Weighted Color Co-occurrence Matrix (WCCM) is introduced as a novel feature for image retrieval. When indexing images with WCCM feature, the similarities of diagonal elements and non-diagonal elements are weighted res...
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Kernel principal component analysis (KPCA) as a powerful nonlinear feature extraction method has proven as a preprocessing step for classification algorithm. A face recognition approach based on KPCA and genetic algor...
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There are growing concerns on the potential side effect of radiation, which could be decreased by lowering the tube current. However, this manner will lead to a degraded image since X-ray imaging is a quantum accumula...
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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 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.
Despite the fact that many character datasets for several languages are publicly available, there are only a very few standardized datasets for Tamil characters. This article presents a subset of the Mepco Tamil Chara...
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Aiming at the effective approximation of sampling particle set relative to system state in observation uncertainty, a novel cost reference particle filter based on adaptive particle swarm optimization is proposed. In ...
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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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