Abstract: A technique and system for counting the number of repetitions of approximately the same action in an input video sequence using 3D convolutional neural networks is disclosed. The proposed system runs online and not on the complete video. It analyzes sequentially blocks of 20 non-consecutive frames. The cycle length within each block is evaluated using a deep network architecture and the information is then integrated over time. A unique property of the disclosed method is that it is shown to successfully train on entirely synthetic data, created by synthesizing moving random patches. It therefore effectively exploits the high generalization capability of deep neural networks. Coupled with a region of interest detection mechanism and a suitable mechanism to identify the time scale of the video, the system is robust enough to handle real world videos collected from YouTube and elsewhere, as well as non-video signals such as sensor data revealing repetitious physical movement.

Abstract: A method of comparing sequences, comprises: inputting a first set of sequences and a second set of sequences; applying an encoder to each set to encode the set into a collection of vectors, each representing one sequence of the set; constructing a grid representation having a plurality of grid-elements, each comprises a vector pair composed of one vector from each of the collections; and feeding the grid representation into a convolutional neural network (CNN), constructed to simultaneously process all vector pairs of the grid representation, and to provide a grid output having a plurality of grid-elements, each defining a similarity level between vectors in one grid-element of the grid representation.

Abstract: A convolutional neural network system for detecting at least one object in at least one image. The system includes a plurality of object detectors, corresponding to a predetermined image window size in the at least one image. Each object detector is associated with a respective down-sampling ratio with respect to the at least one image. Each object detector includes a respective convolutional neural network and an object classifier coupled with the convolutional neural network. The respective convolutional neural network includes a plurality of convolution layers. The object classifier classifies objects in the image according to the results from the convolutional neural network. Object detectors associated with the same respective down-sampling ratio define at least one group of object detectors. Object detectors in a group of object detectors being associated with common convolution layers.

Abstract: A method of recognizing image content, comprises applying to the image a neural network which comprises an input layer for receiving the image, a plurality of hidden layers for processing the image, and an output layer for generating output pertaining to an estimated image content based on outputs of the hidden layers. The method further comprises applying to an output of at least one of the hidden layers a neural network branch, which is independent of the neural network and which has an output layer for generating output pertaining to an estimated error level of the estimate. A combined output indicative of the estimated image content and the estimated error level is generated.

Abstract: In a method of converting an input image patch to a text output, a convolutional neural network (CNN) is applied to the input image patch to estimate an n-gram frequency profile of the input image patch. A computer-readable database containing a lexicon of textual entries and associated n-gram frequency profiles is accessed and searched for an entry matching the estimated frequency profile. A text output is generated responsively to the matched entries.

Abstract: A convolutional neural network system for detecting at least one object in at least one image. The system includes a plurality of object detectors, corresponding to a predetermined image window size in the at least one image. Each object detector is associated with a respective down-sampling ratio with respect to the at least one image. Each object detector includes a respective convolutional neural network and an object classifier coupled with the convolutional neural network. The respective convolutional neural network includes a plurality of convolution layers. The object classifier classifies objects in the image according to the results from the convolutional neural network. Object detectors associated with the same respective down-sampling ratio define at least one group of object detectors. Object detectors in a group of object detectors being associated with common convolution layers.

Abstract: A convolutional neural network system for detecting at least one object in at least one image. The system includes a plurality of object detectors, corresponding to a predetermined image window size in the at least one image. Each object detector is associated with a respective down-sampling ratio with respect to the at least one image. Each object detector includes a respective convolutional neural network and an object classifier coupled with the convolutional neural network. The respective convolutional neural network includes a plurality of convolution layers. The object classifier classifies objects in the image according to the results from the convolutional neural network. Object detectors associated with the same respective down-sampling ratio define at least one group of object detectors. Object detectors in a group of object detectors being associated with common convolution layers.

Abstract: A robot having at least one member having at least one controllably actuable articulation, said articulation having at least one torque sensor for providing a torque signal indicative of a torque applied to the articulation, and one angle sensor for providing an angle signal indicative of an angle of actuation of the articulation; the robot further comprising: a controller for controlling said at least one controllably actuable articulation; a first neural network arranged for receiving the torque and angle signals and arranged for providing to the controller a force signal indicating that an external force is applied to said at least one member: a second neural network arranged for receiving the torque and angle signals and arranged for providing to the controller a direction signal indicating the direction along which said external force is applied to said at least one member.

