Abstract:
A method for corresponding, evolving and tracking feature points in a three-dimensional space performs corresponding, evolving and tracking on the features points after transferring the two-dimensional feature point information in an image information into a corresponding state in the three-dimensional space. A recursion is employed to continuously update the states of the feature points in the three-dimensional space and evaluate the stability of the feature points by evolving. Hence, the obtained three-dimensional feature point information has a stronger corresponding relationship than the feature point information conventionally generated on the basis of the two-dimensional feature point information. As a result, a more precise three-dimensional scene can then be constructed.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This non-provisional application claims priority under 35 U.S.C. § 119(a) on Patent Application No(s). 095136372 filed in Taiwan, R.O.C. on Sep. 29, 2006, the entire contents of which are hereby incorporated by reference. 
       BACKGROUND OF THE INVENTION 
       [0002]    1. Field of Invention 
         [0003]    The present invention relates to a method for generating feature point information, and more particularly to a method for corresponding, evolving and tracking feature points in a three-dimensional space, so as to obtain three-dimension feature point corresponding information required for constructing a three-dimensional scene. 
         [0004]    2. Related Arts 
         [0005]    A three-dimensional scene is constructed mainly by continuously retrieving the feature point information from the image sequence. After recovering the projection geometry represented by the feature point information in an image corresponding manner, a three-dimensional scene can then be reconstructed. The reconstruction precision of the three-dimensional scene is mainly subject to the strong corresponding relationship among the feature point information which are obtained from the image sequence, wherein the strong corresponding relationship comes from whether the feature corresponding of different images are precisely determined or not. In other words, if the corresponding relationship between the reliable feature point information in successive two images is established, a precise reconstruction of three-dimensional scene can then be expected. 
         [0006]    According to different practical requirements and situations, recently the technical field related to “technique for establishing the corresponding relationship between image features” can be categorized into three types. The first type is a technique developed for stereo image. The second type is a technique developed for images having large angle variance and long baseline. The last type is a technique developed for image sequences having small overall variance. The last type is the most commonly used technique for processing an image sequence (constituted by a series of image frames being continuous or discontinuous in time) shot by a handheld digital video camera, which can be applied to the prior art of processing “the establishment of the corresponding relationship between image features” of the similar image sequence. The technical content disclosed in the U. S. Patent No. U.S. Pat. No. 5,606,627 mentioned that according to two images with a known shooting state (i.e., the shooting parameters of the camera are known), the common features of points and edges of the two images are retrieved, and the three-dimensional space coordinates of the feature points are respectively estimated, and the three-dimensional scene is obtained by parallax estimation. This technique uses two-dimensional feature point information to update the feature point information, but the correctness of the feature corresponding relationship is not taken into consideration. However, U.S. Patent No. U.S. Pat. No. 5,946,041 mentioned a feature corresponding relationship established from image sequence in a two-dimensional space, wherein the features are found out in the first image, an operation of the corresponding process is performed in the second image, and then a correct feature corresponding process is established by correcting the estimated two-dimensional motion vector. In the technique, the correctness in the feature corresponding relationship is further taken into account and processed, and the feature point information is also updated based on the two-dimensional feature point information. 
         [0007]    The aforementioned prior arts all belong to the technical content of “the establishment of the image feature corresponding relationship” totally based on the two-dimensional feature point information. The technical content includes the corresponding and tracking processes on the feature points, but has the following problems since the process information provided by the two-dimensional feature point information is limited. 
         [0008]    (1) The robustness of the image feature corresponding relationship established by the two-dimensional feature point information is deficient. 
         [0009]    (2) Errors tend to be generated while transferring from the two-dimensional feature point information into the three-dimensional scene. 
         [0010]    Therefore, when the feature point information is used to reconstruct the three-dimensional scene, the accuracy of the three-dimensional scene will be deficient. This is an inherent problem in the process of corresponding and tracking the feature points using the two-dimensional feature point information. Further, errors are generated while transferring from the two-dimensional feature point information to the three-dimensional scene. As a result, a satisfied effect of the reconstructed three-dimensional scene cannot be achieved. 
       SUMMARY OF THE INVENTION 
       [0011]    In view of the above problems, the main object of the present invention is to provide a method for corresponding, evolving and tracking feature points in a three-dimensional space, which is performed by: (1) using the time series analysis technique to update and track the states of the feature points in three-dimensional space, and (2) using the feature evolving process to screen the feature points needed to be remained in the three-dimensional space according to the stability of the corresponding relationship of each feature point, and using a feature corresponding, evolving and tracking process in a recursive way to finally screen a feature point group having a strong corresponding relationship to output, so as to establish a three-dimensional scene with higher accuracy. 
