## An introduction to edge detection: The sobel edge detector,

- J. Matthews
- Available at http://www.generation5.org/content…
- 2002

- Published 2010

-Histogram is a graphical representation showing a visual impression of the distribution of experimental data. It is an estimate of the probability distribution of a continuous variable and was first introduced by Karl Pearson. A histogram consists of tabular frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to the frequency of the observations in the interval. In this paper the comparative analysis of various Image Edge Detection techniques for Power Amplifier is presented with histogramic implementation. The software is developed using MATLAB 7.5. Evaluation of the images showed that under noisy conditions Canny, LoG( Laplacian of Gaussian), Robert, Prewitt, Sobel exhibit better performance, respectively[1]. Histograms are used to get better results which helps to plot the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the object edge detection is one of the ways of solving the problem of high volume of space images occupy in the computer memory. The problems of storage, transmission over the Internet and bandwidth could be solved when edges are detected. Keywords—Histogram, Histogram equalization, Edge detection techniques, Power Amplifier. I.INTRODUCTION Histograms are used to analyse the tonal value of an image. It shows how bright or dark the image is (number of tones captured at each brightness level) It is made of individual columns (each column representing how many pixels there are in specific tonal value) which range from darkest (value 0) to brightest (value 255).Between the two are the gray values. A Histogram reveals the information that whether or not your image has been properly scanned or exposed. Each pixel in an image has a color which has been produced by some combination of the primary colors red, green, and blue (RGB). Each of these colors can have a brightness value ranging from 0 to 255 for a digital image with a bit depth of 8-bits. A RGB histogram results when the computer scans through each of these RGB brightness values and counts how many are at each level from 0 through 255.Histograms are merely the representative of tonal value of an image. Image processing is a technique which involves enhancement or manipulation of an image which resulted in another image, removal of redundant data and the transformation of a 2-D and 3-D pixel array into a statically uncorrelated data set. Images as a whole contains redundant data and important information lies in the edges of an image. Edges correspond to object boundaries, changes in surface orientation and describe defeat by a small margin. Thus, detecting Edges of power amplifier help in extracting useful information characteristics of the image where there are abrupt changes. The two filters highlight areas of high special frequency, which tend to define the edge of an object in an image. The two filters are designed with the intention of bringing out the diagonal edges within the image. The Gx image will enunciate diagonals that run from the topleft to the bottom-right where as the Gy image will bring out edges that run top right to bottom-left. The two individual images Gx andGy are combined using the approximation equation G Gx Gy II.HISTOGRAM EQUALIZATION It is a method in image processing of contrast adjustment using the image's histogram. With this the intensities can be better distributed on the histogram. It has got applications in the field of thermal, satellite,or X-ray images. This technique is basically used to improve the visuality of an image.Basically in this technique for recovering some of apparently lost contrast in an image by remapping the brightness values in such a way as to equalize, or more evenly distribute, its brightness values. The method to draw the equalized histogram is given below: 1) Read the input image: The input image contains n pixcls where n=height*width. 2) Convert form RGB. 3) Calculate the Histogram: The Histogram og input image is calculated. This is a 256 value array, where H[x] contains the number of pixels with value x. 4) Calculation of C.D.F: The C.D.F of histogram is calculated. This is a 256 value array, where cdf[x] contains the number of pixels with value x or less: cdf[x] = H[0] + H[1] + H[2] + ... + H[x] 5) Loop through the n pixels in the entire image and replace the value at each i'th point: V[i] <-floor(255*(cdf[V[i]] cdf[0])/(n cdf[0])) 6) Convert the image back from HSV to RGB. 7) Save the image in the desired format and file name. Analysis of Power Amplifier by Frontier Recognition and Histograms Surekha Chauhan, Prof. P.K. Ghosh ,Madan Shishodia 1 ECE Deptt.,Mody institute of Technology and Science, Rajasthan INDIA 2 CSE Deptt.,SILB, The Mall, Solan – 173212,HP Surekha Chauhan et al. / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 1 (5) , 2010, 342-346

@inproceedings{Chauhan2010AnalysisOP,
title={Analysis of Power Amplifier by Frontier Recognition and Histograms},
author={Surekha Chauhan and Prof . P . K . Ghosh and Madan Shishodia},
year={2010}
}