A9.com, Inc.

Bryan E Feldman

Nalin Senthamil

Gurumurthy D Ramkumar

Arnab S Dhua

Arnab Sanat Kumar Dhua

William F Stasior

Systems and methods track one or more points between images. A point for tracking may be selected, at least in part, on a determination of how discriminable the point is relative to other points in a region containing the point. A point of an image being tracked may be located in another image by matching a patch containing the point with another patch of the other image. A search for a matching patch may be focused in a region that is determined based at least in part on an estimate of movement of the point between images. Points may be tracked across multiple images. If an ability to track one or more points is lost, information about the points being tracked may be used to relocate the points in another image.

Tracking objects between images

Techniques for augmenting an image of an object captured and displayed in real time with associated content are disclosed. In one embodiment, the method for augmenting the image includes receiving information defining a sampled frame of a video being captured by an electronic device in substantially real time, determining information representative of an object captured in the sampled frame based on the received information, causing the determined information to match stored information defining a plurality of items to locate an item matched to the captured object, retrieving content associated with the matched item, and providing the retrieved content for display with the captured image on the electronic device. The retrieved content may be rendered in an overlay element that overlays the captured image displayed on the electronic device. The rendered content is configured to enable a user to interact with the content.

Augmenting a live view

The present technology relates to methods, systems and computer program products for detecting objects in images captured by a camera of a mobile device. The detection of objects (such as barcodes, QR codes, and text) in images can be based at least in part on image gradients of the image. The image can be divided into a plurality of regions, each having a dominant gradient direction. Based at least in part on the dominant gradient directions of the regions satisfying orientation thresholds, the regions can be identified as candidate or non-candidate regions for corresponding to a predetermined object. The candidate regions can be merged if they satisfy a connecting criterion. The merged regions can then be analyzed to determine if the merged regions satisfy a geometric property of the predetermined object, such as having a rectangular shape substantially similar to that of a barcode.

Detecting objects in images using image gradients

Processes such as image matching can be improved by utilizing a Polar Accumulated Signed Gradient (PASG), a feature descriptor vector that utilizes polar coordinates. Such an approach is efficient because it avoids the costly image rotation and resampling of conventional processes. Further, the gradients can be sampled over different regions in order to achieve effective weighting without analyzing all gradients and applying a weighting factor. Such efficiencies enable image matching to be performed on devices with limited computing capacity, such as smart phones and tablet computers.

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San Francisco, CA, United States of America

Bryan E Feldman