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Conference on Advanced Sensor Systems and Applications XI

作者： Ge, Zhou Zhu, Yanmin Zhang, Yunping Lam, Edmund Y. Univ Hong Kong Dept Elect & Elect Engn Pokfulam Hong Kong Peoples R China

ISBN: (数字)9781510646520

ISBN: (纸本)9781510646520;9781510646513

Dynamic speckle analysis (DSA) is a non-contact method to detect movements of the inspected objects. By illuminating the observed sample using a coherent light source, motion information can be obtained from a series of reflecting speckle patterns. Conventional DSA methods record the intensity of the speckle patterns using a frame-based imaging sensor. Here, we propose a novel implementation of DSA using the event sensor which captures the brightness changes of the dynamic speckle patterns with high temporal resolution and low latency. Our method is based on the block matching algorithm in which the captured event stream is divided into many non-overlapping blocks and motion information can be computed by searching for the blocks with the highest similarity. The experimental results demonstrate the feasibility of our method in different dynamic levels, and this work will be beneficial for various applications, such as biomedical imaging and material science.

关键词： Dynamic speckle analysis block matching algorithm event-based vision

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Object Tracking using Motion Estimation based on block matching algorithm 5

Object Tracking using Motion Estimation based on Block Match...

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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Sri, M. Sushma Naik, B. Rajendra Jayasankar, K. Osmania Univ Coll Engn ECE Hyderabad India Osmania Univ Dept ECE Coll Engn Hyderabad India MGIT Dept ECE Hyderabad India

ISBN: (纸本)9781728146850

Object tracking and motion estimation are key components in large number of applications, ranging from the navigation of autonomous vehicles to video data compression. This object tracking uses the motion estimation for continuous tracking of distinctive features in a successive manner. This helps to detect the motion of features used in computing to detect the exact or appropriate solution by optimizing the block size. To obtain the better prediction quality by using variable size motion estimation, proposed a block matching algorithm for motion estimation, object tracking, and video data compression.

关键词： Motion Estimation block matching algorithm FSBM VSBM Object tracking

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A new block matching algorithm based on stochastic fractal search

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APPLIED INTELLIGENCE 2019年第3期49卷 1146-1160页

作者： Betka, Abir Terki, Nadjiba Toumi, Abida Hamiane, Madina Ourchani, Amina Univ Biskra Algeria Dept Elect Engn Biskra Algeria Ahlia Univ Dept Telecommun Engn Manama Bahrain

block matching algorithm is the most popular motion estimation technique, due to its simplicity of implementation and effectiveness. However, the algorithm suffers from a long computation time which affects its general performance. In order to achieve faster motion estimation, a new block matching algorithm based on stochastic fractal search, SFS, is proposed in this paper. SFS is a metaheuristic technique used to solve hard optimization problems in minimal time. In this work, two main contributions are presented. The first one consists of computing the motion vectors in a parallel structure as opposed to the other hierarchical metaheuristic block matching algorithms. When the video sequence frame is divided into blocks, a multi-population model of SFS is used to estimate the motion vectors of all blocks simultaneously. As a second contribution, the proposed algorithm is modified in order to enhance the results. In this modified version, four ideas are investigated. The random initialization, usually used in metaheuristics, is replaced by a fixed pattern. The initialized solutions are evaluated using a new fitness function that combines two matching criteria. The considered search space is controlled by a new adaptive window size strategy. A modified version of the fitness approximation method, which is known to reduce computation time but causes some degradation in the estimation accuracy, is proposed to balance between computation time and estimation accuracy. These ideas are evaluated in nine video sequences and the percentage improvement of each idea, in terms of estimation accuracy and computational complexity, is reported. The presented algorithms are then compared with other well-known block matching algorithms. The experimental results indicate that the proposed ideas improve the block matching performance, and show that the proposed algorithm outperforms many state-of-the-art methods.

关键词： block matching algorithm Motion estimation Stochastic fractal search Metaheuristics

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Stereoscopic Image Coding Performance using Disparity-Compensated block matching algorithm 23

Stereoscopic Image Coding Performance using Disparity-Compen...

