This paper presents a three-step framework to remove the highlight exists on objects in certain conditions. Unlike traditional HDR (High Dynamic Range) technology requires multiple registrated image sequence;our metho...
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Despite the significantly practical utilities of interior tomography, it still suffers from severe degradation of direct current (DC) shift artifact. Existing literature suggest to introducing prior information of obj...
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Non-photorealistic rendering (NPR) such as stylization has received much concern in area of computer graphics and imageprocessing. In this paper, a simple and effective method is proposed for streamline stylization. ...
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A method for zip code recognition is presented. 2D binary images are input to HAVNET, a neural network which employs the Hausdorff distance as a similarity metric to train the weights which are required to represent t...
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An efficient framework utilizing both local features and geometrical distribution for detecting facial components is presented. First, candidate facial components are efficiently collected by cascaded boosting of Haar...
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In this paper, we describe an experimental investigation to evaluate the significance of different facial regions of a person in the task of gender classification. For this purpose we use a Support Vector Machine (SVM...
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Segmentation of connected handwritten Chinese characters is a very difficult task in document image analysis. In this paper, a novel algorithm based on stroke analysis and background thinning is proposed to segment co...
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CT scans are an exceptional tool for quantitative airway analysis. Due to the complex voxel connectivity and branch structure of the airway, precise and fine segmentation results are difficult to accomplish. We propos...
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The software systems which are related to national science and technology projects are very crucial. This kind of systems always involves high technical factors and has to spend a large amount of money, so the quality...
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The software systems which are related to national science and technology projects are very crucial. This kind of systems always involves high technical factors and has to spend a large amount of money, so the quality and reliability of the software deserve to be further studied. Hence, we propose to apply four intelligent classification techniques most used in data mining fields, including Bayesian belief networks (BBN), nearest neighbor (NN), rough set (RS) and decision tree (DT), to validate the usefulness of software metrics for risk prediction. Results show that comparing with metrics such as Lines of code (LOC) and Cyclomatic complexity (V(G)) which are traditionally used for risk prediction, Halstead program difficulty (D), Number of executable statements (EXEC) and Halstead program volume (V) are the more effective metrics as risk predictors. By analyzing obtained results we also found that BBN was more effective than the other three methods in risk prediction.
We present a method for recovering 3D shapes of deformable surfaces from monocular image sequences. State-of-the-art methods on this problem [1] utilize L∞-norm of reprojection residual vectors and formulate the trac...
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