         [0012]    The method of the present invention is especially suitable for processing an image sequence captured by a handheld digital video camera. The feature points in successive two images may be visible or invisible due to the slight change in the shooting angle. Thus, besides the basic process of the two-dimensional feature point information, the corresponding, evolving and tracking must be performed through the states of the feature points in the three-dimensional space, so as to screen a feature point group having a strong corresponding relationship. The present invention is not only suitable for processing the image sequence comprising image frames discontinuous in time, but also can be directly applied in processing a group of spatial points having three-dimensional track or motion on the time axis. 
         [0013]    Regarding the time series analysis technique used in the present invention, Kalman filter time series analysis model is adopted as one of the embodiments. 
         [0014]    The present invention calculates the strength of the corresponding relationship of the feature points when they evolve with addition and deletion of the feature points. The initial state of the newly added feature point is initialized according to the distance from the neighboring feature points and the survival time thereof. In addition, deletion of a feature point is determined by calculating the error generated during the corresponding or updating process of the feature points. 
         [0015]    The features and examples of the embodiments of the present invention are illustrated in detail with reference to the drawings below. 
         [0016]    Broad scope of applicability of the present invention will become apparent from the detailed description given hereinafter. However, it should be noted that the detailed description and specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description. 
     
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0017]    The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the office upon request and payment of the necessary fee. 
           [0018]    They will be full comprehensibility of the present invention from the detailed description given herein below for illustration only, and thus are not limitative of the present invention, and wherein: 
           [0019]      FIG. 1  is a schematic view of the general operation according to the present invention; 
           [0020]      FIG. 2  is a schematic view of the operation of a time series analysis according to the present invention; 
           [0021]      FIG. 3  is a flow chart of a method according to the present invention; 
           [0022]      FIGS. 4A to 4E  are schematic views of a feature point information evolving process according to the present invention; 
           [0023]      FIG. 5A  is a three-dimensional scene reestablished by feature point information generated by the conventional art; and 
           [0024]      FIG. 5B  is a three-dimensional scene reestablished by feature point information generated by the present invention. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0025]    Referring to  FIG. 1 , a basic schematic view of a general operation of the present invention is shown. As shown in the figure, the present invention is implemented by two stages of process comprising a time series analysis  110  and a feature evolving  120 . The method of the present invention is accomplished by executing a computer software program on a computer platform. As shown by the dashed line, the method is accomplished by a feature point information generating system  100  stored as the computer software program. The input data of the whole feature point information generating system  100  is an object. The object can be an image sequence  10  including a series of image frames being discontinuous in time or a group of space points having three-dimensional track or motion mode on the time axis, and the output data is three-dimensional feature point information  20  of the three-dimensional scene. The operating steps of the method of the present invention are illustrated on the premise that the object of the input data is the image sequence  10 , and the difference in the operating steps caused by the difference in the input data will be given later on. 
         [0026]      FIG. 2  is a schematic view of the operation of the time series analysis  110  in the method according to the present invention. The time series analysis  110  mainly comprises three processes, namely state initializing process  111 , system modeling  112  and state updating process  113 . The state initializing process  111  is responsible for initializing the feature point information of the input object (e.g., the image sequence  10 ) on each time point, and determining the feature point information  30  (including the feature point and the three-dimensional state) of the image frame at the time point. Taking the image sequence  10  as an example, after being initialized, the feature point information of the first image frame in the input image sequence  10  will become feature point information including the feature point group and the three-dimensional state collection corresponding to the feature point group. The system modeling  112  is responsible for establishing an appropriate analysis model to match with the input object (i.e., the image sequence  10 ), which includes a system model and a state description expression (as shown in the part marked with  40 ). This part is set by the data input from outside according to motion mode or other attributes of the practically input image sequence  10 , and also can be properly adjusted according to the motion mode or other attributes of different input objects. After the initialization is accomplished by the above steps, the input image sequence  10  is illustrated through the analysis model in the system modeling program  112  according to the property during shooting. Finally, the state updating process  113  is used to update the state of each feature point. This part operates together with the part of feature evolving process  120  in  FIG. 1 , wherein the feature point information of each image frame after being initialized and described by the analysis model is transferred into the feature evolving process  120  for screening the feature points, and then the feature point information are updated according to the screening result (i.e., the reliable feature point information  50 ), until the process on all input image frames is finished, and finally the feature point group having a strong corresponding relation is remained for being used for reconstructing an accurate three-dimensional scene. In the stage of feature evolving  120 , it must be emphasized that the screening (appearing or disappearing) of the feature points is a process like the biologic evolving, wherein the three-dimensional state of the feature points passes down in a recursion means process, thus being different from a simple screening good from bad. 
