A new distance measure based on generalized Image Normalized Cross-Correlation for robust video tracking and image recognition

被引：71

作者：

Nakhmani, Arie ^{[1
,2
]}

Tannenbaum, Allen ^{[2
,3
]}

机构：

[1] Boston Univ, Dept Elect & Comp Engn, Boston, MA 02215 USA

[2] UAB, Dept Elect & Comp Engn, Birmingham, AL USA

[3] UAB, Dept Radiol, Ctr Comprehens Canc, Birmingham, AL USA

来源：

PATTERN RECOGNITION LETTERS | 2013年 / 34卷 / 03期

基金：

美国国家卫生研究院;

关键词：

Correlation; NCC; Image distance; Template matching;

D O I：

10.1016/j.patrec.2012.10.025

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：315 / 321

页数：7

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