Abstract: A technique and system for counting the number of repetitions of approximately the same action in an input video sequence using 3D convolutional neural networks is disclosed. The proposed system runs online and not on the complete video. It analyzes sequentially blocks of 20 non-consecutive frames. The cycle length within each block is evaluated using a deep network architecture and the information is then integrated over time. A unique property of the disclosed method is that it is shown to successfully train on entirely synthetic data, created by synthesizing moving random patches. It therefore effectively exploits the high generalization capability of deep neural networks. Coupled with a region of interest detection mechanism and a suitable mechanism to identify the time scale of the video, the system is robust enough to handle real world videos collected from YouTube and elsewhere, as well as non-video signals such as sensor data revealing repetitious physical movement.

Abstract: In a method of analysis, a target image is registered to define a plurality of keypoints arranged in sets corresponding to polygons or linear segments in the target image. A database of registered and annotated images is accessed and a polygon-wise comparison between the target image and each database image is employed. The comparison is used for projecting annotated locations from the database images into the target image.

Abstract: A method of correcting gaze offset in an image of at least one individual having eyes is disclosed. The method comprises: processing the image to extract location of at least one eye over the image, processing the image to replace imagery data associated with each location of each eye with replacement data thereby providing a corrected image, and transmitting the corrected image to a display device. The replacement data are preferably previously-recorded imagery data which respectively correspond to the same eye but a different gaze.

Abstract: In a method of analysis, a target image is registered to define a plurality of keypoints arranged in sets corresponding to polygons or linear segments in the target image. A database of registered and annotated images is accessed and a polygon-wise comparison between the target image and each database image is employed. The comparison is used for projecting annotated locations from the database images into the target image.

Abstract: A method of correcting gaze offset in an image of at least one individual having eyes is disclosed. The method comprises: processing the image to extract location of at least one eye over the image, processing the image to replace imagery data associated with each location of each eye with replacement data thereby providing a corrected image, and transmitting the corrected image to a display device. The replacement data are preferably previously-recorded imagery data which respectively correspond to the same eye but a different gaze.

Abstract: A system, computer readable medium and a method for motion detection, the method includes: receiving multiple frames; generating a set of digits for each pixel of multiple pixels of each frame of the multiple frames; wherein each set of digits represents a pixel that belongs to a patch of a frame and represents relationships between (a) first similarities between the patch and a set of patches of a next frame that are located in locations that differ from each other and differ from a location of the patch; and (b) second similarities between the patch and a set of patches of a previous frame that are located in locations that differ from each other and differ from a location of the patch; and processing the sets of digits to detect motion.

Abstract: In a method of analysis, a target image is registered to define a plurality of keypoints arranged in sets corresponding to polygons or linear segments in the target image. A database of registered and annotated images is accessed and a polygon-wise comparison between the target image and each database image is employed. The comparison is used for projecting annotated locations from the database images into the target image.

Abstract: A method of correcting gaze offset in an image of at least one individual having eyes is disclosed. The method comprises: processing the image to extract location of at least one eye over the image, processing the image to replace imagery data associated with each location of each eye with replacement data thereby providing a corrected image, and transmitting the corrected image to a display device. The replacement data are preferably previously-recorded imagery data which respectively correspond to the same eye but a different gaze.

Abstract: A method and system for statistical mapping between genetic information and facial image data including collecting a multiplicity of sets of genetic information and matching facial image data representing a multiplicity of individuals, representing the genetic information of each of the multiplicity of individuals as a first multidimensional representation, representing the facial image data of each of the multiplicity of individuals as a second multidimensional representation; and inferring correlative, non-causal, statistical relationships between the first multidimensional representations and the second multidimensional representations.

Abstract: A method and system for statistical mapping between genetic information and facial image data including collecting a multiplicity of sets of genetic information and matching facial image data representing a multiplicity of individuals, representing the genetic information of each of the multiplicity of individuals as a first multidimensional representation, representing the facial image data of each of the multiplicity of individuals as a second multidimensional representation; and inferring correlative, non-causal, statistical relationships between the first multidimensional representations and the second multidimensional representations.

Abstract: A method for providing an output image, the method includes: determining an importance value for each input pixels out of multiple input pixels of an input image; applying on each of the multiple input pixels a conversion process that is responsive to the importance value of the input pixel to provide multiple output pixels that form the output image; wherein the input image differs from the output image.

Abstract: A method for providing an output image, the method includes: determining an importance value for each input pixels out of multiple input pixels of an input image; applying on each of the multiple input pixels a conversion process that is responsive to the importance value of the input pixel to provide multiple output pixels that form the output image; wherein the input image differs from the output image.