Abstract: Two different approaches for reducing the bit depth of the image data so as to reduce the computation and hardware requirement of image patch matching, with minimal loss of matching accuracy are described. Patch matching is able to be implemented in many different ways, but generally involves matching one area of an image with another area of the same image or another area of a different image (e.g. another video frame) through the use of a matching cost function. Transforming the image data to lower bit depth, image processing techniques are able to be implemented to minimize the needed memory and other resources for patch-matching. The complexity/performance trade-off of the approaches are also adjustable so that they are able to be applied for applications with different quality requirements and hardware constraints.

Abstract: An improved method and apparatus for localizing objects within an image is disclosed. In one embodiment, the method comprises accessing at least one object model representing visual word distributions of at least one training object within training images, detecting whether an image comprises at least one object based on the at least one object model, identifying at least one region of the image that corresponds with the at least one detected object and is associated with a minimal dissimilarity between the visual word distribution of the at least one detected object and a visual word distribution of the at least one region and coupling the at least one region with indicia of location of the at least one detected object.