scholarly journals A Secure Robust Gray Scale Image Steganography Using Image Segmentation

2016 ◽  
Vol 07 (03) ◽  
pp. 152-164
Author(s):  
Mohammed J. Bawaneh ◽  
Atef A. Obeidat
Keyword(s):  
Image Segmentation ◽  
Image Steganography ◽  
Gray Scale ◽  
Gray Scale Image

Gray Scale Image Segmentation by NSGA-II Based OptiMUSIG Activation Function

2012 ◽  
Author(s):  
Sourav De ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty ◽  
Baidya Nath Sarkar ◽  
Piyush Kumar Prabhakar ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Activation Function ◽  
Gray Scale ◽  
Nsga Ii ◽  
Gray Scale Image

scholarly journals A New Method of Image Steganography Using 7th Bit of a Pixel as Indicator by Introducing the Successive Temporary Pixel in the Gray Scale Image

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Kamaldeep Joshi ◽  
Swati Gill ◽  
Rajkumar Yadav
Keyword(s):  
Image Coding ◽  
Information Hiding ◽  
High Capacity ◽  
Image Steganography ◽  
New Method ◽  
The Internet ◽  
Mathematical Function ◽  
Sensitive Information ◽  
Gray Scale ◽  
Gray Scale Image

As the internet has become the medium for transferring the sensitive information, the security of the transferred message has become the utmost priority. Image steganography has emerged out as the eminent tool of information hiding that ensures the security of the transmitted data. Image files provide high capacity, and their frequency of availability over the internet is also high. In this paper, a method of image coding is proposed that hides the information along a selected pixel and on the next value of the selected pixel, that is, pixel + 1. One bit is hidden at the selected pixel, and the second bit is hidden on the pixel +1 value. On the basis of the 7th bit of the pixels of an image, a mathematical function is applied at the 7th bit of the pixels, which generates a temporary variable (pixel + 1). The 7th bit of the selected pixel and 7th bit of pixel + 1 are used for information hiding and extraction. On the basis of a combination of these two values, two bits of the message can be hidden on each pixel. After implementation, the efficiency of the method is checked on the basis of parameters like PSNR and MSE, and then comparison with some already proposed techniques was done. This proposed image steganography showed interesting, promising results when compared with other existing techniques.

scholarly journals Color Image Segmentation without any Information Loss

2019 ◽  
Vol 9 (1) ◽  
pp. 1919-1923
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Color Image ◽  
Information Loss ◽  
Color Image Segmentation ◽  
Gray Scale ◽  
Gray Scale Image ◽  
The Relationship ◽  
Grayscale Images

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

A New Approach for Gray Image Segmentation Using Level Set Method

2014 ◽  
Vol 530-531 ◽  
pp. 372-376 ◽  
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.

A Time Efficient Clustering Algorithm for Gray Scale Image Segmentation

2013 ◽  
Vol 3 (1) ◽  
pp. 22-32 ◽  
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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