基于粗糙集理论的目标边界检测

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI:10.1109/NCVPRIPG.2013.6776259

Ashish Phophalia, S. Mitra, Ajit Rajwade

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引用次数: 3

摘要

提出了一种基于粗糙集理论的封闭形式目标边界检测方法。大多数边缘检测方法都无法得到图像中任意形状物体的封闭边界。基于活动轮廓的方法可以得到这类目标的边界。多相Chan-Vese活动轮廓法是其中最流行的技术之一。然而，它受到图像中存在的对象数量的限制。使用粗糙集方法的颗粒处理克服了这一限制，并在物体边界周围提供了封闭曲线。这些信息可以进一步用于选择类似的patch来解决各种图像处理问题，如图像去噪、图像超分辨率、图像分割等。本文还对存在噪声的边界检测方法进行了测试。实验结果已在人脑的合成图像和MRI上得到证实。结果表明，该方法的性能令人鼓舞。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Object boundary detection using Rough Set Theory

A Rough Set Theory based closed form object boundary detection method has been suggested in this paper. Most of the edge detection methods fail in getting closed boundary of objects of any shape present in the image. Active contour based methods are available to get such object boundaries. The Multiphase Chan-Vese Active Contour Method is one of the most popular of such techniques. However, it is constrained with number of objects present in the image. The granular processing using Rough Set method overcomes this constraint and provides a closed curve around the boundary of the objects. This information can further be utilized in selection of similar patches for various image processing problems such as Image Denoising, Image Super-resolution, Image Segmentation etc. The proposed boundary detection method has been tested in presence of noise also. The experimental results have shown on synthetic image as well as on MRI of human brain. The performance of proposed method is found to be encouraging.

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来源期刊

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

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