A Novel Method for Detection of Significant Areas with Low Contrast Boundaries of Images

Authors

  • S. Siva Jyothi  Research Scholar of Rayalaseema University, Kurnool, India
  • A. Rama Mohan Reddy  Professor, Department of CSE, S.V.University, Tirupathi, India

Keywords:

Helly-Property, Segmentation, Texture model, Neighborhood Spanning Tree, BRF, Markov-Chain-Rule, Edge-Flow, Fuzzy-Logic and Boundary MRF model

Abstract

The problem of Segmenting Gray scale still images has been addressed in this work and proposed new methods by generating random field for image segmentation and boundary detection for image classification. The present work describes image segmentation at multiple scales. The detected regions are homogeneous and surrounded by closed edge boundaries. Segmentations yield texture and boundary information. Boundary information requires much more effort than texture information. The proposed techniques rely on boundary, textured and non-textured information for image segmentation at multiple scales. The definition of a general purpose segmentation technique has been revealed as being a rather complicated task. This complication is owing to the huge amount of different kind of data that a segmentation technique may have to handle. Previous approaches to multistage segmentation represented an image at different scales using a scale space. However, structure is only represented implicitly in this representation, structures at coarser scales are inherently smoothed, and the problem of structure extraction on is unaddressed. This work argues that the issue of scale selection and structure detection can not be treated separately. A new concept of scale will be presented that represents images structures at different scales, and the image itself. This scale is integrated into a non-linear transform, which makes structure explicit in the transformed domain. Structures that are stable to changes in scale are identified as being perceptually relevant, the transform can be viewed as collecting spatially distributed evidence for edges and regions, and making it available at contour locations there by facilitating integrated detection of edge and regions without restrictive models of geometry or homogeneity. Markov random field theory has been widely applied to the challenging problem of image segmentation. Image segmentation is a task that classifies pixels of an image using different labels so that the image is partitioned into non-overlapping labeled regions. Extraction of regions or objects of interest is usually the first important step in almost every task of image processing and high level image analysis for better understanding. Although it is fundamental, image segmentation a is field in which researchers are facing challenges because most of the real objects have complex shapes, boundaries and true images are often corrupted by noise that cannot be ignored. To tackle the difficult problem of image segmentation, researchers have proposed a variety of methods. In this thesis three textured models have been studied and proposed new methods under these models.

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S. Siva Jyothi, A. Rama Mohan Reddy, " A Novel Method for Detection of Significant Areas with Low Contrast Boundaries of Images, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.2006-2019, March-April-2018.