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Reasoning About Edges in Scale Space
April 1992 (vol. 14 no. 4)
pp. 450-468

Explores the role of reasoning in early vision processing. In particular, the problem of detecting edges is addressed. The authors do not try to develop another edge detector, but rather, they study an edge detector rigorously to understand its behavior well enough to formulate a reasoning process that allow appliance of the detector judiciously to recover useful information. They present a multiscale reasoning algorithm for edge recovery: reasoning about edges in scale space (RESS). The knowledge in RESS is acquired from the theory of edge behavior in scale space and represented by a number of procedures. RESS recovers desired edge curves through a number of reasoning processes on zero crossing images at various scales. The knowledge of edge behavior in scale space enables RESS to select proper scale parameters, recover missing edges, eliminate noise or false edges, and correct the locations of edges. A brief evaluation of RESS is performed by comparing it with two well-known multistage edge detection algorithms.

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Index Terms:
computer vision; knowledge representation; inference mechanisms; noise elimination; edges; scale space; edge detector; multiscale reasoning algorithm; edge recovery; edge behavior; edge curves; zero crossing images; computer vision; inference mechanisms; knowledge representation
Citation:
Y. Lu, R.C. Jain, "Reasoning About Edges in Scale Space," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 4, pp. 450-468, April 1992, doi:10.1109/34.126806
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