Texture image segmentation using Vonn mixtures-based hidden Markov tree model and relative phase
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Texture image segmentation using Vonn mixtures-based hidden Markov tree model and relative phase Pan-pan Niu 1 & Li Wang 1 & Xin Shen 1 & Qian Wang 1 & Xiang-yang Wang 1 Received: 13 August 2019 / Revised: 1 July 2020 / Accepted: 29 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Texture segmentation is a frequently occurring and challenging problem in many computer vision and pattern recognition applications. The importance of phase information for texture analysis has been earlier established for many image processing. Undecimated dual tree complex wavelet transform (UDTCWT) is a new image decomposition. It not only provides exact translational invariance and rich directional selectivity, but also offers perfect consistent relative phase relationships across scales. In this paper, we propose a novel texture image segmentation framework using Vonn mixtures-based hidden Markov trees (HMT) and UDTCWT domain relative phase. Firstly, we analyze the robustness and marginal distribution of UDTCWT relative phases, and various strong dependencies between UDTCWT relative phases. Then, we propose a new HMT statistical model in UDTCWT domain, namely Vonn mixtures-based HMT, by describing the UDTCWT relative phases statistical distribution with Vonn mixtures (VM), which can capture both the subband marginal distributions and the strong dependencies across scales of the UDTCWT relative phases. Finally, we develop a texture image segmentation framework using the Vonn mixtures-based HMT model of UDTCWT domain relative phases, in which expectation– maximization (EM) parameter estimation, Bayesian multiscale raw segmentation, and context based multiscale fusion are used. Comparing to the state-of-the-art techniques, the proposed method can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results. Keywords Texture image segmentation . Undecimated dual tree complex wavelet transform . Relative phase . Vonn probability density function . Hidden Markov tree
* Pan-pan Niu [email protected] * Xiang-yang Wang [email protected]
1
School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, People’s Republic of China
Multimedia Tools and Applications
1 Introduction MAGE segmentation, an important technique in computer vision, aims to partition an image into several image segments based on certain properties, like homogeneous colors or similar textural patterns. Image segmentation is a fundamental step for many practical applications, such as object detection and recognition, stereo and motion estimation, content-based image retrieval, and so on. It proves to be extremely challenging due to the huge diversity and ambiguity of visual grouping patterns in natural scene images, in particular in presence of faint object boundaries and cluttered background [26]. Texture is an important visual feature that refers to innate surface properties of an object and its relationship to the sur
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