The Analysis of Similarity Measure Function in Image Matching Algorithms

Abstract:

Similarity measure function is one of the most important factors influencing the matching precision in the field of computer vision. In this paper, a survey is done on the application frequency of distance similarity measure methods and related similarity measure methods, also the statistic characteristic is been given. The significance of Measure functions variable parameters in image matching is showed. In the real time processing aspect, drawn the conclusion that Manhattan distance measure is the fastest, Euclidean distance take second place, correlation coefficient is worst. However, in the robustness of the noise pollution aspect, correlation coefficient has the strongest robustness, then followed is Manhattan distance, Euclidean distance is worst.

• Periodical: Advanced Materials Research (Volume 842)
• Pages: 649-653
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.842.649
• Online since: November 2013
• Authors: Hong Liang Liu, Wei Song*, Peng Yu Na, Ming Li, Pei Yang
• Keywords: Binocular Vision, Computer Vision, Image Matching, Similarity Measure Function

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