A pyramidal approach to active contours implementation for 2D gray scale image segmentation

We can partition the background from foreground and locate the objects of interest using image segmentation techniques. In other words we can say image segmentation is the process of grouping adjacent pixels in to segments. In this research we proposed a model which can differentiate maximum and minimum frequencies for both color and grayscale images without any information loss. After getting the result of both images, we will check which (gray scale image or color image) gives better performance to the image segmentation techniques. So, here we will take the two techniques edge detection and threshold. This research gives better result of segmentation by using the relationship discontinuous and similar pixel values

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A New Approach for Gray Image Segmentation Using Level Set Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.530-531.372 ◽

2014 ◽

Vol 530-531 ◽

pp. 372-376 ◽

Cited By ~ 1

Author(s):

Lai Zhen Li ◽

Shuai Han ◽

Wen Ming Wang ◽

Hu Tan ◽

Qiang Zhou

Keyword(s):

Image Segmentation ◽

Level Set Method ◽

Level Set ◽

Small Area ◽

Experimental Results ◽

Curve Evolution ◽

Gray Scale ◽

New Approach ◽

A Value ◽

Gray Scale Image

The techniques and the processes to divide the image into several parts which have different features and to pick up foreground are called image segmentation. In this work, we propose a new approach for gray scale image segmentation based on level set method. At first, every pixel on the image is divided into either similar-property class or dissimilar-property class based on the variance of a small area centered at the pixel. Then, the velocity of curve evolution for these two classes is defined respectively. It is determined by a value called the dissimilarity of the area. Experimental results show that this approach can obtain good segmentation results of artificial images and real medical images fast and accurately.

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A Time Efficient Clustering Algorithm for Gray Scale Image Segmentation

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2013010102 ◽

2013 ◽

Vol 3 (1) ◽

pp. 22-32 ◽

Cited By ~ 4

Author(s):

Nihar Ranjan Nayak ◽

Bikram Keshari Mishra ◽

Amiya Kumar Rath ◽

Sagarika Swain

Keyword(s):

Image Segmentation ◽

Clustering Algorithm ◽

Signal To Noise Ratio ◽

Clustering Algorithms ◽

Performance Measure ◽

Experimental Result ◽

Computational Time ◽

Watershed Segmentation ◽

Gray Scale ◽

Gray Scale Image

The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this paper, the authors have evaluated the performances of three different clustering algorithms – the classical K-Means, a modified Watershed segmentation as proposed by A. R. Kavitha et al., (2010) and their proposed Improved Clustering method normally used for gray scale image segmentation. The authors have analyzed the performance measure which affects the result of gray scale segmentation by considering three very important quality measures that is – Structural Content (SC) and Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) as suggested by Jaskirat et al., (2012). Experimental result shows that, the proposed method gives remarkable consequence for the computed values of SC, RMSE and PSNR as compared to K-Means and modified Watershed segmentation. In addition to this, the end result of segmentation by means of the Proposed technique reduces the computational time as compared to the other two approaches irrespective of any input images.

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