Peak detection in Hough transform via self-organizing learning

Peak detection in Hough transform via self-organizing learning

In this paper, we suggest a novel concept of applying the self-organizing map (SOM) in the Hough domain for a significant reduction of the Hough space. By using the SOM as the output space of the generalized Hough transform, the conventional 4-D Hough domain is replaced by a 10/spl times/10 map, org...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Choy, C.S.-T., Ser, P.-K., Siu, W.-C.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Analog circuits
Costs
Councils
Feature extraction
Genetic expression
Neural networks
Neurons
Pixel
Testing
Voting
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Beschreibung
Zusammenfassung:In this paper, we suggest a novel concept of applying the self-organizing map (SOM) in the Hough domain for a significant reduction of the Hough space. By using the SOM as the output space of the generalized Hough transform, the conventional 4-D Hough domain is replaced by a 10/spl times/10 map, organized in a rectangular grid. Experimental results indicate high accuracy in voting is attainable despite its small memory requirement.
DOI:10.1109/ISCAS.1995.521470