         [0027]    The detailed method flow is illustrated with reference to  FIGS. 3 and 4A  to  4 E, which is illustrated on the premise that the input object is the image sequence  10 . 
         [0028]    First, when each image frame in the image sequence  10  is input according to the time point, the part of the time series analysis  110  executes the processes described in  FIG. 2 , and the analysis model adopted in the time series analysis is mainly the Kalman Filter time series analysis model: 
         [0029]    (1) The state initializing process  110  mainly includes two situations. The first executing situation of the initializing process is executed as that, when a first image frame is input, i.e., when time t=0, the initializing process directly generates a feature point group {Y 0 } of the first image frame (Step  200 ), and generates a three-dimensional state collection {X 0 } corresponding to the feature point group (Step  210 ) as the feature point information thereof. As shown by the triangular pattern in  FIG. 4 , a three-dimensional state collection {X 0 }  400  corresponding to the time t=0 is directly generated by the feature point group {Y 0 }  300  of the first image frame at the time t−0. 
         [0030]    {X 0 } is obtained by initializing three-dimensional states on each feature point y 0  of the {Y 0 } through the Kalman Filter time series analysis model. The three-dimensional state x 0  includes the horizontal position, vertical position and depth position of the feature point in the three-dimensional space. The depth position can be generated by first computing {Y 0 } and {Y 1 } through the three-dimensional vision manner, and then projecting through a camera model. {Y 1 } is obtained by finding corresponding point {Y 0 } in the first image frame. 
         [0031]    Another situation of the initializing process is executed as that, when the image frame is input not for the first time (i.e., when the input image frame is not the first image frame), i.e., the image frame input at the time t, since the reliable feature point information  50  (i.e., the feature point information including the feature point group and the three-dimensional state collection) remained after the operation of the former image frame is updated into the feature point information of the former image frame, the next input image frame takes the updated feature point information as the initialization result (Step  260 ). This situation is shown as the triangular pattern in  FIG. 4B . 
         [0032]    (2) After the state initializing process is finished, the feature point information of the image frame being processed is transferred into the system modeling  112  for predicting the state, and the predicting of the feature point information ({Y t+1 }, {x t+1 }) of the initialized feature point information {Y t } and the three-dimensional state {x t } at the next time point is performed through the established analysis model (Step  220 ). As described above, the analysis model used in the embodiment of the present invention is the Kalman Filter time series analysis model, wherein the descriptions on Y t  and X t  are represented by the following expressions: 
         [0000]        X   t+1   =F   t   X   t   +U   t   +Q   t ; 
         [0000]        Y   t   =H   t   X   t   +R   t ; 
         [0033]    F t  simulates the linear variation process of the state X t  along with the time, U t  is a known translation amount at the state of X t , H t  simulates the relationship between X t  and Y t , Q t  and R t  simulate the interference of noise, wherein Q t  also can be represented as Q t ˜N(0, q t ), R t  also can be represented as R t ˜N (0, r t ). Therefore, the prediction value of each X t+1  is represented as 
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       The part of y t+1  can be obtained from each y t  in {Y t } through a feature matching method. 
       [0034]    As shown in  FIG. 4B , the circular pattern shown in the upper portion is a predicted feature point group {Y t+1 }  310  at the time t+1, and the triangular pattern shown in the lower portion is the predicted three-dimensional state collection {X t+1 }  410 . 
         [0035]    (3) After the state is predicted, the three-dimensional state collection {X t+1 } in the prediction result must be properly corrected (Step  230 ). The above can be mainly achieved by the correcting model existing in the Kalman Filter time series analysis model, and the correcting model is represented by the following expression. 
