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A New Distance Measure Based on Generalized Image Normalized Cross-correlation for Robust Video Tracking and Image Recognition

Institution:
Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.
Publication Date:
Feb-2013
Journal:
Pattern Recognit Lett
Volume Number:
34
Issue Number:
3
Pages:
315-21
Citation:
Pattern Recognit Lett. 2013 Feb 1;34(3):315-21.
PubMed ID:
23503649
PMCID:
PMC3596837
Keywords:
Correlation, Image distance, NCC, Template matching
Appears in Collections:
NAC, NA-MIC
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
P41 RR013642/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Nakhmani A., Tannenbaum A. A New Distance Measure Based on Generalized Image Normalized Cross-correlation for Robust Video Tracking and Image Recognition. Pattern Recognit Lett. 2013 Feb 1;34(3):315-21. PMID: 23503649. PMCID: PMC3596837.
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We propose two novel distance measures, normalized between 0 and 1, and based on normalized cross-correlation for image matching. These distance measures explicitly utilize the fact that for natural images there is a high correlation between spatially close pixels. Image matching is used in various computer vision tasks, and the requirements to the distance measure are application dependent. Image recognition applications require more shift and rotation robust measures. In contrast, registration and tracking applications require better localization and noise tolerance. In this paper, we explore different advantages of our distance measures, and compare them to other popular measures, including Normalized Cross-Correlation (NCC) and Image Euclidean Distance (IMED). We show which of the proposed measures is more appropriate for tracking, and which is appropriate for image recognition tasks.

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