Unsupervised Image Segmentation using Defocus Map and Superpixel Grouping

• Main Authors: Lo, Chun-Kuei, 羅鈞魁
• Other Authors: Chang, Long-Wen
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/82798630353284423628

碩士 === 國立清華大學 === 資訊工程學系 === 104 === Image segmentation is an important and difficult issue in computer vision and image processing. It categorized into two categories, supervised image segmentation and unsupervised image segmentation. The supervised methods need some interactions of users. It makes those methods inconvenient. Recently, most of segmentation methods usually use similarity which is defined by color difference or histogram. Every similarity has its weak side. In this paper, we proposed an unsupervised method. It uses defocus map, edge and color as similarity of pixels or superpixels. We generate an edge strength map. Then, we construct a minimum spanning tree with the superpixels and the edge map to divide the image to foreground and background. In our experiment, out method doesn’t need user interaction and the performance is better than previous superpixels grouping method.