Using local orientation and hierarchical spatial feature matching for the robust recognition of objects

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Abstract
A new approach to the robust recognition of objects is presented. The fundamental picture primitives employed are local orientations, rather than the more traditionally used edge positions. A simple technique of feature-matching is used, based on the accumulation of evidence in binary channels (similar to the Hough transform) followed by a weighted non- linear sum of the evidence accumulators (matched filters, similar to those used in neural networks). By layering this simple feature-matcher, a hierarchical scheme is produced whose base is a binary representation of local orientations. The individual layers represent increasing levels of abstraction in the search for an object, so that the object can be arbitrarily complex. The universal algorithm presented can be implemented in less than 100 lines of a high-level programming language (e.g., Pascal). As evidenced by practical examples of various complexities, objects can be reliably and robustly identified in a wide variety of surroundings.

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