Browsing by Author "Liu, C. -W."

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Accurate stereo matching algorithm based on cost aggregation with adaptive support weight
(2015-11-01)
Lin, C. -H.
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Liu, C. -W.
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資訊工程學系
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Department of Computer Science
The primary aim of this paper is to develop an accurate stereo matching algorithm based on cost aggregation with adaptive support weight (CAASW). In this study, we use a pair of images (from left and right cameras) to find corresponding points. First, the truncated absolute difference is represented as cost computing, and the cost aggregation is completed with adaptive support weight. The winner take all method is then used to find the minimum cost aggregation value of the location in order to obtain the initial disparity. In order to enhance the accuracy of this study, a disparity map is employed, which uses continuity for disparity neighboring relationships; the histogram is represented as a disparity refinement, making it possible to reduce the disparity map\'s errors. In this paper, the CAASW can be divided into two parts. The first part is CABSW, a method employing binary target and reference images with an area of intersection to form an irregular adaptive support window. The second part is CAASW, using similarity and proximity as features of an adaptive support window with CABSW. In order to better represent the accuracy of this method, the experiment uses the Middlebury database, in addition to other methods, for comparison and analysis, to explore the experimental results and to obtain results with a lower percentage of unsatisfactory matching pixels. Future research will explore applications of this method in robot navigation, industrial manufacturing, human interface, three-dimensional reconstruction and improved computer intelligence capabilities.
Image retrieval and classification using adaptive local binary patterns based on texture features
(2012-10-01)
Lin, C. -H.
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Liu, C. -W.
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Chen, H. -Y.
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資訊工程學系
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Department of Computer Science
In this study, adaptive local binary patterns (ALBP) are proposed for image retrieval and classification. ALBP are based on texture features for local binary patterns. The texture features were used to propose an adaptive local binary patterns histogram (ALBPH) and gradient for adaptive local binary patterns (GALBP) in this study. Two texture features are most useful for describing the relationship in a local neighbourhood. ALBPH shows the texture distribution of an image by identifying and employing the difference between the centre pixel and the neighbourhood pixel values. In the GALBP, the gradient for each pixel is computed and the sum of the gradient of the ALBP number is adopted as an image feature. In this study, a set of colour and greyscale images were used to generate a variety of image subsets. Then, image retrieval and classification experiments were carried out for analysis and comparison with other methods. From the experimental results, the authors discovered that the proposed feature extraction method can effectively describe the characteristics of images in regard to texture image and image type. The image retrieval and classification experiments also produced better results than other methods.