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Unsupervised segmenting region of interest in images is very useful in content-based application such as image indexing for content-based retrieval and target recognition. The proposed method applies fuzzy theory to separate the salient region of interest from background in low depth of field (DOF) images automatically. First the image is divided into regions based on mean shift method and the regions...
Feature selection is an effective data preprocessing step to reduce the dimension of feature space and save storage space. Binary particle swarm optimization (BPSO) has been applied successfully to solve feature selection problem. But it was easy to fall into local optimal point. M2BPSO was an improved BPSO algorithm. The particles of M2BPSO were updated by using various evolutionary strategies according...
A major problem in content-based image retrieval is the unsupervised identification of perceptually salient regions of interest in images. This paper proposed a scheme for automatic extracting the salient regions of interest in images. In this scheme, first classify images to two different groups according to their wavelet modulus maxima point densities, and then extract the salient regions of interest...
This paper propose a novel algorithm, the trust region embedded particle filter (TREPF), for target tracking in infrared imagery. Trust regions and particle filters are two successful methods for object tracking. The presented TREPF algorithm integrates the advantages of the two approaches. Contrasting the original particle filters and trust regions, the new algorithm can maintain multiple hypotheses...
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