Image Matching Using Photometric Information

Image matching is an essential task in many computer vision applications. It is obvious that thorough utilization of all available information is critical for the success of matching algorithms. However most popular matching methods do not incorporate effectively photometric data. Some algorithms are based on geometric, color invariant features, thus completely neglecting available photometric information. Others assume that color does not differ significantly in the two images; that assumption may be wrong when the images are not taken at the same time, for example when a recently taken image is compared with a database. This paper introduces a method for using color information in image matching tasks. Initially the images are segmented using an off-the-shelf segmentation process (EDISON). No assumptions are made on the quality of the segmentation. Then the algorithm employs a model for natural illumination change to define the probability of two segments to originate from the same ...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Michael Kolomenkin, Ilan Shimshoni
• Keywords: Computer Vision, CVPR 2006, Image Matching, Image Matching System, Image Matching Tasks, Off-the-shelf Segmentation Process, Popular Matching Methods