         [0000]        X   t+1   ˜N ( {circumflex over (X)}   t+1   , P   t+1 ); 
         [0036]    wherein 
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         [0037]    In the Kalman Filter time series analysis model, the error E and gain K are 
         [0000]    respectively defined as E t+1 =(Y t+1 −H t+1 {circumflex over (X)} t+1 ) and 
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       Therefore, after the process, a shift is generated on the prediction, and the three-dimensional state collection is corrected as {{circumflex over (X)} t+1 }, and is reset as {X t+1 }. 
       [0038]    As shown in  FIG. 4B , the circular pattern shown in the lower portion is the predicted three-dimensional state collection {X t+1 } generated after being corrected at the time t+1, with the position being corrected from the triangular pattern shown in the lower portion before being corrected to the position of the circular pattern. It includes predicted feature point group {Y t+1 }  310  at the time t+1 and the predicted three-dimensional state collection {X t+1 }  410  at the time t+1. 
         [0039]    The above correcting model can be properly adjusted according to the employed analysis model, and further can be properly adjusted according to the motion mode of the input image sequence  10  in the three-dimensional space. The related adjusting method differs according to the different analysis models, but the present invention holds the design flexibility of the adjustment of this part. 
         [0040]    (4) The corrected feature point information ({Y t+1 }, {X t+1 }) is transferred from the time series analysis  110  stage to the feature evolving  120  stage. In this stage, the reliable feature point information ({{tilde over (Y)} t+1 }, {{tilde over (X)} t+1 }) to be remained is screened by the evolving operation on the feature points (Step  240 ), which mainly includes two parts of screening given below in detail. 
         [0041]    The first part is generating a new feature point, which includes the following steps. 
         [0042]    (a) The new feature point is found out according to the method of corresponding the feature points between {Y t } and {Y t+1 }, and added into {{tilde over (Y)} t+1 }. 
         [0043]    (b) A weight 
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         [0000]    is set for initializing the three-dimensional state collection of the collection {{tilde over (X)} t+1 }, wherein the weight is determined by the state value of the neighboring feature points. The weight value is represented by the existing time of neighboring feature points in the whole image sequence  10 , or represented by the distance from the neighboring feature points. 
         [0044]    The definition of the neighboring feature points is represented by the following expression: 
         [0000]        X′   t+1   ={x   t+1   εX   t+1   |∥y   t+1   −{tilde over (y)}   t+1 ∥&lt;η}; 
         [0045]    And, the expression of the weight 
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         [0046]    Therefore, the three-dimensional state of each x,+, after being initialized can be further represented by the following expression: 
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         [0047]    After the process of generating the new feature point, the feature point information is shown by the hollow circular pattern connected by dashed line in  FIG. 4C , which includes the newly added feature point group {{tilde over (Y)} t+1 }  320  at the time t+1 and the newly added three-dimensional state collection {{tilde over (X)} t+1 }  420  at the time t+1. 
         [0048]    The second part is deleting the feature point, which includes the following steps. 
         [0049]    (c) The feature point generating an error larger than the threshold during the feature matching process is deleted from the existing feature point collection {{tilde over (Y)} t+1 }. This part may generate errors in the feature matching process when the feature point is found out by the feature matching method, thus the feature points with large errors must be deleted. 
         [0050]    (c) The feature point generating an error larger than the threshold during the feature corresponding is deleted from the newly generated feature point collection {{tilde over (Y)} t+1 }. This part is the same as the former process, which is used to delete the feature points generated by the feature matching error. 
         [0051]    (c) and (d) mainly define P t (         ) as a rectangular region taking           as the center at the time t, and thus at the time t+1, the feature matching error E t+1 (y t , y t+1 ) of each feature point y t+1  in the existing feature point collection {Y t+1 } is defined as the following expression: 
         [0000]        E   t+1 ( y   t   , y   t+1 )=∥ P   t ( y   t )− P   t+1 ( y   t+1 )∥; 
         [0052]    and the feature corresponding error E t+1 ({tilde over (y)} t , {tilde over (y)} t+1 ) of each feature point {tilde over (y)} t+1 , in the newly generated feature point collection {{tilde over (Y)} t+1 } is defined as the following expression: 
         [0000]        E   t+1 ( {tilde over (y)}   t+1   , {tilde over (y)}   t )=∥ P   t+1 ( {tilde over (y)}   t+1 )− P   t ( {tilde over (y)}   t )∥; 
         [0053]    and when E t+1 (y t , y t+1 ) and E t+1 ({tilde over (y)} t+1 , {tilde over (y)} t ) are respectively larger than the preset threshold, the feature points y t+1  and {tilde over (y)} t+1  are deleted. 
