Abstract: A computing device segments an image into a plurality of segments, wherein each segment of the plurality of segments has a segment location and a set of pixels that share visual characteristics. The computing device determines an initial set of bounding boxes for the image based on the plurality of segments. The computing device determines a reduced set of bounding boxes based on combining bounding boxes of the initial set of bounding boxes, the reduced set of bounding boxes corresponding to one or more objects in the image, each of the one or more objects having an object class and an object location.

Abstract: Methods and systems for processing an image to facilitate automated object recognition are disclosed. More particularly, an image is processed based on a perceptual grouping for the image (e.g., derived via segmentation, derived via contour detection, etc.) and a geometric-configuration model for the image (e.g., a bounding box model, a constellation, a k-fan, etc.).

Abstract: A computing device segments an image into a plurality of segments, wherein each segment of the plurality of segments comprises a set of pixels that share visual characteristics. The computing device then determines expected contexts for the segments, wherein an expected context for a segment comprises at least one of additional segments or features expected to occur in the image together with the segment. The computing device then identifies a probable object based on the expected contexts.

Abstract: Methods and systems for processing an image to create an object model are disclosed. In accordance with one embodiment, each segment of the image is assigned to a respective bin of a bounding box. For each bin of the bounding box, the value of a feature for the bin is computed based on the values of that feature for each of the segments assigned to the bin. An object model is then created based on the values of the feature for the bin.