Browsing by Author "Lee, KL"

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A new method for extracting primitives of regular textures based on wavelet transform
(2002-02-01)
Lee, KL
;
Chen, LH
;
資訊工程學系
;
Department of Computer Science
In this paper, a new method for extracting primitives of a regular texture via wavelet transform is provided. Each primitive is restricted to be a parallelogram. The main work of the proposed method is to extract the two displacement vectors that form the primitive of a regular texture. A contrast-based criterion is used to select appropriate subimages obtained from wavelet transform for detecting displacement vectors. An edge thresholding method is then performed on the subimages selected to locate edges. Based on these edges, Hough transform is applied to extract the displacement vectors. The proposed method is quite efficient and can get more accurate results for displacement vectors when comparing to traditional co-occurrence matrix based methods. Synthesized textures are provided to show the effectiveness of the proposed method.
An efficient computation method for the texture browsing descriptor of MPEG-7
(2005-05-01)
Lee, KL
;
Chen, LH
;
資訊工程學系
;
Department of Computer Science
In this paper, an efficient computation method for computing the texture browsing descriptor of MPEG-7 is provided. Texture browsing descriptor is used to characterize a texture's regularity, directionality and coarseness. To compute the regularity of textures, Fourier transform is first performed. To get more discriminative features for regularity computation, the Fourier spectrum is treated as an image and the Fourier transform is performed again to produce an enhanced Fourier spectrum. A regularity measure based on the variance of the radial wedge distribution is then calculated to determine the regularity of textures. For regular textures, the texture primitives are assumed to be parallelograms, the two dominant directions are extracted by Hough transform. A scale computation method is then provided to determine the scales corresponding to the two dominant directions. In addition, principal component analysis is provided to detect textures with only one dominant direction. Experiments of texture browsing, coarse classification of textures and similarity-based image-to-image matching are performed on the texture images of Brodatz album and Corel Gallery image database to demonstrate the efficiency and effectiveness of the proposed method. The proposed method can be used in the applications of texture browsing and texture retrieval. (c) 2005 Elsevier B.V. All rights reserved.
Unsupervised texture segmentation by determining the interior of texture regions based on wavelet transform
(2001-12-01)
Lee, KL
;
Chen, LH
;
資訊工程學系
;
Department of Computer Science
Traditional approaches for texture segmentation via wavelet transform usually adopt textural features to achieve segmentation purposes. However, for a natural image, the characteristics of the pixels in a texture region are not similar everywhere from a global viewpoint, and over-segmentation often occurs. To deal with this issue, an unsupervised texture segmentation method based on determining the interior of texture regions is proposed. The key idea of the proposed method is that if the pixels of the input image can be classified into interior pixels (pixels within a texture region) and boundary ones, then the segmentation can be achieved by applying region growing on the interior pixels and reclassifying boundary pixels. Based on the fact that each pixel P within a texture region will have similar characteristics with its neighbors, after applying wavelet transform, pixel P will have similar response with its neighbors in each transformed subimage. Thus, by applying a multilevel thresholding technique to segment each subimage into several regions, pixel P and its neighbors will be assigned to the same region in most subimages. Based on these segmented results, an interior pixels finding algorithm is then provided to find all interior pixels of textural regions. The algorithm considers a pixel which is in the same region as its neighbors in most subimages as an interior pixel. The effectiveness of this method is proved by successfully segmenting natural texture images and comparing with other methods.