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23rd IEEE Conference on Signal Processing algorithms, Architectures, Arrangements, and Applications (SPA)

作者： Kadri, Imen Dauphin, Gabriel Mokraoui, Anissa Lachiri, Zied Univ Paris 13 L2TI Inst Galilee Villetaneuse France Univ Tunis El Manar Ecole Natl Ingn Tunis LSITI Tunis Tunisia

ISBN: (纸本)9788362065363

This paper focuses on the disparity-compensated stereoscopic image coding. Such approach takes advantage of the existing redundancy between the two views as they are intended to render the visual impression of a 3D-scene, in which inter-view object displacements are understood as depth-related information. The classical approach is based on block matching (BM) algorithm, yielding a disparity map with which the predicted image is most similar to its original version. Then, with no modification of the disparity map, the residual image is encoded, yielding a refinement added to the predicted image. The proposed approach, first, improves all the possible predicted images taking into account this refinement, and then, estimates the disparity map as the one with which the predicted image resembles most that same view. Despite the significant increase in the numerical complexity, the substantial improved performance in terms of Peak-Signal to Noise-Ratio (PSNR) of this new approach is evidence of ongoing progress in this field of research.

关键词： Stereoscopic Image Coding block matching algorithm Disparity Disparity-Compensation

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Implementation of Video Error Concealment Using block matching algorithm 2nd

Implementation of Video Error Concealment Using Block Matchi...

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2nd International Conference on Information and Communication Technology for Intelligent Systems (ICTIS)

作者： Rajani, P. K. Khaparde, Arti Ghuge, Aditi D. Pimpri Chinchwad Coll Engn Pune Maharashtra India Maharashtra Inst Technol Pune Maharashtra India

ISBN: (纸本)9783319636733;9783319636726

Video Error Concealment is the error hiding technique in videos. In recent years, there is huge requirement of error concealment in video applications such as in video streaming, entertainment, advertisement, media, security, etc. The simulation on MATLAB for the error videos using block matching algorithm (BMA) has been performed to achieve the concealed videos. From an error video, error frame is detected using Histogram and correlation. This frame is corrected using BMA. First step of BMA is to divide the current frame of a video into macroblock. Second step is to compare each of the macroblocks with a corresponding block and its adjacent neighbors in the frame or previous frame. Third step is to models the movement in a macroblock from one location to another. Last step is to calculate this movement for all the macro blocks that is comprising a frame. This error block is replaced by correct reference block. The quality of the error video and concealed video is measured using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Method). An improvement in quality is observed in concealed video.

关键词： block matching algorithm Error concealment SSIM PSNR

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block matching algorithm Using Mean and Low Order Moments

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Research Journal of Applied Sciences, Engineering and Technology 2016年第4期12卷 498-506页

作者： Zainab J. Ahmed Loay E. George Department of Computer Science College of Science Baghdad University Baghdad Iraq

In this study, a fast block matching search algorithm based on blocks’ descriptors and multilevel blocks filtering is introduced. The used descriptors are the mean and a set of centralized low order moments. Hierarchal filtering and MAE similarity measure were adopted to nominate the best similar blocks lay within the pool of neighbor blocks. As next step to blocks nomination the similarity of the mean and moments is used to classify the nominated blocks and put them in one of three sub-pools, each one represents certain nomination priority level (i.e., most, less & least level). The main reason of the introducing nomination and classification steps is a significant reduction in the number of matching instances of the pixels belong to the compared blocks is achieved. Instead of pixels-wise comparisons a set of hierarchal similarity comparisons between few descriptors of the compared blocks is done. The computations of blocks descriptors have linear complexity, O(n) and small number of involved similarity comparisons is required. As final stage, the selected blocks as the best similar blocks according to their descriptors are only pushed to pixel-wise blocks comparison stage. The performance of the proposed system was tested for both cases: (i) without using prediction for assessing the initial motion vector and (ii) with using prediction that based on the determined motion vectors of already scanned neighbor blocks. The test results indicated that the introduced method for both cases (without/ with prediction) can lead to promising results in terms of time and error level; because there is reduction in search time and error level parameters in comparison with exhaustive search and three step search (TSS) algorithms.

