Samsung Electronics Co., Ltd.

Orthopedic Navigation Ltd.

Ishay Goldin

Ran Vitek

Nathan Henri Levy

Shai Litvak

Yoel Yaffe

Dan Rappaport

Omer Shabtai

Amit Eisenberg

Ram Nathaniel

Ariel Orfaig

Michael Dinerstein

Gershi Koltun

Alexandra Dana

Roy Josef Jevnisek

Yotam Platner

Netanel Stein

Noga Levy

Tal Heller

Yonatan Dinai

Alon Intrater

Yaron Ukrainitz

David Tsidkiahu

Zuf Pilosof

Gil Shapira

One or more aspects of the present disclosure enable high accuracy computer vision and image processing techniques with decreased system resource requirements (e.g., with decreased computational load, shallower neural network designs, etc.). As described in more detail herein, one or more aspects of the described techniques may leverage key layers (e.g., certain key layers of a neural network) and compressed tensor comparisons to efficiently exploit temporal redundancy in videos and other slow changing signals (e.g., to efficiently reduce neural network inference computational burden, with only minor increase in data transfer power consumption). For example, key layers of a neural network may be identified, and temporal/spatial redundancy across frames may be efficiently leveraged such that only a computation region in a subsequent frame n+1 is re-computed in layers between identified key layers, while remaining feature-map calculations may be disabled in the layers between the identified key layers.

Low-power change-based neural network inference for image processing

Techniques and apparatuses enabling high accuracy video object detection using reduced system resource requirements (e.g., reduced computational load, shallower neural network designs, etc.) are described. For example, a search domain of an object detection scheme (e.g., a target object class, a target object size, a target object rotation angle, etc.) may be separated into subdomains (e.g., such as subdomains of object classes, subdomains of object sizes, subdomains object rotation angles, etc.). Specialized, subdomain-level object detection/segmentation tasks may then be separated across sequential video frames. As such, different subdomain-level processing techniques (e.g., via specialized neural networks) may be implemented across different frames of a video sequence. Moreover, redundancy information of consecutive video frames may be leveraged, such that specialized object detection tasks combined with visual object tracking across consecutive frames may enable more efficient (e.g., more accurate, less computationally intensive, etc.) full domain object detection and object segmentation schemes.

Fast object detection in video via scale separation

A method for processing a digital content includes acquiring content data using a sensor. Compressed reference data is generated from the acquired content data. A hash of the compressed reference data is generated using a hashing function. The generated hash is signed using an encryption function. The acquired content data is transmitted along with the compressed reference data and the signed hash.

Robust selective image, video, and audio content authentication

Embodiments of the present disclosure enable and accurate detection of facial landmarks on mobile devices in real-time. An architecture of a facial landmark detection model is provided including one or more of an attention mechanism (e.g., an attention network), a graph convolution model (e.g., a two-dimensional facial geometry graph convolution model), a multiscale coarse-to-fine mechanism, a patch-facial landmark detachment mechanism, and error estimation techniques. The attention mechanism may increase the accuracy of the facial landmark detection model by attending to meaningful patches. The graph convolution network may improve patch feature aggregation by considering the facial landmarks' geometry. The coarse-to-fine mechanism reduces a network convergence to two cycles (e.g., two facial landmark detection iterations). A patch-facial landmark detachment mechanism reduces the computation burden without significant accuracy degradation. Error estimation techniques provide accurate estimation of the regression error to the computation load and increase the accuracy of the model.

Real-time facial landmark detection

Disclosed is a method for generating a panoramic image of a region of interest (ROI) which is larger than a field of a view of a radiation based imaging device, comprising, positioning markers along the ROI, acquiring a set of images along the ROI, wherein the acquired images have at least partially overlapping portions, aligning at least two separate images by aligning a common marker found in both images and compensating for a difference between a distance from a radiation source to the marker element and the distance from the radiation source to a plane of interest.

Method and system for stitching multiple images into a panoramic image

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Tel Aviv, Israel

Ishay Goldin