Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A merchant system offers various products for sale to users, with prices for different products received from a pricing server. The merchant system provides information identifying a user to a pricing server, which determines user-specific prices for the products offered by the merchant system. Prior purchases of products by various users via the merchant system and user-specific information maintained by the pricing server for the user corresponding to the user identifying information are used to generate the user-specific prices. The pricing server selects the user-specific price for a product as a candidate price having a maximum expected profit to the merchant system. Subsequently, the pricing server transmits to the merchant system a listing of products offered by the merchant system and their user-specific prices ordered by their expected profit to the merchant system. The merchant system displays the products to the user in the order received from the pricing server.

Abstract: Devices, systems, and methods herein relate to processing biosignal data. These systems and methods may obtain sensor data from a plurality of electrodes and may also be used to augment cortical function, treat neurological disease, and provide insight and analysis of biological processes and/or clinical therapeutic outcomes. An implantable biosignal processing system may comprise a lead having at least one biosignal sensor configured to transmit biosignal data based on electrophysiological activity of a subject. A first processing system may be coupled to the biosignal sensor and comprise a plurality of analog signal processing circuits configured to be selectively powered based on a selectable treatment mode. A second processing system in communication with the first processing system and may comprise a plurality of digital signal processing circuits configured to be selectively powered based on the treatment mode. A neurostimulator may stimulate tissue according to the set of biosignal characteristics.

Abstract: Devices, systems, and methods herein relate to processing biosignal data. These systems and methods may obtain sensor data from a plurality of electrodes and may also be used to augment cortical function, treat neurological disease, and provide insight and analysis of biological processes and/or clinical therapeutic outcomes. An implantable biosignal processing system may comprise a lead having at least one biosignal sensor configured to transmit biosignal data based on electrophysiological activity of a subject. A first processing system may be coupled to the biosignal sensor and comprise a plurality of analog signal processing circuits configured to be selectively powered based on a selectable treatment mode. A second processing system in communication with the first processing system and may comprise a plurality of digital signal processing circuits configured to be selectively powered based on the treatment mode. A neurostimulator may stimulate tissue according to the set of biosignal characteristics.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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

Abstract: 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.

Abstract: 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.

Abstract: 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.