An efficient robust watermarking scheme for digital images is presented, based on the discrete Gabor transforms (DGT), The watermark in digital images is hidden by changing their DGT coefficients to represent special ...
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An efficient robust watermarking scheme for digital images is presented, based on the discrete Gabor transforms (DGT), The watermark in digital images is hidden by changing their DGT coefficients to represent special low frequencies. In the proposed watermark-checking algorithm, the geometric transforms and corresponding matching method are applied for geometric transformed images in order to recover them for watermark-checking. The proposed scheme was tested by StirMark 4.0, a well-known software for testing watermarking schemes, and it performed successfully in almost all testing cases with high robustness and efficiency.
Based on fuzzy association degree, a new pattern recognition algorithm is set up. First, some new concepts of fuzzy association coefficient (FAC), fuzzy association degree (FAD) and fuzzy relative weight (FRW) have be...
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Based on fuzzy association degree, a new pattern recognition algorithm is set up. First, some new concepts of fuzzy association coefficient (FAC), fuzzy association degree (FAD) and fuzzy relative weight (FRW) have been proposed for surveying data information. Second, on the basis of the concepts proposed here, a new pattern recognition algorithm has been set up. At last, the algorithm set up here is applied to surveying data. The results of simulation application show that the recognition algorithm presented here is feasible and effective
The key issue of peer data management systems (PDMSs) is how to efficiently organize and manage distributed resources in P2P networks to accurately route queries from the peer initiating the query to appropriate peers...
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The key issue of peer data management systems (PDMSs) is how to efficiently organize and manage distributed resources in P2P networks to accurately route queries from the peer initiating the query to appropriate peers to avoid network flooding. This paper proposes a semantic-based PDMS model, called R-Chord, by deploying the resource space model above the Chord overlay for uniformly, normally and effectively organizing and managing resources distributed in P2P networks. Experiments show that, compared to the Chord model, the R-Chord model is more flexible to support semantic-based queries and can significantly decrease the average visiting number of and visiting times on peers for answering queries.
Gait is an identifying biometric feature. In recent years, Video-based gait recognition is becoming a new challenging problem in the field of computer vision. In this paper, wavelet reflective symmetry moments (WRSMs)...
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Gait is an identifying biometric feature. In recent years, Video-based gait recognition is becoming a new challenging problem in the field of computer vision. In this paper, wavelet reflective symmetry moments (WRSMs) have been proposed to describe and recognize gait automatically. WRSMs represent the appearance of people with merits of moments and wavelet analysis and reflect people's symmetrical walking habit. Moments have translation, scale and rotation invariant characteristics;while wavelet analysis is able to extract the multi-resolution features subtly and deal with noise. So combination of wavelet moments and reflective symmetry not only has characteristics of moments and wavelet analysis, but also is in accordance with one of the relative results in psychological researches which state that gait is a type of symmetrical model. Experiments based on USF's database have shown that the application of wavelet reflective symmetry moments in gait recognition leads to relatively high distinguishability of gait with effective noise handling.
A formal representation of ontologies is proposed, based on F-logic and O-logic; and the works in the building of ontologies in NKI. An ontology includes class frames, slot frames, class-slot frames, object frames and...
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A formal representation of ontologies is proposed, based on F-logic and O-logic; and the works in the building of ontologies in NKI. An ontology includes class frames, slot frames, class-slot frames, object frames and axioms. The value restrictions of slots are defined in slot frames. For each slot and each class, there is a class-slot frame representing the specific value restrictions of the slot when defining the class; and the relations between class-slot frames and slot frames are discussed. For a slot in a class frame, its values are inherited to its subclasses without blocking; and its default values are inherited to its subclasses taking overriding, revising and conflict resolution into account. After giving the formal representation of ontologies, the semantics of ontologies are discussed, and main results are presented.
A knowledge flow is invisible but it plays an important role in ordering knowledge exchange in teamwork. It can help achieve effective team knowledge management by modeling, optimizing, monitoring and controlling the ...
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A knowledge flow is invisible but it plays an important role in ordering knowledge exchange in teamwork. It can help achieve effective team knowledge management by modeling, optimizing, monitoring and controlling the operation of knowledge flow processes. This paper proposes the notion of knowledge energy as the driving cause of forming an autonomous knowledge flow network, and explores the behind principles. Knowing these principles can help team managers and the support systems improve cooperation by monitoring the knowledge energy of nodes, and by evaluating and adjusting knowledge flows. A knowledge flow network management system can be the high layer of the knowledge grid to help improve the efficiency of distributed knowledge-intensive teamwork.
Feature extraction or selection is one of the most important steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propose...
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Feature extraction or selection is one of the most important steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propose a new concept of probability information distance (PID) and prove that the PID satisfies four requests of axiomatization of the distance. So the PID is a kind of distance measure, which can be used to measure the degree of variation between two random variables. We make the PID be separability criterion of the classes for information feature extraction, and call it PID criterion (PIDC). Based on PIDC, we design a novel algorithm for information feature extraction. Compared with principal components analysis (PCA), correlation analysis etc., the algorithm put forward in this paper had regarded for the class information, and so it is a kind of supervised algorithm of feature extraction. The experimental results demonstrate that the algorithm is valid and reliable, and it provides a new research approach for feature extraction, data mining and pattern recognition.
Principal component analysis (PCA) is an important method in multivariate statistical analysis, and its main idea is compression of dimensionality including variables and samples. In this paper, based on the ideas con...
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Principal component analysis (PCA) is an important method in multivariate statistical analysis, and its main idea is compression of dimensionality including variables and samples. In this paper, based on the ideas concerned with information function and information entropy of Shannon information theory, consider the inherent characteristic of eigenvalues of matrix, two new concepts of possibility information function (PIF) and possibility information entropy (PIE) are proposed firstly. On the basis of these, the formulae of information rate (IR) and accumulated information rate (AIR) are set up, by which the degree of information compression is measured. In the end, we improve the PCA algorithm called improved principal component analysis (PCA). Through simulated application in practice, the results show that the IPCA proposed here is efficient and satisfactory. It provides a new research approach of information feature compression for pattern recognition.
In this paper, we present a brief summary to 3D mesh model segmentation techniques, including definition, latest achievements, classification and application in this field. Then evaluations on some of typical methods,...
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In this paper, we present a brief summary to 3D mesh model segmentation techniques, including definition, latest achievements, classification and application in this field. Then evaluations on some of typical methods, such as Watershed, topological and geometrical method, are introduced. After some applications are presented, problems and prospect of the techniques are also discussed.
This paper presents a novel segmentation method based on a non-parametric background model that has the ability of modeling multi-model. Firstly, both the intensity and edge features are used to improve robustness of ...
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This paper presents a novel segmentation method based on a non-parametric background model that has the ability of modeling multi-model. Firstly, both the intensity and edge features are used to improve robustness of the foreground detection. Secondly, we also present an adaptive shadow detection model to find the accurate moving objects. The experiment results show that our proposed method is effective.
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