A survey of image registration techniques
ACM Computing Surveys (CSUR)
Object Matching Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Task-Specific Gesture Analysis in Real-Time Using Interpolated Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable template models: a review
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Combinatorial methods for approximate image matching under translations and rotations
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Direct search methods: then and now
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
Maximum-Likelihood Image Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Position-Orientation Masking Approach to Parametric Search for Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locating objects using the Hausdorff distance
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Multi-Feature Hierarchical Template Matching Using Distance Transforms
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Template Matching: Matched Spatial Filters and Beyond
Template Matching: Matched Spatial Filters and Beyond
Detection and Recognition of Lung Abnormalities Using Deformable Templates
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Two-dimensional pattern matching with rotations
CPM'03 Proceedings of the 14th annual conference on Combinatorial pattern matching
IEEE Transactions on Image Processing
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This paper presents a template matching technique to identify the location and orientation of an object by a fast algorithm. The fundamental principle in template matching is to minimize a potential energy function, which is a quantitative representation of the ’closeness’ of a defined object (template) relative to a portion of an image. However, the computation of potential energy suffers a major drawback in efficiency. A significant amount of the processing time is dedicated to match the information from the template to the image. This work proposes an alternative way to match the template and the image that reduces the number of operations from O(nm) to O(n) in calculating the potential energy of a template and an image that have n and m number of edge pixels, respectively. This work illustrates this approach by template edge matching which uses the edge information to perform the template matching. The experimental results show that while the proposed method produces a slightly larger error in the resulting template location, the processing time is decreased by a factor of 4.8 on average.