A Comparison of Different Block Matching Algorithms for Motion Estimation

Yaakob, Razali; Aryanfar, Alihossein; Halin, Alfian Abdul; Sulaiman, Nasir

doi:10.1016/j.protcy.2013.12.181

Cited by 31 publications

(16 citation statements)

References 8 publications

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“…But in the metrology of internally machined metal hole, there are very limited features available for dense matching. In this study, we employed block matching algorithm to find the corresponding [21]. For every pixel in image 2 (see Fig.…”

Section: Methodsmentioning

confidence: 99%

Axial-Stereo 3-D Optical Metrology for Inner Profile of Pipes Using a Scanning Laser Endoscope

Gong

Johnston

Melville

et al. 2015

International Journal of Optomechatronics

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As the rapid progress in the development of optoelectronic components and computational power, 3D optical metrology becomes more and more popular in manufacturing and quality control due to its flexibility and high speed. However, most of the optical metrology methods are limited to external surfaces. This paper proposed a new approach to measure tiny internal 3D surfaces with a scanning fiber endoscope and axial-stereo vision algorithm. A dense, accurate point cloud of internally machined threads was generated to compare with its corresponding X-ray 3D data as ground truth, and the quantification was analyzed by Iterative Closest Points algorithm.

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Section: Methodsmentioning

confidence: 99%

Axial-Stereo 3-D Optical Metrology for Inner Profile of Pipes Using a Scanning Laser Endoscope

Gong

Johnston

Melville

et al. 2015

International Journal of Optomechatronics

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“…However, in this case, the algorithm VASLA-ME and TPLA-ME have a better performance than the other motion estimation algorithms (Figs. 7,8,9). As a summary of the estimation quality performance, Fig.…”

Section: Estimation Qualitymentioning

confidence: 99%

“…Therefore, the best match for the current block will have the lowest error function among all candidate blocks within a search window [9,10].…”

Section: Introductionmentioning

confidence: 99%

Motion estimation using learning automata

Damerchilu

Norouzzadeh

Meybodi

2016

Machine Vision and Applications

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Block-matching algorithms (BMAs) are widely employed for motion estimation. BMAs divide input frames into several blocks and minimize an error function for each block to calculate motion vectors. Afterward, each motion vector is applicable for all of the pixels within the block. Since computing the error functions is resource intensive, many fast-search motion estimation algorithms have been suggested to reduce the computational cost. These fast algorithms provide a significant reduction in computation but often converge to a local minimum. A learning automaton is an adaptive decision-making unit that learns the optimal action through repeated interactions with its environment. Learning automata (LA) have been applied successfully to a wide range of applications including pattern recognition, dynamic channel assignment, and social network analysis. In this paper, we apply LA to motion estimation problem, which is one of the basic problems in computer vision. We compare the accuracy and performance of the suggested algorithms with other well-known BMAs. Interestingly, the obtained results indicate high efficiency and accuracy of the proposed methods. The results suggest that simplicity, efficiency, parallel nature, and accuracy of LA-based methods make them a good candidate to solve computer vision problems.

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“…In the proposed technique, Full-Search BMA is used for extracting the motion vectors. Full-Search is more efficient in terms of accuracy as compared to all other BMAs [7]. The computational time of Full-Search BMA is high, but its accuracy is better than all other BMAs.…”

Section: Introductionmentioning

confidence: 96%

Medical Image(s) Watermarking and its Optimization using Genetic Programming

Habib¹,

Alquhayz²

2019

IJACSA

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In this paper, an medical image watermarking technique has been proposed, where intelligence has been incorporated into the encoding and decoding structure. The motion vectors of the medical image sequence are used for embedding the watermark. Instead of a manual selection of the candidate motion vectors, a generalized approach is used to select the most suitable motion vectors for embedding the watermark. Genetic programming (GP) module has been employed to develop a function in accordance with imperceptibility and watermarking capacity. Employment of intelligence in the system improves its imperceptibility, capacity, and resistance toward different attacks that can occur during communication and storing. The motion vectors are generated by applying a blockbased motion estimation algorithm. In this work, Full-Search method has been used for its better performance as compared to the other methods. Experimental results show marked improvement in capacity and visual similarity as compared to the conventional approaches.

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A Comparison of Different Block Matching Algorithms for Motion Estimation

Cited by 31 publications

References 8 publications

Axial-Stereo 3-D Optical Metrology for Inner Profile of Pipes Using a Scanning Laser Endoscope

Axial-Stereo 3-D Optical Metrology for Inner Profile of Pipes Using a Scanning Laser Endoscope

Motion estimation using learning automata

Medical Image(s) Watermarking and its Optimization using Genetic Programming

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