37994970
Image coding quality assessment using fuzzy integrals with a three-component image model
Based on importance measures and fuzzy integrals, a new assessment method for image coding quality is presented in this paper. The proposed assessment is based on two subevaluations. In the first subevaluation, errors on edges, textures, and flat regions are computed individually. The errors are then assessed using an assessment function. A global evaluation with Sugeno fuzzy integral is then obtained based on the importance measure of edge, texture, and flat region. In the second subevaluation, an importance measure is first established depending on the types of regions where errors occur, a subtle evaluation is then obtained using Sugeno fuzzy integral on all pixels of the image. A final evaluation is obtained based on the two subevaluations. Experimental results show that this new image quality assessment closely approximates human subjective tests such as mean opinion score with a high correlation coefficient of 0.963, which is a significant improvement over peak signal-to-noise ratio, picture quality scale, and weighted mean square error, three other image coding quality assessment methods, which have the correlation coefficients of 0.821, 0.875, and 0.716, respectively.
7606399
A fuzzy image metric with application to fractal coding
Image quality assessment is an important issue addressed in various image processing applications such as image/video compression and image reconstruction. The peak signal-to-noise ratio (PSNR) with the L(2)-metric is commonly used in objective image quality assessment. However, the measure does not agree very well with the human visual perception in many cases. A fuzzy image metric (FIM) is defined based on Sugeno's (1977) fuzzy integral. This new objective image metric, which is to some extent a proper evaluation from the viewpoint of the judgment procedure, is closely approximates the subjective mean opinion score (MOS) with a correlation coefficient of about 0.94, as compared to 0.82 obtained using the PSNR. Compared to the L(2)-metric, we demonstrate that a better performance can be achieved in fractal coding by using the proposed FIM.
38592732
Remote Sensing Image Classification Based on Evidence Theory and Neural Networks
An effective method based on evidence theory and neural networks to classify the remote sensing images is brought forward in the paper. Firstly, with the spatial information in consideration, the original image is smoothed with a modified gradient inverse weighting smoothing method, then the classification of the original and smoothed images is performed separately using a B-P neural network. Finally, result comes out after fusing the two classification results with D-S evidence theory. Experimental results demonstrate that the proposed method is effective and can improve the classification accuracy.
15724966
A Semantic Web-based Architecture for Collaborative Multi-Agent Functional Modeling in Design
This paper presents a novel architecture for collaborative multi-agent functional modeling in design on the semantic Web. The proposed architecture is composed of two visiting levels, i.e., local level and global level. The local level is an ontology-based functional modeling framework, which uses Web ontology language (OWL) to build a domain-specific local functional design ontology repository. Using this local ontology repository, the requests coming from the functional design agent can be parsed and performed effectively. The global level is a distributed multi-agent collaborative virtual environment, in which, OWL is used as a content language within the standard FIPA agent communication language (FIPA ACL) messages for describing ontologies, enabling diverse functional design ontologies between different agents to be communicated freely. The proposed architecture facilitates the exchange between diverse knowledge representation schemes in different functional modeling environments, and supports computer supported cooperative work (CSCW) between multiple functional design agents
120992950
A mathematical model for bifurcations in a Belousov-Zhabotinsky reaction
Abstract In our investigation we find that there are three stages during the whole lifespan of a Belousov–Zhabotinsky (BZ) system with 1,4-cyclohexanedione (CHD) and ferroin: transitional period (TP), induction period (IP), and main period (MP). When the system goes from the TP to the IP, there is a bifurcation from monostability (orange steady-state) to bistability (both orange and blue steady-states). When the system shifts from the IP to the MP, a different bifurcation occurs from the bistability back to the monostability (orange steady-state). A reduction step before the second bifurcation propagates slower than oxidation steps and pulses during the whole lifespan of the medium. This sharp difference of propagation speed has not been observed in other BZ systems, and cannot be interpreted by the prototype Oregonator model. We construct here an Oregonator-type model that simulates the two bifurcations and the propagation speed difference of the two kinds of step before the second bifurcation. Furthermore, our model demonstrates other bifurcations that are not observable in experiment. Our discovery not only unfolds a new aspect of the well-studied BZ reaction, but also leads to a new demand for further understanding of a reaction–diffusion equation system as the corresponding ordinary differential equation system bifurcates.
17685609
Efficient multi-label classification with hypergraph regularization
Many computer vision applications, such as image classification and video indexing, are usually multi-label classification problems in which an instance can be assigned to more than one category. In this paper, we present a novel multi-label classification approach with hypergraph regularization that addresses the correlations among different categories. First, a hypergraph is constructed to capture the correlations among different categories, in which each vertex represents one training instance and each hyperedge for one category contains all the instances belonging to the same category. Then, an improved SVM like learning system incorporating the hypergraph regularization, called Rank-HLapSVM, is proposed to handle the multi-label classification problems. We find that the corresponding optimization problem can be efficiently solved by the dual coordinate descent method. Many promising experimental results on the real datasets including ImageCLEF and MediaMill demonstrate the effectiveness and efficiency of the proposed algorithm.
14149219
Multi-agent coordination: a fuzzy logic based approach
In the past decade, the multiagent systems have appeared as a new promising software paradigm. However, its succesful application is subject to the adoption of effective agent coordination techniques. In this paper, we consider a fuzzy logic based approach to agent coordination. Specifically, a bidding based coordination strategy has been proposed to manage the inter-relations among agents. The strategy is developed under the fuzzy logic based task structure model (FSTS) we proposed. Through using the strategy, agents can effectively coordinate their behaviors without consuming so much communication and computation resources.