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Efficient Hardware Architecture for Fast Image Similarity Calculation  

Kwon, Soon (Daegu Gyeongbuk Institute of Science and Technology)
Lee, Chung-Hee (Daegu Gyeongbuk Institute of Science and Technology)
Lee, Jong-Hun (Daegu Gyeongbuk Institute of Science and Technology)
Moon, Byung-In (School of Electronics Engineering, Kyungpook National University)
Lee, Yong-Hwan (School of Electronics Engineering, Kumoh National Institute of Technology)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SD / v.48, no.4, 2011 , pp. 6-13 More about this Journal
Abstract
Due to its robustness to illumination change, normalized cross-correlation based similarity measurement is widely used in many machine vision applications. However, its inefficient computation structure is not adequate for real-time embedded vision system. In this paper, we present an efficient hardware architecture based on a normalized cross correlation (NCC) for fast image similarity measure. The proposed architecture simplifies window-sum process of the NCC using the integral-image. Relieving the overhead to constructing integral image, we make it possible to process integral image construction at the same time that pixel sequences are inputted. Also the proposed segmented integral image method can reduce the buffer size for storing integral image data.
Keywords
Similarity measure; Normalized Cross Correlation; Box-filtering; Segmented integral image;
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