Microscopic halftone imagerecognition and analysis can provide quantitative evidence for printing quality control and fault diagnosis of printing devices, while halftone image segmentation is one of the significant s...
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Microscopic halftone imagerecognition and analysis can provide quantitative evidence for printing quality control and fault diagnosis of printing devices, while halftone image segmentation is one of the significant steps during the procedure. Automatic segmentation on microscopic dots by the aid of the Fuzzy C-Means (FCM) method that takes account of the fuzziness of halftone image and utilizes its color information adequately is realized. Then some examples show the technique effective and simple with better performance of noise immunity than some usual methods. In addition, the segmentation results obtained by the FCM in different color spaces are compared, which indicates that the method using the FCM in the f 1f 2f 3 color space is superior to the rest.
Approach for bilevel image restoration and reconstruction using a modified Hopfield neural network is proposed in this paper. A group of threshold update (TU) algorithms with respective to simultaneous, partially simu...
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In Hopfield neural network approach for bilevel image restoration the autoconnections of the network generally weight heavier than interconnections. This characteristic exists in general degradation models of image re...
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The feature contrast model (FCM), which is the simplest form of the matching function in Tversky's set-theoretic similarity, is a famous similarity model in psychological society. Although FCM can be employed to e...
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The feature contrast model (FCM), which is the simplest form of the matching function in Tversky's set-theoretic similarity, is a famous similarity model in psychological society. Although FCM can be employed to explain the similarity with both semantic and perceptual features, it is very difficult for FCM to measure natural image similarity with semantic features because of the requirement that all features must be binary and the complex mechanism that semantic features are transformed into binary features. The fuzzy feature contrast model (FFCM) is an extension of FCM, which replaces the complex feature representation mechanism with a proper fuzzy membership function. By this fuzzy logic, visual features, in the FFCM, can be represented as multidimensional points instead of expansible feature set and used to measure visual similarity between two images. Based on the analysis of the distinction between two feature structures (i.e., the expansible feature set and multidimensional vector), we propose a ratio model, which expresses similarity between two images as a ratio of the measures of semantic features set to that of multidimensional visual features. Experiments results, over real-world image collections, show that our model addresses the distinction between semantic and visual feature structures to some extension. In particular, our model is suit for the case that semantic features are implicitly obtained from interaction with users and the visual features are transparent for users, for example, the relevance feedback in interactive image retrieval.
The patternrecognition methods and a four-parameter model, based on extended Miede-ma’s cellular model of alloy phases, are used to study the regularities of formation of ternary compounds between two transition ele...
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The patternrecognition methods and a four-parameter model, based on extended Miede-ma’s cellular model of alloy phases, are used to study the regularities of formation of ternary compounds between two transition elements (T,T ’) and one non-transition element (N) (T-T ’-N system) . The criterion of formation can be expressed as some functions of φ (electronegativity), n1/3ws (valence electron density in Wagner-Seitz cell), R (Pauling’s metallic radii) and Z (number of valence electrons in atom).
In this paper, a novel subspace learning method, semi-supervised marginal discriminant analysis (SMDA), is proposed for classification. SMDA aims at maintaining the intrinsic neighborhood relations between the data po...
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We present a method for tracking deformable surfaces in 3D using a stereo rig. Different from traditional recursive tracking approaches that provide a strong prior on the pose for each new frame, the proposed method t...
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A multi-character recognition method based on hidden Markov model （HMM） was presented. The method can reduce the calculation load of correlation and improve recognition accuracy compared with singlecharacter recogni...
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A multi-character recognition method based on hidden Markov model （HMM） was presented. The method can reduce the calculation load of correlation and improve recognition accuracy compared with singlecharacter recognition in video. The characteristics used for recognizing include the shape character, the color character, the texture character and so on. Even our human being generally uses these characteristics to recognize objects in practice..4, recognition experiment of 17 fishes was carried out in the paper. The experimental results demonstrate the high veracity of the multi-character recognition algorithm. Together with the tracking process, it can handle dynamic objects, so the multi-character recognition is more like the human recognition, and has great application value.
This paper introduces a shape descriptor based on a combination of topological image analysis and texture information. Critical points of a shape’s skeleton are determined first. The shape is described according to p...
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Sparse representation has recently been proved to be a powerful tool in imageprocessing and object *** paper proposes a novel small target detection algorithm based on this *** modelling a small target as a linear co...
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Sparse representation has recently been proved to be a powerful tool in imageprocessing and object *** paper proposes a novel small target detection algorithm based on this *** modelling a small target as a linear combination of certain target samples and then solving a sparse 0-minimization problem,the proposed apporach successfully improves and optimizes the small target representation with ***,the sparsity concentration index（SCI） is creatively employed to evaluate the coefficients of each block representation and simpfy target *** the detection frame,target samples are firstly generated to constitute an over-complete dictionary matrix using Gaussian intensity model（GIM）,and then sparse model solvers are applied to finding sparse representation for each sub-image ***,SCI lexicographical evalution of the entire image incorparates with a simple threshold locate target *** effectiveness and robustness of the proposed algorithm are demonstrated by the exprimental results.
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