Particle Swarm Optimization for Object Recognition in Computer Vision

Perlin, Hugo Alberto; Lopes, Heitor Silvério; Centeno, Tania Mezzadri

doi:10.1007/978-3-540-69052-8_2

Cited by 13 publications

(20 citation statements)

References 4 publications

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“…The authors suggest, in fairly descriptive terms, that the PSO part of their framework could be regarded as multi-layer importance sampling, although the exact relationship between importance sampling and PSO has not yet been completely analyzed; we offer some observations in Section 3.3. Anton-Canalis et al [37] and Kobayashi et al [39] are other examples of work in which PSO has been applied to non-articulated object tracking.…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge, ours is the first application of PSO to articulated human body tracking. Perlin et al [37] adopt PSO for object recognition. Zhang et al [38] report an application of a variant of PSO, called sequential PSO, to box tracking in video sequences.…”

Section: Related Workmentioning

confidence: 99%

“…The research on PSO convergence is still very much ongoing and the latest results by Poli [44] analyse the convergence behaviour of a stochastic PSO system under stagnation and give full account of the PSO sampling distribution, modelling PSO search behaviour. A number of experimental studies demonstrating the power of PSO search on specific problems have also been published recently [37][38][39].…”

Section: Convergencementioning

confidence: 99%

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Markerless human articulated tracking using hierarchical particle swarm optimisation

John

Trucco

Iveković

2010

Image and Vision Computing

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Convergencementioning

confidence: 99%

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Markerless human articulated tracking using hierarchical particle swarm optimisation

John

Trucco

Iveković

2010

Image and Vision Computing

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“…During the optimization process, the population of solutions can converge to a sub-optimal region in search space and result in stagnation of the best solution for a certain number of cycles continuously. When stagnation occurs, in order to restart the search for the best solution, an explosion procedure is applied for the object recognition problem [24]. The explosion procedure generally aids the algorithm to search in different regions of search space and to find the best solution gradually during the iterations.…”

Section: Improved Abc Algorithm (Iabc)mentioning

confidence: 99%

“…Metaheuristic optimization algorithms, such as those from the Swarm Intelligence area, were successfully applied to face recognition problems. Several optimization algorithms have been successfully applied to face recognition purposes, such as Particle Swarm Optimization (PSO) [23], [24] and Artificial Bee Colony (ABC) [25] algorithms. Face detection and recognition constitute problems in which optimization algorithms have great potential to improve detection or recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multiple Face Recognition Using Local Features and Swarm Intelligence

Chidambaram

Neto

Dorini

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYFace recognition plays an important role in security applications, but in real-world conditions face images are typically subject to issues that compromise recognition performance, such as geometric transformations, occlusions and changes in illumination. Most face detection and recognition works to date deal with single face images using global features and supervised learning. Differently from that context, here we propose a multiple face recognition approach based on local features which does not rely on supervised learning. In order to deal with multiple face images under varying conditions, the extraction of invariant and discriminative local features is achieved by using the SURF (Speeded-Up Robust Features) approach, and the search for regions from which optimal features can be extracted is done by an improved ABC (Artificial Bee Colony) algorithm. Thresholds and parameters for SURF and improved ABC algorithms are determined experimentally. The approach was extensively assessed on 99 different still images -more than 400 trials were conducted using 20 target face images and still images under different acquisition conditions. Results show that our approach is promising for real-world face recognition applications concerning different acquisition conditions and transformations.

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A comparison of swarm intelligence algorithms for structural engineering optimization

Parpinelli

Teodoro

Lopes

2012

Numerical Meth Engineering

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SUMMARY This paper compares the performance of three swarm intelligence algorithms for the optimization of hard engineering problems. The algorithms tested were bacterial foraging optimization (BFO), particle swarm optimization (PSO), and artificial bee colony (ABC). Besides the regular BFO, two other variants reported in the literature were also included in the study: adaptive BFO and swarming BFO. Both PSO and ABC were tested using the regular algorithm and variants that include explosion (mass extinction). Three optimization problems of structural engineering were used: minimization of the cost of a welded beam, minimization of the construction cost of a pressure vessel, and minimization of the total weight of a 10‐bar plane truss. All problems are strongly constrained. The algorithms were evaluated using two criteria: quality of solutions and the number of function evaluations. The results show that PSO presented the best balance between these two criteria. For the optimization problems approached in this paper, we can also conclude that the explosion procedure resulted in no significant improvements. Copyright © 2012 John Wiley & Sons, Ltd.

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Particle Swarm Optimization for Object Recognition in Computer Vision

Cited by 13 publications

References 4 publications

Markerless human articulated tracking using hierarchical particle swarm optimisation

Markerless human articulated tracking using hierarchical particle swarm optimisation

Multiple Face Recognition Using Local Features and Swarm Intelligence

A comparison of swarm intelligence algorithms for structural engineering optimization

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