         [0054]    (e) The feature point with an error calculated by the system model analysis during the prediction of {X t+1 } larger than the threshold is to be deleted. This part is mainly directed to delete the feature point with large error when the three-dimensional state is transferred through the Kalman Filter time series analysis model. 
         [0055]    The error is defined as the difference between the state {circumflex over (X)} t+1  (obtained by correcting each x t+1  in the collection {X t+1 } through the Kalman Filter time series analysis model) multiplying H t+1  and each y 1  in the feature point collection {Y t }, which can be represented as 
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       Likewise, when the error exceeds the preset threshold, the corresponding feature point is deleted. 
       [0056]    After the feature point is deleted, the deleted feature point information is represented by connecting the part marked with “X” by dashed lines, and the survival feature point includes the feature point group {{tilde over (Y)} t+1 }  330  after the deletion at the time t+1 and the three-dimensional state collection {{tilde over (X)} t+1 }  430  after the deletion at the time t+. 
         [0057]    After the Step  240 , the generated feature point information ({{tilde over (Y)} t+1 }, {{tilde over (X)} t+1 }) is the so-called reliable feature point information, and the feature point groups in the feature point information all have a strong corresponding relationship. At this point, whether other images in the image sequence  10  still need to be processed or not is determined (Step  250 ). If yes, it is necessary to return to the Step  220  to execute in a recursion manner, however, before that, the step  260  of transferring the newly obtained reliable feature point information back to the stage of time series analysis  110  for being updated must be performed. That is, during the state updating process  113 , {Y t+1 }={Y t+1 }+{{tilde over (Y)} t+1 } and {X t+1 }={X t+1 }+{{tilde over (X)} t+1 } are taken as the state ({Y t+1 },{X t+1 }) of the reliable feature point information  50  when a next image frame in the image sequence  10  is processed. 
         [0058]    Now the reliable feature point information  50  ({Y t+1 },{X t+1 }) is the triangular pattern connected by dashed lines as in  FIG. 4E , which includes the updated feature point group {Y t+1 }  340  at the time t+1 and updated three-dimensional state collection {X t+1 }  440  at the time t+1. 
         [0059]    On the contrary, if all the image frames in the image sequence  10  are processed, the finally remained reliable feature point information  50  (i.e., the so-called three-dimensional feature point information) is output to the state updating process  113  (Step  270 ), and the {Y t+1 }={Y t+1 }+{{tilde over (Y)} t+1 } and {X t+1 }={X t+1 }+{{tilde over (X)} t+1 } are taken as the final three-dimensional scene information of the whole image sequence  10 . 
         [0060]    As mentioned before, the input object is a group of space points having three-dimensional track or motion on the time axis. Since the space point itself has the content of the three-dimensional feature point information, when the above-mentioned state initializing process  111  executes the first situation of the initializing process, the process of generating three-dimensional state collection (i.e., the part of Step  210 ) executed when the first space point is input can be omitted. 
         [0061]    The reconstruction result of-the three-dimensional scene will become more accurate by means of the operation of the present invention-as demonstrated in the comparison between  FIG. 5A  and  FIG. 5B .  FIG. 5A  is a three-dimensional scene established according to the feature point information generated by the conventional methods, and  FIG. 5B  is a three-dimensional scene reconstructed according to the feature point information generated by the technique of the present invention. It can be known from the marks ( 500  and  510 ) made in  FIGS. 5A and 5B , the three-dimensional scene  510  generated according to the present invention has a much higher accuracy than the three-dimensional scene  500  reconstructed according to the conventional art. It is noted that at the position of the marks, the conventional methods tend to generate the feature point corresponding error resulting in the mistake of the three-dimensional scene information, so that an abnormal mapping phenomenon (e.g., the concavo-convex part of the mark  500 ) is generated on the texture mapping model. Since the present invention uses the time series analysis  110  to process and analyze the feature point information, the feature point information having a strong corresponding relationship is established by the present invention through initializing, predicting and correcting the feature point information. In addition, the present invention screens the feature point information continuously through the feature evolving  120 , such that the finally obtained feature point information has a stronger corresponding relationship, and the generated three-dimensional scene has a higher accuracy than before. 
         [0062]    With the descriptions of invention, it will be obvious that the same may be varied in many ways. Such variations are not to be regarded as a departure from the principle and scope of the invention, and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the following claims.