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|Title: ||A Roughness-Based Matching Algorithm of Fractal Wavelet Coding for Side-Scan Sonar Images|
|Authors: ||FU-TAI WANG|
C.-Y. JENNY LEE
|Issue Date: ||2016-08-10T02:39:57Z
|Publisher: ||Mathematical and Computational Methods in Science and Engineering|
|Abstract: ||Abstract: Texture is regarded as a similarity grouped in an image. Efficiently extracting texture is a powerful method for classifying and identifying notable information in an area. The fractal dimension (FD) technique is a widely used texture analysis technique [1,2]. Roughness is a perceived image texture property, and is used to qualify image texture. FD technique is suitable for estimating roughness and has been successfully applied to quantitatively measure texture [4,5]. The researchers were motivated to conduct this study by the observation that an FD expresses an image through surface stability, and the image roughness distribution can be used to describe the texture information derived from an image, thereby enabling the FD texture features to be extracted. The roughness-based FD value describes the distribution of image roughness, enabling the feasible FD representing texture features to be extracted from the images.
This study proposes a texture-based fractal-wavelet (FW) coding algorithm based on the roughnessbased FD. The roughness-based FW algorithm uses the roughness-based FD to locate each range subtree for the optimal matched domain subtree according to the minimal texture similarity distances. The minimal similarity distance quantifies the degree of texture similarity between domain-range subtrees. Based on the roughness-based FW algorithm, conducting multiresolution frequency analysis on image texture is possible. Because the roughnessbased FDs of images in different frequencies can be received on various scales, texture information can be acquired in a horizontal, vertical, and angular direction, which is seldom achieved by using other texture analysis methods. The proposed matching algorithm relies on texture similarity, thus enabling the roughness-based FD algorithm to effectively determine the appropriate domain subtrees to successfully approximate the range subtrees and preserve the image texture information of an image after encoding.
This study is organized as follows. Section II presents an overview of fractal techniques and the fractal-wavelet image coding scheme. Section III introduces the proposed wavelet-fractal coding matching algorithm. Section IV includes the experimental results and conclusions. The experimental results are discussed using the measurements of mean square error (MSE) and peak signal-to-noise ratio (PSNR). The final section presents a brief conclusion.
|Appears in Collections:||[電機工程學系] 期刊論文|
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