329 results about "Motion field" patented technology

PCT No. PCT / EP96 / 01272 Sec. 371 Date Nov. 21, 1997 Sec. 102(e) Date Nov. 21, 1997 PCT Filed Mar. 22, 1996 PCT Pub. No. WO96 / 29679 PCT Pub. Date Sep. 26, 1996The present invention concerns improved motion estimation in signal records. A method for estimating motion between one reference image and each frame in a sequence of frames, each frame consisting of a plurality of samples of an input signal comprises the steps of: transforming the estimated motion fields into a motion matrix, wherein each row corresponds to one frame, and each row contains each component of motion vector for each element of the reference image; performing a Principal Component Analysis of the motion matrix, thereby obtaining a motion score matrix consisting of a plurality of column vectors called motion score vectors and a motion loading matrix consisting of a plurality of row vectors called motion loading vectors, such that each motion score vector corresponds to one element for each frame, such that each element of each motion loading vector corresponds to one element of the reference image, such that one column of said motion score matrix and one motion loading vector together constitute a factor, and such that the number of factors is lower than or equal to the number of said frames; wherein the results from the Principal Component Analysis on the motion matrix are used to influence further estimation of motion from the reference image to one or more of the frames.

Described is a robotic system for detecting obstacles reliably with their ranges by a combination of two-dimensional and three-dimensional sensing. In operation, the system receives an image from a monocular video and range depth data from a range sensor of a scene proximate a mobile platform. The image is segmented into multiple object regions of interest and time-to-contact (TTC) value are calculated by estimating motion field and operating on image intensities. A two-dimensional (2D) TTC map is then generated by estimating average TTC values over the multiple object regions of interest. A three-dimensional TTC map is then generated by fusing the range depth data with image. Finally, a range-fused TTC map is generated by averaging the 2D TTC map and the 3D TTC map.

The invention provides a facial expression recognition method and a system, and a training method and a system of a facial expression classifier. The facial expression recognition method specifically comprises the following steps: carrying out feature extraction on an optical flow motion field fusing the sequences of a binary image and a gray scale image and obtaining a feature sequence; and taking the inputted feature sequence as an observation sequence and judging the expression category of the observation sequence according to the facial expression classifier, wherein the facial expression classifier is obtained by combining hidden markov models of a variety of expressions, and the hidden markov models are obtained by training neutral expression sequences and weak expression sequences in facial expression samples. The invention is used for overcoming the shortcomings of discrete results, jumping and unnatural property of the existing expression recognition.

Techniques are disclosed for detecting foreground objects in a scene captured by a surveillance system and tracking the detected foreground objects from frame to frame in real time. A motion flow field is used to validate foreground objects(s) that are extracted from the background model of a scene. Spurious foreground objects are filtered before the detected foreground objects are provided to the tracking stage. The motion flow field is also used by the tracking stage to improve the performance of the tracking as needed for real time surveillance applications.

A method and system for extracting coronary vessels fluoroscopic image sequences using coronary digital subtraction angiography (DSA) are disclosed. A set of mask images of a coronary region is received, and a sequence of contrast images for the coronary region is received. For each contrast image, vessel regions are detected in the contrast image using learning-based vessel segment detection and a background region of the contrast image is determined based on the detected vessel regions. Background motion is estimated between one of the mask images and the background region of the contrast image by estimating a motion field between the mask image and the background image and performing covariance-based filtering over the estimated motion field. The mask image is then warped based on the estimated background motion to generate an estimated background layer. The estimated background layer is subtracted from the contrast image to extract a coronary vessel layer for the contrast image.

Techniques are disclosed for detecting foreground objects in a scene captured by a surveillance system and tracking the detected foreground objects from frame to frame in real time. A motion flow field is used to validate foreground objects(s) that are extracted from the background model of a scene. Spurious foreground objects are filtered before the foreground objects are provided to the tracking stage. The motion flow field is also used by the tracking stage to improve the performance of the tracking as needed for real time surveillance applications.

A method for video filtering of an input video sequence by utilizing joint motion and noise estimation includes the steps of: (a) generating a motion-compensated video sequence from the input video sequence and a plurality of estimated motion fields; (b) spatiotemporally filtering the motion compensated video sequence, thereby producing a filtered, motion-compensated video sequence; (c) estimating a standard deviation from the difference between the input video sequence and the filtered, motion-compensated video sequence, thereby producing an estimated standard deviation; (d) estimating a scale factor from the difference between the input video sequence and the motion compensated video sequence; and (e) iterating through steps (a) to (d) using the scale factor previously obtained from step (d) to generate the motion-compensated video sequence in step (a) and using the estimated standard deviation previously obtained from step (c) to perform the filtering in step (b) until the value of the noise level approaches the unknown noise of the input video sequence, whereby the noise level is then characterized by a finally determined scale factor and standard deviation.

Techniques are disclosed for detecting foreground objects in a scene captured by a surveillance system and tracking the detected foreground objects from frame to frame in real time. A motion flow field is used to validate foreground objects(s) that are extracted from the background model of a scene. Spurious foreground objects are filtered before the foreground objects are provided to the tracking stage. The motion flow field is also used by the tracking stage to improve the performance of the tracking as needed for real time surveillance applications.

Methods and apparatus are provided for maintaining a background image model in a background subtraction system using accumulated motion. A background image model is maintained by obtaining a map of accumulated motion; and adjusting the background image model based on the map of accumulated motion. The map of accumulated motion may be obtained, for example, based on one or more of motion field images; stability maps; frame differences; or information from a background subtraction system. Objects can be added to or removed from the background model or the background model can be otherwise updated One or more pixels from an image are added to the background image model if a stability measure for the one or more pixels satisfies a predefined criteria. A portion of the background image model can be invalidated in regions where the map of accumulated motion exceeds a predefined threshold.

Methods for coherent block-based motion estimation for motion-compensated frame rate conversion of decoded video sequences are provided. In some of the disclosed methods, motion vectors are estimated for each block in a decoded frame in both raster scan order and reverse raster scan order using prediction vectors from selected spatially and temporally neighboring blocks. Further, in some of the disclosed methods, a spatial coherence constraint that detects and removes motion vector crossings is applied to the motion vectors estimated for each block in a frame to reduce halo artifacts in the up-converted video sequence. In addition, in some of the disclosed methods, post processing is performed on estimated motion vectors to improve the coherence of the motion vectors. This post-processing includes application of vector median filters to the estimated motion vectors for a frame and / or application of a sub-block motion refinement to increase the density of the motion field.

The invention provides a PET image acquiring method and system. The method comprises registering a first PET image and a corrected second PET image to acquire a motion field of an image space between the corrected first PET image and a second PET image; harmonizing a first CT image using the motion field to acquire a second CT image of a scanned object; carrying out attenuation correction on second PET data using the second CT image to acquire the corrected second PET iamge. According to the PET image acquiring method and system, the corrected second PET image can be acquired through carrying out attenuation correction on second PET data using the second CT image, a process for acquiring the second CT image through secondary CT scanning can be omitted, time of X-ray radiation during CT scanning can be shortened, and a better clinic experience can be achieved.