A Fast Image Edge Detection Algorithm Based on Row-Column Scanning

A new algorithm of image edge detection is proposed based on analysis that there are three parts can be improved in the classical method of image edge detection. A novel algorithm is introduced in detail and the image edge is respectively detected in row and column direction. The flow chart of algorithm is given. In the end, the algorithm is applied in some test systems and results show it can effectively detect complicated image edge with higher quality and fast speed compared with other detection methods. I. INTRODUCTION Because edge contains important information and significant features, it is widely applied in the analysis of contour, feature extraction and other fields (1). How to extract edge feature ef- fectively has always been the major studying projects in image preprocessing field. Edge is one of the most fundamental and significant features. Edge detection is always one of classical studying projects of computer vision and image processing field. Generally speaking, edge is where the point's grey degree is dramatically changed compared with normal level. It has rich high frequency spectrum. There are three kinds of edges, first one is step edge where its grey degree level dramatically changes from one level of grey degree to another level of grey degree. Second is roof edge where its grey degree level slowly increases to certain degree level and then slowly reduces. The third is line-edge where its grey degree level leaps into another level of grey degree and then returns its original grey degree level. In view of differential angle, line-edge is zero order and nonconsecutive, and step edge is one order and nonconsecutive. Roof edge is two orders and nonconsecutive. The goal of edge detection is extracting a borderline be- tween objects. The changes of image grey degree can be reflected by the distribution of its grads. The local image differential technology can be used for obtaining edge de- tection arithmetic operators. The classical algorithm mainly adopts convolution. Because the values of edge grey degree is nonconsecutive, derivative operator can detects the change of its grey degree, make application of derivative arithmetic operator to image so as to give prominence to the edge of local image. The differential value can be recognized as the boundary intensity of the corresponding points. And it can use the method of setting threshold TH to extract the collection of the edge. The commonly detection edge operator include Robert operator, Sobel operator, Prewitt operator, LOG operator, Canny operator and etc (2). The common character- istics of above operators is that templates is needed for edge detecting. In practical use, enhancement and de-noising has to be made firstly, and then classical extracting operators is used. With the development of signal processing, more and more new technology such as wavelet (3, 4), fuzzy (5), neural network (6), grey relational analysis (7), morphology, fractal theory and other edge detecting theories are adopted in edge detection. In this paper, A fast image edge detection method based on analysis that there are three parts can be improved in the classical method of image edge detection. A novel algorithm is introduced in detail and the image edge is respectively detected in row and column direction.