Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques
International Journal of Computer Vision
Fast template matching using bounded partial correlation
Machine Vision and Applications
The evaluation of normalized cross correlations for defect detection
Pattern Recognition Letters
Fast normalized cross correlation for defect detection
Pattern Recognition Letters
A Class of Algorithms for Fast Digital Image Registration
IEEE Transactions on Computers
Fast variable window for stereo correspondence using integral images
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Fast panoramic stereo matching using cylindrical maximum surfaces
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Machine Learning and Geometric Technique for SLAM
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Exploiting inter-frame correlation for fast video to reference image alignment
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Exploiting transitivity of correlation for fast template matching
IEEE Transactions on Image Processing
Mixed color/level lines and their stereo- matching with a modified Hausdorff distance
Integrated Computer-Aided Engineering
RT-SLAM: a generic and real-time visual SLAM implementation
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
VISON: VIdeo Summarization for ONline applications
Pattern Recognition Letters
Image and Vision Computing
Photometric visual servoing for omnidirectional cameras
Autonomous Robots
Enhancing a disparity map by color segmentation
Integrated Computer-Aided Engineering
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This paper describes a class of algorithms enabling efficient and exhaustive matching of a template into an image based on the Zero mean Normalized Cross-Correlation function (ZNCC). The approach consists in checking at each image position two sufficient conditions obtained at a reduced computational cost. This allows to skip rapidly most of the expensive calculations required to evaluate the ZNCC at those image points that cannot improve the best correlation score found so far. The algorithms shown in this paper generalize and extend the concept of Bounded Partial Correlation (BPC), previously devised for a template matching process based on the Normalized Cross-Correlation function (NCC).