Correlation-Coefficient-Based Fast Template Matching Through Partial Elimination (original) (raw)

Computation Elimination Algorithms for Correlation Based Fast Template Matching

Template matching is frequently used in Digital Image Processing, Machine Vision, Remote Sensing and Pattern Recognition, and a large number of template matching algorithms have been proposed in literature. The performance of these algorithms may be evaluated from the perspective of accuracy as well as computational complexity. Algorithm designers face a tradeoff between these two desirable characteristics; often, fast algorithms lack robustness and robust algorithms are computationally expensive. ance of the residue signal will always be less than the existing motion compensation schemes (Mahmood et al., 2007). This result may potentially be used to increase compression of video signal as compared to the current techniques. The fast correlation strategies, proposed in this thesis, may be coupled with this result to develop correlation-based video encoders, having low computational cost.

Fast and high-performance template matching method

… Vision and Pattern Recognition (CVPR), 2011 …, 2011

This paper proposes a new template matching method that is robust to outliers and fast enough for real-time operation. The template and image are densely transformed in binary code form by projecting and quantizing histograms of oriented gradients. The binary codes are matched by a generic method of robust similarity applicable to additive match measures, such as L p -and Hamming distances. The robust similarity map is computed efficiently via a proposed Inverted Location Index structure that stores pixel locations indexed by their values. The method is experimentally justified in large image patch datasets. Challenging applications, such as intra-category object detection, object tracking, and multimodal image matching are demonstrated.

Maximum Entropy Matching: An Approach to Fast Template Matching

2000

One important problem in image analysis is the localization of a template in a larger image. Applications where the solution of this problem can be used include: tracking, optical flow, and stereo vision. The matching method studied here solve this problem by defining a new similarity measurement between a template and an image neighborhood. This similarity is computed for all possible integer positions of the template within the image. The position for which we get the highest similarity is considered to be the match. The similarity is not necessarily computed using the original pixel values directly, but can of course be derived from higher level image features.The similarity measurement can be computed in differentways and the simplest approach are correlation-type algorithms. Aschwanden and Guggenb¨uhl [2] have done a comparison between such algorithms. One of best and simplest algorithms they tested is normalized cross-correlation (NCC). Therefore this algorithm has been used t...

Modifications in Normalized Cross Correlation Expression for Template Matching Applications

cerc.wvu.edu

This paper analyzes the performance of sum of squared differences (SSD), sum of absolute differences (SAD), normalized cross correlation (NCC), zero mean normalized cross correlation (ZNCC) and several other proposed modified expressions of NCC. Experimental results on real images demonstrate that some of the proposed modified expressions of NCC are more efficient than conventional NCC for template matching. Three of the modified expressions of NCC perform similar to ZNCC however they are computationally less intensive. Modified expressions of NCC were also studied under different values of additive white Gaussian noise. Some of them perform better than ZNCC in terms of successfully found points and computation time for noisy images.

FRoTeMa: Fast and Robust Template Matching

International Journal of Advanced Computer Science and Applications, 2015

Template matching is one of the most basic techniques in computer vision, where the algorithm should search for a template image T in an image to analyze I. This paper considers the rotation, scale, brightness and contrast invariant grayscale template matching problem. The proposed algorithm uses a sufficient condition for distinguishing between candidate matching positions and other positions that cannot provide a better degree of match with respect to the current best candidate. Such condition is used to significantly accelerate the search process by skipping unsuitable search locations without sacrificing exhaustive accuracy. Our proposed algorithm is compared with eight existing state-of-the-art techniques. Theoretical analysis and experiments on eight image datasets show that the proposed simple algorithm can maintain exhaustive accuracy while providing a significant speedup.