tailieunhanh - Báo cáo hóa học: " Unsupervised Performance Evaluation of Image Segmentation"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Unsupervised Performance Evaluation of Image Segmentation | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 96306 Pages 1-12 DOI ASP 2006 96306 Unsupervised Performance Evaluation of Image Segmentation Sebastien Chabrier Bruno Emile Christophe Rosenberger and Helene Laurent Laboratoire Vision et Robotique UPRES EA 2078 ENSI de Bourges Universite d Orleans 10 boulevard Lahitolle 18020 Bourges cedex France Received 1 March 2005 Revised 5 January 2006 Accepted 21 January 2006 We present in this paper a study of unsupervised evaluation criteria that enable the quantification of the quality of an image segmentation result. These evaluation criteria compute some statistics for each region or class in a segmentation result. Such an evaluation criterion can be useful for different applications the comparison of segmentation results the automatic choice of the best fitted parameters of a segmentation method for a given image or the definition of new segmentation methods by optimization. We first present the state of art of unsupervised evaluation and then we compare six unsupervised evaluation criteria. For this comparative study we use a database composed of 8400 synthetic gray-level images segmented in four different ways. Vinet s measure correct classification rate is used as an objective criterion to compare the behavior of the different criteria. Finally we present the experimental results on the segmentation evaluation of a few gray-level natural images. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Segmentation is an important stage in image processing since the quality of any ensuing image interpretation depends on it. Several approaches have been put forward in the literature 1 2 . The region approach for image segmentation consists in determining the regions containing neighborhood pixels that have similar properties gray-level texture . . The contour approach detects the boundaries of these regions. We have decided to focus on