Abstract or Introduction
Edge detection is a research field within Image processing and Computer vision, in particular within the area of feature extraction. It is extensively used in image segmentation when we want to divide the image into areas corresponding to different objects. Representing an image by its edges has the further advantage that the amount of data is reduced significantly while retaining most of the image information.
Since edges consist of mainly high frequencies, we can, in theory, detect edges by applying a high pass frequency filter in the Fourier domain or by convolving the image with an appropriate kernel in the spatial domain. In practice, edge detection is performed in the spatial domain, because it is computationally less expensive and often yields better results. Since edges correspond to strong illumination gradients, we can highlight them by calculating the derivatives of the image.
The present Thesis aims at extracting a good & accurate edge detected image from the application of various masks or edge detection operators on the image.Convolution is the mathematical tool, that is used to implement the various masks operators to get an edge detected image from the original image. Our thesis provides the implementation of the following edge detection techniques to get a better edge detected image: a) 1 Dimensional operators : Kirch ,Prewitt, Sobel and Quick Masking; b) 2 Dimensional operators: LOG( Laplacian of Guassian) and DOG( difference of Guassian).
- Quote paper
- Ashima Kalra (Author), 2008, Implementation and Performance Study of Edge Detection of Images, Munich, GRIN Verlag, https://www.grin.com/document/496900