Shutterfly, Inc.

Yahoo! Inc.

Shutterffy, LLC

Ravin Ai Ltd.

Roman Sandler

Alexander M Kenis

Yaron Stein

Yehonatan Yehudai

Ronny Lempel

Edward Bortnikov

Dmitri Perelman

Alexander Serebryiani

Alexander Kenis

Eliron Ekstein

A photo product design customization system for customizing a photo product design with minimal and intuitive user inputs includes a product design module that displays a photo product design in a main design panel, a user input detection unit that can detect a first directional movement from a first dynamic object in a product type selection panel to the main design panel, or a second directional movement from a second dynamic object in a product style selection panel to the main design panel, or a third directional movement from a third dynamic object in a product style selection panel to the main design panel, and an intelligent product design creation engine that can automatically change the photo product design in the main design panel in response to detection of the first, the second, or the third directional movement.

System and method for customizing photo product designs with minimal and intuitive user inputs

An automatic system for detecting a damage to a vehicle, comprising: (a) at least one camera for capturing 'handover' and “return” images irrespective of the vehicle's movement or orientation relative to the camera; (b) a memory for storing images of basic-parts (c) a first determination unit for, based on the basic-parts images, determine the location of one or more basic-parts within the “handover” and “return” images; (d) a second determination unit configured to, based on the determined locations of the basic parts within the images, determine the locations of “other parts” within the “handover” and “return” images, thereby to form “handover” and “return” part-images, respectively; (e) a transformation unit configured to separately transform each pair of “handover” and “return” part-images, respectively, to a same plane; (f) a comparison unit configured to separately compare pixel-by-pixel each transformed pairs of “handover” and “return” part-images, to detect a difference above a predefined threshold.

System and method for automatically detecting damages in vehicles

A computer-implemented method of grouping faces in large user account for creating an image product includes adding the face images obtained from an image album in a user's account into a first chunk; if the chunk size of the first chuck is smaller than a maximum chuck value, keeping the face images from the image album into the first chunk; otherwise, automatically separating the face images from the image album into a first portion and one or more second portions; keeping the first portion in the first chunk; automatically moving the second portions to subsequent chunks; automatically grouping face images in the first chunk to form face groups; assigning the face groups to known face models associated with the user account; and creating a design for an image-based product based on the face images in the first chunk associated with the face models.

Automatic image product creation for user accounts comprising large number of images

A computer-implemented method for sorting face images of different individuals into different groups includes obtaining face images comprising faces of unknown individuals by a computer processor; calculating similarity functions between pairs of face images by the computer processor; joining face images that have values of the similarity functions above a predetermined threshold into a hypothetical face group, wherein the face images in the hypothetical face group hypothetically belong to a same person; conducting non-negative matrix factorization on values of the similarity functions in the hypothetical face group to test truthfulness of the hypothetical face group; and identifying the hypothetical face group as a true face group if a percentage of the associated similarity functions being true is above a threshold based on the non-negative matrix factorization.

Grouping face images using statistic distribution estimate

A computer-implemented method for creating an image product by accurately grouping faces includes receiving an initial set of face groups for a plurality of face images, training classifiers between pairs of face groups in the initial set of face groups using image-product statistics by a computer processor, classifying the plurality of face images by classifiers to output binary vectors for the plurality of face images by the computer processor, calculating a value for an improved similarity function using the binary vectors for each pair of the plurality of face images, grouping the plurality of face images into modified face groups based on values of the binary similarity functions by the computer processor, and creating an image product based at least in part on the modified face groups.

Image product creation based on face images grouped using image product statistics

Face-containing images within web pages are automatically annotated to identify the people having those faces. The annotation is based on faces detected in the images and named entities detected in text associated with the images. Each candidate named entity may be scored by the prominence of the named entity in the text relative to the other extracted named entities. Queries are sent to a search engine based on the extracted candidate named entities. Sample images are returned. Face similarity calculations may be computed based on the featured faces and sample faces detected in the search engine-returned sample images to associate a probability score between each featured face and each candidate named entity. A bipartite matching instance may be solved to arrive at a maximum likelihood assignment of named entities to featured faces.

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Haifa, Israel

Roman Sandler