关键词： block matching algorithm Descriptor Exhaustive Search Hierarchal Filtering Moments Predicted Motion Vector Three Step Search (TSS)

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Modified block matching algorithm Improving Rate-Distortion Performance for Stereoscopic Image Coding 15

Modified Block Matching Algorithm Improving Rate-Distortion ...

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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Kadaikar, Aysha Dauphin, Gabriel Mokraoui, Anissa Univ Paris 13 Inst Galilee L2TI 99 Ave Jean Baptiste Clement F-93430 Villetaneuse France

ISBN: (纸本)9781509004805

This paper deals with the block-based disparity map estimation of a stereoscopic image. While most existing algorithms estimate this map by minimizing a dissimilarity metric, the proposed optimization algorithm aims at minimizing the rate distortion compromise using the disparity map yielded by the traditional block matching algorithm as an initial reference map. The developed algorithm analyzes the performance impact of the permutation of each disparity of the reference map with all possible disparities. The retained disparity is one that improves the joint rate-distortion metric. This process is repeated as long as improvements are observed. Moreover, a particular attention is given to the updating process of the joint metric so that the algorithm computational cost is not affected. Simulation results clearly show that our approach achieves better performance than the traditional block matching algorithm in terms of rate-distortion compromise.

关键词： block matching algorithm Optimization Disparity estimation Entropy Distortion

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An efficient algorithm for fast block matching motion estimation using an adaptive threshold scheme

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第33-34期79卷 24173-24184页

作者： Kerfa, Djoudi Saidane, AbdelKader Natl Polytech Sch Oran Elect Engn Dept CaSiCCE Lab Maurice Audin Ex ENSET BP 1523 El MNaouar Es Senia 31000 Algeria

A new block-matching algorithm for fast motion estimation is proposed. The so-called Star Diamond Search with Adaptive Threshold(SDth)is two steps algorithm. Adaptive threshold for matching errors eliminates invalid blocks early from motion estimation procedure. Then it performs a search for final motion vector with a Star Diamond algorithm. Proposed SD(th)algorithm has been implemented and tested using several video sequences. SD(th)algorithm is also compared with previous search methods to demonstrate its utility.

关键词： block matching algorithm Threshold Motion estimation

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Moving objects detection in thermal scene videos using unsupervised Bayesian classifier with bootstrap Gaussian expectation maximization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第2期83卷 6335-6350页

作者： Kerfa, Djoudi Natl Polytech Sch Oran Maurice Audin Ex Enset PB1523 El Mnaouar 31000 Algeria

In this paper, a new algorithm for moving object detection is proposed by using unsupervised Bayesian classifier with bootstrap Gaussian expectation maximization algorithm. It consists of the following steps: the first contains of classify and estimate the motion vectors between successive frames using the Star diamond search algorithm based on unsupervised Bayesian classifier with Gaussian Expectation of Maximization algorithm, this step serves also to detect the static and dynamic blocks. In the second step, the dynamic blocks are compensated with the white pixels value and the stationary are compensated by black pixels value. In the third step, the morphological opening and closing filters are used for refining the object detected. The proposed approach is trained and evaluated using available infrared (FLIR_ADAS_v2) dataset. The results demonstrate the effectiveness of the proposed method.

关键词： Infrared video sequences Moving object detection block matching algorithm Unsupervised bayesian classifier Gaussian expectation of maximization

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An Efficient algorithm for Fast block matching Motion Estimation Using an Adaptive Threshold Scheme

An Efficient Algorithm for Fast Block Matching Motion Estima...

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Conference on Real-Time Image Processing and Deep Learning

作者： Kerfa, Djoudi Saidane, Abdelkader Polytech Natl Sch Oran Maurice Audin Ex Enset Elect Engn Dept Lab CaSiCCE BP 1523 El Mnaouar 31000 Algeria

ISBN: (纸本)9781510626584

A new block-matching algorithm for fast motion estimation is proposed. The so-called Star Diamond Search with Adaptive Threshold (SDth) is two steps algorithm. Adaptive threshold for matching errors eliminates invalid blocks early from motion estimation procedure. Then it performs a search for final motion vector with a Star Diamond algorithm. Proposed SDth algorithm has been implemented and tested using several video sequences. SDth algorithm is also compared with previous search methods to demonstrate its utility.

关键词： block matching algorithm threshold Motion estimation

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