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An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut

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Book Synopsis An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut by : Yanhui Guo

Download or read book An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut written by Yanhui Guo. This book was released on . Available in PDF, EPUB and Kindle. Book excerpt: Segmentation is considered as an important step in image processing and computer vision applications, which divides an input image into various non-overlapping homogenous regions and helps to interpret the image more conveniently. This paper presents an efficient image segmentation algorithm using neutrosophic graph cut (NGC).

A Fast Image Segmentation Algorithm Based on Saliency Map and Neutrosophic Set Theory

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Genre : Mathematics
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Book Synopsis A Fast Image Segmentation Algorithm Based on Saliency Map and Neutrosophic Set Theory by : Sensen Song

Download or read book A Fast Image Segmentation Algorithm Based on Saliency Map and Neutrosophic Set Theory written by Sensen Song. This book was released on . Available in PDF, EPUB and Kindle. Book excerpt: Due to more or less deviations in the imaging system, there will be noise in the image, which makes the image segmentation inaccurate. To divide a natural image into a more accurate binary image, the target and background of the image are effectively separated to achieve a more effective segmentation result. Therefore, this paper proposes an image segmentation algorithm combining a saliency map and neutrosophic set (NS) theory. First, to overcome the problem of weak edges in the image, we highlight the details and use the guided filter to filter the various channels of the natural image. Then, the initial saliency map is generated. After the weighted superposition of the initial saliency map, the local entropy map and the gray scale map, the final saliency map can be generated using the nonlinear function, and it can effectively highlight the foreground information of the image. Second, the saliency map is transformed to the NS domain and interpreted by three subsets: true (T), indeterminate (I), and false (F). According to NS theory, the indeterminacy is reduced, and the segmentation results are finally obtained by using the method of threshold. Various experiments were done and compared with other state-of-the-art approaches. These experiments demonstrate the effect of the proposed work, which is fast and effective for de-noising.

Symmetry, vol. 9, issue 10 / 2007 - Special Issue: Neutrosophic Theories Applied in Engineering

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Book Synopsis Symmetry, vol. 9, issue 10 / 2007 - Special Issue: Neutrosophic Theories Applied in Engineering by : Florentin Smarandache

Download or read book Symmetry, vol. 9, issue 10 / 2007 - Special Issue: Neutrosophic Theories Applied in Engineering written by Florentin Smarandache . This book was released on . Available in PDF, EPUB and Kindle. Book excerpt: This Special Issue presents original research papers that report on state-of-the-art and recent advancements in neutrosophic sets and logic in soft computing, artificial intelligence, big and small data mining, decision making problems, and practical achievements.

New neutrosophic approach to image segmentation

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Book Synopsis New neutrosophic approach to image segmentation by : Yanhui Guo

Download or read book New neutrosophic approach to image segmentation written by Yanhui Guo. This book was released on . Available in PDF, EPUB and Kindle. Book excerpt: Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra. NS is a formal framework that has been recently proposed.

Comparative Analysis of Deep Learning and Graph Cut Algorithms for Cell Image Segmentation

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Release : 2020
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Book Synopsis Comparative Analysis of Deep Learning and Graph Cut Algorithms for Cell Image Segmentation by : Ghazal Reshad

Download or read book Comparative Analysis of Deep Learning and Graph Cut Algorithms for Cell Image Segmentation written by Ghazal Reshad. This book was released on 2020. Available in PDF, EPUB and Kindle. Book excerpt: Image segmentation is a commonly used technique in digital image processing with many applications in the area of computer vision and medical image analysis. The goal of image segmentation is to partition an image into multiple regions, normally based on the characteristics of pixels in a given image. Image segmentation could involve separating the foreground from background in an image, or clustering image regions based on similarities in intensity, color, or shape. In this thesis, we consider the problem of cell image segmentation and evaluate the performance of two major techniques on a dataset of cell image sequences. First, we apply a traditional segmentation algorithm based on the so-called graph cut that addresses the segmentation problem using an energy minimization scheme defined on a weighted graph. Second, we use modern techniques based on deep neural networks, namely U-Net and LSTM that have a time-consuming training and a relatively quick testing phase. Performance of each technique will be analyzed qualitatively and quantitatively based on various standard measures and will be compared statistically.

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