Computation Elimination Algorithms for Correlation Based Fast Template Matching (original) (raw)
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Correlation-Coefficient-Based Fast Template Matching Through Partial Elimination
IEEE Transactions on Image Processing, 2012
Partial computation elimination techniques are often used for fast template matching. At a particular search location, computations are prematurely terminated as soon as it is found that this location cannot compete already known best-match location. Due to non-monotonic growth pattern of the correlationbased similarity measures, partial computation elimination techniques have been traditionally considered inapplicable to speedup these measures. In this work we show that partial elimination techniques may be applied to correlation coefficient by using a monotonic formulation and we propose a basic mode and an extended mode partial correlation elimination algorithm for fast template matching. The basic mode algorithm is more efficient on small template sizes while the extended mode is faster on medium and larger templates. We also propose a strategy to decide which algorithm to use for a given dataset. To achieve a high speed up, elimination algorithms require an initial guess of the peak correlation value. We propose two initialization schemes including coarse-to-fine scheme for larger templates and a two-stage technique for small and medium sized templates. Our proposed algorithms are exact, having exhaustive equivalent accuracy, and are compared with the existing fast techniques using real image datasets on a wide variety of template sizes. While the actual speedups are data dependent, in most of the cases our proposed algorithms have been found to be significantly faster than the other algorithms.
STUDY OF TEMPLATE COMPRESSION IN IMAGE CORRELATION BASED MOTION ESTIMATION
IAEME PUBLICATION, 2021
The aim of present study was to develop a template tracking based image correlation algorithm for small-scale motion estimation. Image correlation based motion estimation algorithms consume significant data due to storage of high-resolution image sequence. We propose a template tracking based image correlation algorithm coupled with wavelet based data compression technique. We configured a two-camera system stereovision setup for synchronized image acquisition. The setup calibration produced camera parameters to implement in the proposed correlation algorithm. We obtained hand motion sequences from the setup and correlated using the proposed algorithm to obtain template tracking parameter (TTP) as a measure of correlation. To investigate the effect of data compression on correlation, we correlated the compressed motion frames using compressed templates with bior 4.4, bior 5.5 and bior 6.8 compression levels. Inclusion of data compression in the proposed algorithm produced TTP value close to those obtained with uncompressed templates. TTP value using bior 6.8 was 0.9493 and 0.8608 for left and right camera, which was close to uncompressed image frames. The correlation results were visualized using Paraview visualization tool to validate the proposed algorithm.
IET Image Processing, 2012
Design and implementation of a new early termination algorithm for efficient calculation of correlation coefficient for template matching is presented. The proposed algorithm correlates the candidates against 'negative' (bit-inverted) version of reference instead of the original to implement a low cost early termination criterion. Around 10% computational savings have been demonstrated while using the proposed algorithm to compute normalised correlation as error metric for motion estimation in software implementation of H.264 video encoder. The algorithm lends itself to efficient hardware implementation because of its simple cost function. Further hardware savings have been realised by noting that the multiplication products generated by the proposed negative reference correlation algorithm tend to have low magnitudes with significantly less variance than those generated by other schemes. This allows a low-precision summation stage to accumulate majority of the multiplication products without losing the precision of results. Results of the hardware implementation on Xilinx Virtex-5 FPGA have been provided. The overall design is shown to consume around 85% less logic resources and operate at 140% higher speed than existing architectures.
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...
A simple and efficient template matching algorithm
We propose a general framework for object tracking in video images. It consists in low-order parametric models for the image motion of a target region. These models are used to predict the movement and to track the target. The difference of intensity between the pixels belonging to the current region and the pixels of the selected target (learnt during an off-line stage) allows a straightforward prediction of the region position in the current image.
On optimizing template matching via performance characterization
2005
Template matching is a fundamental operator in computer vision and is widely used in feature tracking, motion estimation, image alignment, and mosaicing. Under a certain parameterized warping model, the traditional template matching algorithm estimates the geometric warp parameters that minimize the SSD between the target and a warped template. The performance of the template matching can be characterized by deriving the distribution of warp parameter estimate as a function of the ideal template, the ideal warp parameters, and a given noise or perturbation model. In this paper, we assume a discretization of the warp parameter space and derive the theoretical expression for the probability mass function (PMF) of the parameter estimate. As the PMF is also a function of the template size, we can optimize the choice of the template or block size by determining the template/block size that gives the estimate with minimum entropy. Experimental results illustrate the correctness of the theory. An experiment involving feature point tracking in face video is shown to illustrate the robustness of the algorithm in a real-world problem.