An Experimental Study and Analysis of Different Image Segmentation Techniques

AbstractImage segmentation is a process of classifying or identifying sub patterns in a given image. Image segmentation is widely used in applications like automatic pattern recognition, content based image retrieval, machine vision, medical imaging, face detection and object detection. In this work, different image segmentation methods like threshold based, region based segmentation and edge based segmentation methods are explored. Experiments have been done using MATLAB 7.12 and the standard benchmark images from Berkeley database are used. Different edge detection methods such as Sobel, Prewitt and Canny methods are performed on the benchmark images and the performance is analyzed with respect to accuracy and error rate. Simple, multiple threshold and region based methods are also tested on the benchmark images. Texture filters such as range, entropy and standard deviation are experimented on the texture images