Real and complex analysis, 3rd ed.
Real and complex analysis, 3rd ed.
Digital Picture Processing
Fast algorithms for the estimation of motion vectors
IEEE Transactions on Image Processing
Successive elimination algorithm for motion estimation
IEEE Transactions on Image Processing
ZNCC-based template matching using bounded partial correlation
Pattern Recognition Letters
Representing Images Using Nonorthogonal Haar-Like Bases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient NCC-Based Image Matching Based on Novel Hierarchical Bounds
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Efficient normalized cross correlation based on adaptive multilevel successive elimination
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Simple low-dimensional features approximating NCC-based image matching
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Human eyebrow recognition in the matching-recognizing framework
Computer Vision and Image Understanding
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This paper describes a novel, fast templatematching technique, referred to as bounded partial correlation (BPC), based on the normalised cross-correlation (NCC) function. The technique consists in checking at each search position a suitable elimination condition relying on the evaluation of an upper-bound for the NCC function. The check allows for rapidly skipping the positions that cannot provide a better degree of match with respect to the current best-matching one. The upper-bounding function incorporates partial information from the actual cross-correlation function and can be calculated very efficiently using a recursive scheme. We show also a simple improvement to the basic BPC formulation that provides additional computational benefits and renders the technique more robust with respect to the parameters choice.