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On vector quantization for fast facet edge detection

M.Y. Jaisimha, J.R. Goldschneider, A.E. Mohr, E.A. Riskin, R.M. Haralick

Year
2002
Citations
4

Abstract

Presents an approach for performing edge detection which builds on prior work in fast facet edge detection using tree-structured vector quantization (TSVQ). The authors first extend the approach by using larger image vectors to reduce computational complexity by performing edge detection on multiple pixels at once. They then reduce the computational complexity of the edge detector without sacrificing performance by pruning the TSVQ with an edge detection-based criterion. They present results of edge detector performance on a sequence of images obtained from a mobile robot.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Keywords

Vector quantizationEdge detectionComputer scienceEnhanced Data Rates for GSM EvolutionLearning vector quantizationComputer visionArtificial intelligenceQuantization (signal processing)DetectorComputational complexity theory

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