Colour-texture image segmentation based on graph cut using student's t distribution
01 Aug 2016-pp 1-6
TL;DR: The proposed work uses the different type of procedures has been followed to carry out colour-texture image segmentation to integrate more feature information, with high accuracy and satisfactory visual entirety.
Abstract: Image segmentation for analysis is a major aspect of perception and till date it is challenging issue for machine perception. Many years of study in computer vision prove that segmenting an image into meaningful regions for subsequent processing (e.g., pattern recognition) is just as hard problem as invariant pattern recognition. In this paper, the proposed work uses the different type of procedures has been followed to carry out colour-texture image segmentation. Segmentation methods are designed to integrate more feature information, with high accuracy and satisfactory visual entirety. The segmentation process is based on MSST and student's t-distribution method.
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24 Jan 2018
TL;DR: The proposed one uses the particular sort of frameworks had been taken after to complete shading surface picture division, and is intended to incorporate more component data, with high exactness and agreeable visual total.
Abstract: The Image segmentation is that for investigation is a noteworthy part of discernment and up to date it is still testing issue for machine recognition. Numerous times of concentrate in PC view demonstrate that dividing a picture into important districts for ensuing preparing (e.g., design acknowledgment) is similarly as troublesome issue as never changing case identification. In this paper work, the proposed one uses the particular sort of frameworks had been taken after to complete shading surface picture division. Division strategies are intended to incorporate more component data, with high exactness and agreeable visual total. The division procedure depends on MSST and understudy’s t-conveyance technique.
References
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07 Jul 2001
TL;DR: In this paper, the authors present a database containing ground truth segmentations produced by humans for images of a wide variety of natural scenes, and define an error measure which quantifies the consistency between segmentations of differing granularities.
Abstract: This paper presents a database containing 'ground truth' segmentations produced by humans for images of a wide variety of natural scenes. We define an error measure which quantifies the consistency between segmentations of differing granularities and find that different human segmentations of the same image are highly consistent. Use of this dataset is demonstrated in two applications: (1) evaluating the performance of segmentation algorithms and (2) measuring probability distributions associated with Gestalt grouping factors as well as statistics of image region properties.
6,505 citations
TL;DR: The focus of this work is on spatial segmentation, where a criterion for "good" segmentation using the class-map is proposed and applying the criterion to local windows in theclass-map results in the "J-image," in which high and low values correspond to possible boundaries and interiors of color-texture regions.
Abstract: A method for unsupervised segmentation of color-texture regions in images and video is presented. This method, which we refer to as JSEG, consists of two independent steps: color quantization and spatial segmentation. In the first step, colors in the image are quantized to several representative classes that can be used to differentiate regions in the image. The image pixels are then replaced by their corresponding color class labels, thus forming a class-map of the image. The focus of this work is on spatial segmentation, where a criterion for "good" segmentation using the class-map is proposed. Applying the criterion to local windows in the class-map results in the "J-image," in which high and low values correspond to possible boundaries and interiors of color-texture regions. A region growing method is then used to segment the image based on the multiscale J-images. A similar approach is applied to video sequences. An additional region tracking scheme is embedded into the region growing process to achieve consistent segmentation and tracking results, even for scenes with nonrigid object motion. Experiments show the robustness of the JSEG algorithm on real images and video.
1,476 citations
TL;DR: The use of the ECM algorithm to fit this t mixture model is described and examples of its use are given in the context of clustering multivariate data in the presence of atypical observations in the form of background noise.
Abstract: Normal mixture models are being increasingly used to model the distributions of a wide variety of random phenomena and to cluster sets of continuous multivariate data. However, for a set of data containing a group or groups of observations with longer than normal tails or atypical observations, the use of normal components may unduly affect the fit of the mixture model. In this paper, we consider a more robust approach by modelling the data by a mixture of t distributions. The use of the ECM algorithm to fit this t mixture model is described and examples of its use are given in the context of clustering multivariate data in the presence of atypical observations in the form of background noise.
903 citations
17 Jun 1997
TL;DR: A novel boundary detection scheme based on "edge flow" that utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector.
Abstract: A novel boundary detection scheme based on "edge flow" is proposed in this paper. This scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector. By iteratively propagating the edge flow, the boundaries can be detected at image locations which encounter two opposite directions of flow in the stable state. A user defined image scale is the only significant control parameter that is needed by the algorithm. The scheme facilitates integration of color and texture into a single framework for boundary detection.
258 citations