Yeda Research and Develpment & Co. Ltd

Saferide Technologies Ltd.

Alexander Apartsin

Ronen Ezra Basri

Chen Brestel

Meirav Galun

Yuval Shalev

Stanislav Barabanov

Sofiia Kovalets

A model configuration selection system, the model configuration selection system comprising a processing circuitry configured to: (A) obtain: (a) one or more model configurations, each model configuration includes a set of parameters utilized to generate respective models, and (b) a training data-set comprising a plurality of unlabeled records, each unlabeled record including a collection of features describing a given state of a physical entity; (B) cluster the training data-set into two or more training data-set clusters using a clustering algorithm; (C) label (a) the unlabeled records of a subset of the training data-set clusters with a synthetic normal label, giving rise to a normal training data-set, and (b) the unlabeled records of the training data-set clusters not included in the subset with a synthetic abnormal label; (D) train, for each model configuration, using the normal training data-set, a corresponding model utilizing the corresponding set of parameters, each model capable of receiving the unlabeled records, and determining, for each of the unlabeled records, a corresponding normal label or abnormal label, wherein the normal label being indicative of conformity of the respective unlabeled record with an allowed state of the physical entity and the abnormal label being indicative of conformity of the respective unlabeled record with a disallowed state of the physical entity; (E) determine, for each model, a score, associated with an ability of the corresponding model to determine labels to the unlabeled records of the training data-set in accordance with the synthetic normal labels and with the synthetic abnormal labels; and (F) perform an action, based on the scores.

System and method for model configuration selection preliminary class

A method and apparatus for finding correspondence between portions of two images that first subjects the two images to segmentation by weighted aggregation (), then constructs directed acylic graphs () from the output of the segmentation by weighted aggregation to obtain hierarchical graphs of aggregates (), and finally applies a maximally weighted subgraph isomorphism to the hierarchical graphs of aggregates to find matches between them (). Two algorithms are described; one seeks a one-to-one matching between regions, and the other computes a soft matching, in which is an aggregate may have more than one corresponding aggregate. A method and apparatus for image segmentation based on motion cues. Motion provides a strong cue for segmentation. The method begins with local, ambiguous optical flow measurements. It uses a process of aggregation to resolve the ambiguities and reach reliable estimates of the motion. In addition, as the process of aggregation proceeds and larger aggregates are identified, it employs a progressively more complex model to describe the motion. In particular, the method proceeds by recovering translational motion at fine levels, through affine transformation at intermediate levels, to 3D motion (described by a fundamental matrix) at the coarsest levels. Finally, the method is integrated with a segmentation method that uses intensity cues. The utility of the method is demonstrated on both random dot and real motion sequences.

Method and apparatus for matching portions of input images

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Rehovot, Israel

Alexander Apartsin