International Business Machines Corporation

Amazon Technologies, Inc.

Sharon Alpert

Pavel Kisilev

Ido Yerushalmy

Antonio Criminisi

Noam Sorek

Ianir Ideses

Eduard Oks

Dotan Kaufman

Eli Alshan

Mark Kliger

Liza Potikha

Miri Erihov

Mattias Marder

Alexander Lorbert

Lior Fritz

Guy Adam

Arik Poznanski

Matan Goldman

Imry Kissos

Omer Meir

Eran Borenstein

Bar Hilleli

Assaf Neuberger

Ridge Carpenter

Michael Chertok

Georgy Melamed

Lamarr Smith

Elizabeth Reisman

Claire Mcgowan

Characteristics of a user's movement are evaluated based on performance of activities by a user within a field of view of a camera. Video data representing performance of a series of movements by the user is acquired by the camera. Pose data is determined based on the video data, the pose data representing positions of the user's body while performing the movements. The pose data is compared to a set of existing videos that correspond to known errors to identify errors performed by the user. The errors may be used to generate scores for various characteristics of the user's movement. Based on the errors, exercises or other activities to improve the movement of the user may be determined and included in an output presented to the user.

System for correcting user movement

Systems for preparing a medical report, and related methods, generate and output feedback indicative or potential errors in the medical report. A system for preparing a medical report is configured to receive image data for a medical image of a patient, display the medical image, monitor content of the medical report during preparation of the medical report, process the image data to detect one or more organs of the patient imaged in the medical image, compare the content of the medical report with the one or more organs imaged in the medical image to detect one or more potential errors in the medical report, and output feedback indicative of the one or more potential errors in the medical report.

Methods and systems for detection of errors in medical reports

A first computing device acquires video data representing a user performing an activity. The first device uses a first pose extraction algorithm to determine a pose of the user within a frame of video data. If the pose is determined to be potentially inaccurate, the user is prompted for authorization to send the frame of video data to a second computing device. If authorization is granted, the second computing device may use a different algorithm to determine a pose of the user and send data indicative of this pose to the first computing device to enable the first computing device to update a score or other output. The second computing device may also use the frame of video data as training data to retrain or modify the first pose extraction algorithm, and may send the modified algorithm to the first computing device for future use.

Systems for improving pose determination based on video data

Systems and techniques for producing image-based radiology reports including contextual cropping of image data and radiologist supplied notes and annotations are provided herein. Computer vision and natural language processing algorithms may enable processing of image data and language inputs to identify objects associated with annotations, aid in cropping the image data according to the annotations and object identification and in producing a final text and image laden report.

Contextual image cropping and report generation

Video output is generated based on first video data that depicts the user performing an activity. Poses of the user during performance of the activity are compared with second video data that depicts an instructor performing the activity. Corresponding poses of the user's body and the instructor's body may be determined through comparison of the first and second video data. The video data is used to determine the rate of motion of the user and to generate video output in which a visual representation of the instructor moves at a rate similar to the that of the user. For example, video output generated based on an instructional fitness video may be synchronized so that movement of the presented instructor matches the rate of movement of the user performing an exercise, improving user comprehension and performance.

System for synchronizing video output based on detected behavior

First image data representing a first human wearing a first article of clothing may be received. The first image data, when rendered on a display, may include a first photometric artifact. A first generator network may be used to generate second image data from the first image data. The first photometric artifact may be removed from the second image data. A second generator network may be used to generate third image data from the second image data, the third image data representing the first human in a different pose relative to the first image data. Fourth image data representing the first article of clothing segmented from the first human may be generated and displayed on a display.

Catalog normalization and segmentation for fashion images

Asymmetries are detected in one or more images by partitioning each image to create a set of patches. Salient patches are identified, and an independent displacement for each patch is identified. The techniques used to identify the salient patches and the displacement for each patch are combined in a function to generate a score for each patch. The scores can be used to identify possible asymmetries.

Unsupervised asymmetry detection

A method comprising using at least one hardware processor for computing a patch distinctiveness score for each of multiple patches of a medical image, computing a shape distinctiveness score for each of multiple regions of the medical image, and computing a saliency map of the medical image, by combining the patch distinctiveness score and the shape distinctiveness score.

Anomaly detection in medical imagery

A method, executed by one or more processors, includes computing a saliency surface for an image, conducting a mean shift algorithm on the saliency surface to provide mean shift data for the saliency surface, producing a mode voting map for the saliency surface from the mean shift data, and classifying features in the image according to the mode voting map. The features may correspond to medical conditions. In some embodiments, computing the saliency surface comprises determining a distinctiveness score for each of a plurality of image patches. In some embodiments, producing the mode voting map comprises determining a plurality of modes and an area of influence for each mode of the plurality of modes where the area of influence corresponds to mean shift data that leads to a particular mode. A corresponding computer program product and computer system are also disclosed herein.

Image feature classification

A method, system and product for image classification. The method comprising obtaining a set of encoding functions and signature values which corresponds to a set of class images, wherein each pair of an encoding function and a signature value corresponds to a class image of the set of class images, wherein the signature value is a value produced by applying the encoding function on the class image; obtaining an image to be classified to a class associated with a class image of the set of class images; with respect to each class image of the set of class images: determining a transformation from the image to the class image; and applying the encoding function using the transformation on the image to produce a value; and automatically determining a class to which the image is classified based on the values and the signature values.

Automatic image classification

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Rehovot, Israel

Sharon Alpert