of image i q  and i r . 
     Because N m ≤K q , and N m ≤K r , 0≤SC(i q , i r )≤1, the confidence score in this example is greater than or equal to zero and less than or equal to one. 
     If the confidence score SC(i q , i r1 ) is larger than the confidence score SC(i q , i r2 ) and a selected threshold, the final result is first candidate result image i r1 , and if the confidence score SC(i q , i r1 ) is larger than the confidence score SC(i q , i r1 ) and a selected threshold, the final result is second candidate result image i r2 . Otherwise, the output can be set to not found, such as at  324 . 
     Although specific examples have been illustrated and described herein, a variety of alternate and/or equivalent implementations may be substituted for the specific examples shown and described without departing from the scope of the present disclosure. This application is intended to cover any adaptations or variations of the specific examples discussed herein. Therefore, it is intended that this disclosure be limited only by the claims and the equivalents thereof.