An introduction to digital image processing
An introduction to digital image processing
A Fast k Nearest Neighbor Finding Algorithm Based on the Ordered Partition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer vision: a first course
Computer vision: a first course
An efficient branch-and-bound nearest neighbour classifier
Pattern Recognition Letters
Strategies for efficient incremental nearest neighbor search
Pattern Recognition
Computer Vision
Fast Nearest-Neighbor Search in Dissimilarity Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sequential pattern recognition as applied to scene matching.
Sequential pattern recognition as applied to scene matching.
A fast hue-based colour image indexing algorithm
Machine Graphics & Vision International Journal - Special issue on latest results in colour image processing and applications
On pre-detect template matching
International Journal of Computer Applications in Technology
Fast Multivariate Ordinal Type Histogram Matching
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Generalization of the binary pattern matching in image processing
Mathematical and Computer Modelling: An International Journal
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Given a large text image and a small template image, the Template Matching Problem is that of finding every location within the text which looks like the pattern. This problem, which has received attention for low-level image processing, has been formalizedby defining a distance metric between arrays of pixels and finding allsubarrays of the large image which are within some threshold distanceof the template. These so-called metric methods tends to be tooslow for many applications, since evaluating the distance function cantake too much time.We present a method for quickly eliminating most positions of thetext from consideration as possible matches. The remainingcandidate positions are then evaluated one by one against thetemplate for a match. We are still guaranteed to find allmatching positions, and our method gives significant speed-ups. Finally, we consider the problem of matching a dictionary oftemplates against a text. We present methods which are much fasterthan matching the templates individually against the input image.