An embedded architecture for real-time object detection in digital images based on niching particle swarm optimization

Mehmood, Shahid; Cagnoni, Stefano; Mordonini, Monica; Khan, Shoab A.

doi:10.1007/s11554-012-0256-7

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…It is to be noted that only few algorithms work for real-time video image of a number plate [33], [30], [39], [51], [61], otherwise static image of number plate is remitted to ANPR for further processing. In Table 1, different plate segmentation detection success rate against plate resolution of different ANPR is depicted.…”

Section: Discussionmentioning

confidence: 99%

“…Image segmentation techniques such as color texture based [55], coarse-to-fine strategy [56], wrapper based approach [57], content based image retrieval [58], dynamic region merging [59], Dual Multiscale Morphological Reconstructions and Retrieval Applications [37], background recognition and perceptual organization [60], niching particle swarm optimization [61], constraint satisfaction neural network [62], two stage self organizing network [63], adaptive local thresholds [64], vectorial scale-based fuzzyconnected image segmentation [65], mixed deterministic and Monte-Carlo [66], evaluation matrix based image segmentation [67], neutrosophic set and wavelet transformation [36], non linear distance matrix [68], shapeprior based image segmentation with intensity-based image registration [69] and least squares support vector machine (LS-SVM) [70] can be useful for object detection. In [71], survey of different image segmentation techniques is discussed.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

See 1 more Smart Citation

Automatic Number Plate Recognition System (ANPR): A Survey

Patel¹,

Shah²,

Patel³

2013

IJCA

147

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Traffic control and vehicle owner identification has become major problem in every country. Sometimes it becomes difficult to identify vehicle owner who violates traffic rules and drives too fast. Therefore, it is not possible to catch and punish those kinds of people because the traffic personal might not be able to retrieve vehicle number from the moving vehicle because of the speed of the vehicle. Therefore, there is a need to develop Automatic Number Plate Recognition (ANPR) system as a one of the solutions to this problem. There are numerous ANPR systems available today. These systems are based on different methodologies but still it is really challenging task as some of the factors like high speed of vehicle, non-uniform vehicle number plate, language of vehicle number and different lighting conditions can affect a lot in the overall recognition rate. Most of the systems work under these limitations. In this paper, different approaches of ANPR are discussed by considering image size, success rate and processing time as parameters. Towards the end of this paper, an extension to ANPR is suggested.

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Section: Discussionmentioning

confidence: 99%

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Automatic Number Plate Recognition System (ANPR): A Survey

Patel¹,

Shah²,

Patel³

2013

IJCA

147

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“…Multimodal optimization (MMO) algorithms [1] (also known as niching methods), which can locate multiple global optima in a single run, are essential for solving many scientific and engineering optimization problems [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, niching particle swarm optimization (niching PSO) algorithms have been proposed [7][8][9][10][11] by incorporating algorithmic particle-clustering procedures in the classical PSO algorithm [12][13][14]. These niching PSOs are able to find the multiple global optima and hence solve many real-world MMO problems [2][3][4][5][6]. However, these algorithms have drawbacks, i.e., increases of the execution cost and the number of tuning parameters, which are caused by the incorporated particle-clustering procedures.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Analysis of Gathering and Scattering Behavior Generated by Gravitational Particle Swarm Algorithm

Yoshida

2020

Transactions of ISCIE

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In this study, we propose a novel multimodal optimization algorithm, gravitational particle swarm algorithm (GPSA), by replacing the global feedback term of a classical particle swarm optimization with a term that introduces inverse-square gravitational force between particles. We analyze the search behavior of these particles by Monte-Carlo simulation, demonstrating that the particles often gather around the global optima but seldom scatter away from them. Furthermore, our GPSA's performance is evaluated on benchmark functions, showing that it found over 95.5% and 78.2% of the global optima over the course of multiple runs for two-and three-dimensional benchmark functions, respectively. At the same time, it found all global optima at least 62% and 5% of the separate runs, whereas the existing methods almost entirely failed to do.-16 -16

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“…Particle Swarm Optimization is a method of optimization with scanning the search space by a group of candidate solutions named particle, and these particles are suitable for parallelization. Particle Swarm Optimization is well parallelized by GPU computing [1][2][3][4][5][6] with application to computer sciences [7][8][9][10][11][12][13][14], finance [15,16], physics [17], biology [18], etc.…”

Section: Introductionmentioning

confidence: 99%

High-accuracy Optimization by Parallel Iterative Discrete Approximation and GPU Cluster Computing

Zhao¹

2014

JSW

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High-accuracy optimization is the key component of time-sensitive applications in computer sciences such as machine learning, and we develop single-GPU Iterative Discrete Approximation Monte Carlo Optimization (IDA-MCS) and multi-GPU IDA-MCS in our previous research. However, because of the memory capability constrain of GPUs in a workstation, single-GPU IDA-MCS and multi-GPU IDA-MCS may be in low performance or even functionless for optimization problems with complicated shapes such as large number of peaks. In this paper, by the novel idea of parallelizing Iterative Discrete Approximation with CUDA-MPI programming, we develop the GPU cluster version (GPU-cluster) of IDA-MCS with two different parallelization strategies: Domain Decomposition and Local Search, under the style of Single Instruction Multiple Data by CUDA 5.5 and MPICH2, and we exhibit the performance of GPU-cluster IDA-MCS by optimizing complicated cost functions. Computational results show that, by the same number of iterations, for the cost function with millions of peaks, the accuracy of GPU-cluster IDA-MCS is approximately thousands of times higher than that of the conventional method Monte Carlo Search. Computational results also show that, the optimization accuracy from Domain Decomposition IDA-MCS is much higher than that of Local Search IDA-MCS.

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An embedded architecture for real-time object detection in digital images based on niching particle swarm optimization

Cited by 12 publications

References 22 publications

Automatic Number Plate Recognition System (ANPR): A Survey

Automatic Number Plate Recognition System (ANPR): A Survey

Monte Carlo Analysis of Gathering and Scattering Behavior Generated by Gravitational Particle Swarm Algorithm

High-accuracy Optimization by Parallel Iterative Discrete Approximation and GPU Cluster Computing

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