JOURNAL ARTICLE

<title>Software SKIPSM implementation for template matching</title>

Frederick M. WaltzJohn W. V. Miller

Year: 2001 Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Vol: 4189 Pages: 31-40   Publisher: SPIE

Abstract

This paper presents an updated version of a general method for carrying out binary template matching, which is useful for image analysis in general and automated visual inspection and quality control in particular. In a series of 23 papers, image processing implementations based on the SKIPSM (Separated- Kernel Image Processing using finite-State Machines) paradigm have been shown to be faster or much faster than conventional implementations. One of the earliest of these papers, published in 1994, was devoted to binary template matching of various types. As with all the papers of that era, the theory was presented in general form but the specific applications used LUTs (lookup tables) and pipelined hardware. The results were impressive - templates 35x35 or even larger could be executed in the same time as the identical hardware, programmed conventionally, could execute a 3x3 template. This paper develops and extends the same basic approach to provide fast and highly efficient binary template matching on ordinary desktop computers. This implementation does not use LUTs, because computers with pipelined instruction streams and vector data structures perform relatively slowly when using LUTs.

Keywords:
Computer science Template Kernel (algebra) Template matching Implementation Matching (statistics) Software Binary number Parallel computing Lookup table Image (mathematics) Computer hardware Computer architecture Theoretical computer science Computer engineering Programming language Artificial intelligence Arithmetic

Metrics

3
Cited By
0.00
FWCI (Field Weighted Citation Impact)
4
Refs
0.15
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Medical Image Segmentation Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Industrial Vision Systems and Defect Detection
Physical Sciences →  Engineering →  Industrial and Manufacturing Engineering

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