Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation...
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Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation between neighboring pixels can be used to achieve high levels of detection accuracy in the presence of dynamic background. However, color similarity between foreground and background will cause many foreground pixels to be misclassified. In this paper, an adaptive foreground model is exploited to detect moving objects in dynamic scenes. The foreground model provides an effective description of foreground by adaptively combining the temporal persistence and spatial coherence of moving objects. Building on the advantages of MAP-MRF (the maximum a posteriori in the Markov random field) decision framework, the proposed method performs well in addressing the challenging problem of missed detection caused by similarity in color between foreground and background pixels. Experimental results on real dynamic scenes show that the proposed method is robust and efficient.
In this study we focus on the problem of segmentation and visualization of soft tissue structures in three-dimensional (3D) magnetic resonance (MR) imaging. We introduce a classification method which is a combination ...
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In this study we focus on the problem of segmentation and visualization of soft tissue structures in three-dimensional (3D) magnetic resonance (MR) imaging. We introduce a classification method which is a combination of a recently proposed contour detection algorithm and Haslett's contextual classification method extended to 3D. This classification method is used in the classification step of a rendering model suggested by Drebin et al. for visualizing normal and pathological tissue structures in the brain. We evaluate the combination of these two methodologies, and identify some problems which have to be solved in order to develop a clinical useful tool.
Four parameters, φ (electronegativity), nws1/3 (valence electron density in Wagner-Seitz cell),R (Pauling's metallic radius) and Z (number of valence electrons in atom), and the patternrecognition methods were u...
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Four parameters, φ (electronegativity), nws1/3 (valence electron density in Wagner-Seitz cell),R (Pauling's metallic radius) and Z (number of valence electrons in atom), and the patternrecognition methods were used to investigate the regularities of formation of ternary intermetallic compounds between three transition elements. The obtained mathematical model expressed by some inequalities can be used as a criterion of ternary compound formation in "unknown" phase diagrams of alloy systems.
Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. An improved representation called Local Differenc...
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Reasoning about action is an important aspect of common sense reasoning and planning. It gives rise to three classical problems: the frame problem,the qualification problem and the ramification problem. Ekisting appro...
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Reasoning about action is an important aspect of common sense reasoning and planning. It gives rise to three classical problems: the frame problem,the qualification problem and the ramification problem. Ekisting approaches cannot deal with these problems efficiently. This paper presents a new method which uses the stratified ATMS for reasoning about action to overcome the limitations of these approaches.
Human's real life is within a colorful world. Compared to the gray images, color images contain more information and have better visual effects. In today's digital imageprocessing, image segmentation is an im...
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Human's real life is within a colorful world. Compared to the gray images, color images contain more information and have better visual effects. In today's digital imageprocessing, image segmentation is an important section for computers to "understand" images and edge detection is always one of the most important methods in the field of image segmentation. Edges in color images are considered as local discontinuities both in color and spatial domains. Despite the intensive study based on integration of single-channel edge detection results, and on vector space analysis, edge detection in color images remains as a challenging issue.
Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as t...
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A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by an...
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A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by analyzing the face shapes from different people under varying poses. The shape alignment is vital in the process of building the statistical model. Then, six trigonometric functions are employed to represent the face pose parameters. Lastly, the mapping function is constructed between face image and face pose by linearly relating different parameters. The proposed approach is able to estimate different face poses using a few face training samples. Experimental results are provided to demonstrate its efficiency and accuracy.
A novel latent semantic indexing （LSI） approach for content-based image retrieval is presented in this paper. Firstly, an extension of non-negative matrix factorization （NMF） to supervised initialization is discus...
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A novel latent semantic indexing （LSI） approach for content-based image retrieval is presented in this paper. Firstly, an extension of non-negative matrix factorization （NMF） to supervised initialization is discussed. Then, supervised NMF is used in LSI to find the relationships between low-level features and high-level semantics. The retrieved results are compared with other approaches and a good performance is obtained.
This paper presents a novel approach that leverages two models to integrate features from numerous unlabeled images, addressing the challenge of semi-supervised salient object detection (SSOD). Unlike conventional met...
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