Parallel Algorithms for Image Template Matching on Hypercube SIMD Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel Image Correlation: Case Study to Examine Trade-Offs in Algorithm-to-Machine Mappings
The Journal of Supercomputing
Digital Picture Processing
ACM Computing Surveys (CSUR)
Fast linear discriminant analysis using binary bases
Pattern Recognition Letters
Representing Images Using Nonorthogonal Haar-Like Bases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast normalized cross correlation for motion tracking using basis functions
Computer Methods and Programs in Biomedicine
Collaboration of spatial and feature attention for visual tracking
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
A fast approach for person detection and tracking
International Journal of Computer Applications in Technology
Human eyebrow recognition in the matching-recognizing framework
Computer Vision and Image Understanding
Automatic scene calibration for detecting and tracking people using a single camera
Engineering Applications of Artificial Intelligence
Persistent tracking of static scene features using geometry
Computer Vision and Image Understanding
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Template matching by normalized correlations is a common technique for determine the existence and compute the location of a shape within an image. In many cases the run time of computer vision applications is dominated by repeated computation of template matching, applied to locate multiple templates in varying scale and orientation. A straightforward implementation of template matching for an image size n and a template size k requires order of kn operations. There are fast algorithms that require order of n log n operations. We describe a new approximation scheme that requires order n operations. It is based on the idea of "Integral-Images", recently introduced by Viola and Jones.