Texture segmentation:An algorithmic framework for integrating the analysis on multiple textural features
|關鍵字:||紋理;紋理分割;分群法;texture;texture segmentation;clustering method;co-occurrence matrix;Laws' texture energy;Gabor filter|
二、此架構以三階段的程序來處理像點的分類， (1)第一階段，在獨立單一的紋理特徵空間裡進行分群的工作。 (2)第二階段，利用第一階段所得到的分析結果，以一種合併的程序得到一個整體的分群結果，此結果遠優於單一紋理特徵的分群結果。 (3)最後的階段，根據第二階段的合併結果，我們以一種類似nearest-neighborhood 的方法在多維向量空間進行像點重新分類的工作，但此方法在計算時間上的代價遠低於傳統的方法。
Texture segmentation plays an important role in image analysis and computer vision. The major goal of texture segmentation is to partition an image into meaningful regions of homogeneous texture properties, i.e. pixels in the same region share similar texture attributes. Many different approaches have been proposed to extract texture features in decades .The performance of texture segmentation depends on not only texture feature extraction methods which discriminate texture variations but also segmentation algorithms which demarcate regions of homogeneous distribution of texture property. A three-phase algorithmic framework for texture segmentation is proposed in the thesis, with some innovation to be mentioned below. (1)It allows the embedding of texture extractors at users’ disposal. Any set of texture extractors demanded by concerns of various aspects can be fit into the first phase of the framework. (2)The classification is handled by a 3-staged process, (a) clustering in each individual feature space associated with each feature extractor in phase I of the framework, (b) followed by a merging process in phase II for exploiting any complementary behaviors exhibited among texture extractors in order to obtain an overall result of clustering much better than any of the individual clustering by single texture alone, and (c) in the final phase, the merged (and refined) clustering result from phase II is reclassified in a way similar to conventional nearest-neighborhood problem in multidimensional vector space but at much lower computational cost for the final refinement of the segmentation. The strength of the proposed framework for texture segmentation lies in that the difficulty of resolving both the determination cluster centroids (as representative for each class) and the clustering operation simultaneously, as encountered in most conventional clustering problem in multidimensional vector space, has been circumvented by the three-phased design in which potential cluster centroids are determined in phase two, followed by the final staged clustering in phase three. Further more, it is a straight forward one-pass segmentation process, as opposed to conventional ones which are mostly iterative in nature. The proposed framework has been tested against textured images of various kinds, with regular texture patterns and natural scenes, and demonstrates satisfactory performance.
|Appears in Collections:||Thesis|