Fast method for face location and tracking by distributed behaviour-based agents

Wang, Y.; Yuan, Baozong

doi:10.1049/ip-vis:20020395

Cited by 16 publications

(13 citation statements)

References 15 publications

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“…Based on this idea, many agent-based applications are reported during past years, such as image feature extraction [17], image segmentation [18], and optimization problems [19–24]. In our previous work [25, 26], we also proposed an evolutionary agent model for color-based face detection and location. …”

Section: Memory-based Multiagent Coevolution Modelingmentioning

confidence: 99%

Memory‐Based Multiagent Coevolution Modeling for Robust Moving Object Tracking

Wang

2013

The Scientific World Journal

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The three-stage human brain memory model is incorporated into a multiagent coevolutionary process for finding the best match of the appearance of an object, and a memory-based multiagent coevolution algorithm for robust tracking the moving objects is presented in this paper. Each agent can remember, retrieve, or forget the appearance of the object through its own memory system by its own experience. A number of such memory-based agents are randomly distributed nearby the located object region and then mapped onto a 2D lattice-like environment for predicting the new location of the object by their coevolutionary behaviors, such as competition, recombination, and migration. Experimental results show that the proposed method can deal with large appearance changes and heavy occlusions when tracking a moving object. It can locate the correct object after the appearance changed or the occlusion recovered and outperforms the traditional particle filter-based tracking methods.

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Section: Memory-based Multiagent Coevolution Modelingmentioning

confidence: 99%

Memory‐Based Multiagent Coevolution Modeling for Robust Moving Object Tracking

Wang

2013

The Scientific World Journal

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“…These methods have a groundwork based on fundamental image segmentation method; some researchers [4,[9][10][11] used splitand-merge method for basic framework after that agents which has been distributed within image by using their propertied, segment the given image. These researchers could be achieved good result via their methods; in addition they could decrease some limitations in propounded problem.…”

Section: Review Of Other Workmentioning

confidence: 99%

A Multi-agent System Approach for Medical Image Segmentation

Chitsaz

Seng

2009

2009 International Conference on Future Computer and Communication

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Image segmentation still requires improvements although there have been research works since the last few decades. This is coming due to some issues. Firstly, most image segmentation solutions are problem-based. Secondly, medical image segmentation methods generally have restrictions because medical images have very similar gray level and texture among the interested objects. The goal of this work is to design a framework to extract simultaneously several objects of interest from Computed Tomography (CT) images by using some priori-knowledge. Our method used properties of agent in a multi-agent environment. The input image is divided into several sub-images, and each agent works on a sub-image and tries to mark each pixel as a specific region by means of given priori-knowledge. During this time the local agent marks each cell of sub-image individually. Moderator agent checks the outcome of all agents' work to produce final segmented image. The experimental results for cranial CT images demonstrated segmentation accuracy around 90%.

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“…Therefore, we choose the skin-color model which combines these two models as follows [5] The skin-color binary image can be obtained by this skin-color model. (4) If , the current velocity and position of particle i are updated by formula (3) and (4):…”

Section: Skin Color Modelmentioning

confidence: 99%

Face detection based on pso and neural network

Wang¹,

Li²,

Suo³

2008

2008 9th International Conference on Signal Processing

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This paper presents a novel method to search face candidate regions in color images. First, a skin-color model is used to get the skin regions. Then, particle swarm optimization (PSO) is utilized for searching face candidate regions, which can save time and eliminate small noises. Experimental results show that this searching method is super to the conventional method which scans the whole image pixel by pixel. Finally, BP neural network is used to verify the face. The output error formula is modified to make the neural network converge more quickly. Bootstrap method is utilized to choose the training samples for the network, which reduces the correlation between samples and improves the detection effects. This approach is robust and can achieve high detection rate when detecting frontal faces, a little rotated and profile faces. In addition, it can detect faces with different size, expression, as well as having glasses and beard.

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Fast method for face location and tracking by distributed behaviour-based agents

Cited by 16 publications

References 15 publications

Memory‐Based Multiagent Coevolution Modeling for Robust Moving Object Tracking

Memory‐Based Multiagent Coevolution Modeling for Robust Moving Object Tracking

A Multi-agent System Approach for Medical Image Segmentation

Face detection based on pso and neural network

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