Vision-Based Object Registration for Real-Time Image Overlay
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Nonlinear Modeling of Scattered Multivariate Data and Its Application to Shape Change
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affine-Invariant Recognition of Gray-Scale Characters Using Global Affine Transformation Correlation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Antifaces: A Novel, Fast Method for Image Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multispace KL for Pattern Representation and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Pictorial Portrait Indexing Using View-Based Eigen-Eyes
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Auto-associative models and generalized principal component analysis
Journal of Multivariate Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Mathematical Imaging and Vision
ACM Transactions on Information Systems (TOIS)
Face recognition in global harmonic subspace
IEEE Transactions on Information Forensics and Security
Object recognition for obstacle avoidance in mobile robots
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Human eyebrow recognition in the matching-recognizing framework
Computer Vision and Image Understanding
A novel evolutionary algorithm inspired by the states of matter for template matching
Expert Systems with Applications: An International Journal
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We present a fast pattern matching algorithm with a large set of templates. The algorithm is based on the typical template matching speeded up by the dual decomposition; the Fourier transform and the Karhunen-Loeve transform. The proposed algorithm is appropriate for the search of an object with unknown distortion within a short period.Patterns with different distortion differ slightly from each other and are highly correlated. The image vector subspace required for effective representation can be defined by a small number of eigenvectors derived by the Karhunen-Loeve transform. A vector subspace spanned by the eigenvectors is generated, and any image vector in the subspace is considered as a pattern to be recognized.The pattern matching of objects with unknown distortion is formulated as the process to extract the portion of the input image, find the pattern most similar to the extracted portion in the subspace, compute normalized correlation between them at each location in the input image, and find the location with the best score. Searching for objects with unknown distortion requires vast computation. The formulation above makes it possible to decompose highly correlated reference images into eigenvectors, as well as to decompose images in frequency domain, and to speed up the process significantly.