Edge detection is an important feature in computer supervision and image processing. In this paper, we discuss several digital image processing techniques in edge feature extraction. Firstly,we define keyterms,such as image,digitalimage,edge, Image noiseetc.leading to certain methods of image de-noising and edge detection. Edge detection includes operators such as Sobel, Prewitt and Roberts. Secondly, a comparative study is made to show that the Sobel operator gives best results.Finally, Edge extraction using edge histogram is taken into account. The edge extraction method proposed in this paper is feasible. Index terms: digital image, edge detection, operators, edge histogram. Introduction:
The edge is a set of those pixels whose grey have stepchange and rooftop change, and it exists between object andbackground, object and object, region and region, and betweenelement and element. Edge always indwells in twoneighboring areas having different grey level. It is the result ofgray level being discontinuous. Edge detection is a kind ofmethod of image segmentation based on range non-continuity.Image edge detection is one of the basal contents in the imageprocessing and analysis, and also is a kind of issues which areunable to be resolved completely so far. When image isacquired, the factors such as the projection, mix, aberranceand noise are produced. These factors bring on image feature’sblur and distortion, consequently it is very difficult to extractimage feature. Moreover, due to such factors it is also difficultto detect edge. The method of image edge and outlinecharacteristic's detection and extraction has been research hotin the domain of image processing and analysis technique. Edge feature extraction has been applied in many areaswidely. This paper mainly discusses about advantages anddisadvantages of several edge detection operators .In order to gainmore legible image outline, firstly the acquired image isfiltered and denoised. And then different operatorsareapplied to detect edge including Sobel operator, Prewitt operator, and Roberts operator. Finally, edge extraction is done using edge histogram. Image:
An image (from Latin: imago) is an artefact, for example a two-dimensional picture, that has a similar appearance to some subject—usually a physical object or a person. A digital image is a numeric representation (normally binary) of a two-dimensional image. An image may be defined as a two-dimensional function f(x, y), where (x, y) are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or the gray level of the image point. When x and y and the amplitudes values of f are all finite, discrete quantities, we call the image a digital image. A digital image is composed of a finite number of elements, each of which has a particular location and value known as pixels. Edge:
There are three basic types of grey-level discontinuities in a digital image: points, lines and edges. Edge detection is by far the most common approach for detecting meaningful discontinuities in gray-level. An edge is a set of connected pixels that lie on the boundary between two regions. A reasonable definition of edge requires the ability to measure gray-level transitions in a meaningful way.
Fig.1 (a) Model of an ideal edge with gray-profile on a horizontal line through the image
Fig.1 (b) Model of a ramp digital edge with gray-level profile of a horizontal line through the image. An ideal edge has the properties of the model shown in Fig.1(a).An ideal edge according to this model is a set of connected pixels(in the vertical direction here), each of which is located at an orthogonal step transition in gray level(as shown by the horizontal profile in the figure). In practice, optics, sampling, and other image acquisition imperfections yield edges that are blurred, with the degree of blurring determined by factors such as the quality of the image acquisition system, the...