Color Invariant Feature Detection and Matching in Underwater Stereo Images

Abstract

In this paper, we present an approach to find correspondences in underwater stereo images based on detection and matching of feature points, which are invariant to photometric variations. In underwater environment, the problem of finding correspondences in stereo images is specific step in order to estimate the motion of an underwater vehicle. The current state-of-the-art feature detectors have been proven to be the most robust to geometric variations and avoid dealing with color images due to color constancy problem. The propagation property of light in the underwater causes variations in color information between two underwater images taken under same imaging conditions. To render the color values changed by the various radiometric factors of underwater environment, we use comprehensive color image normalization method to normalize the color image. Our technique uses SIFT to detect interest points from the normalized image. In order to establish correspondences between images, we use window-based correlation measure instead of feature-based correlation techniques. The underwater images are low contrast in nature and lack of image features cause the feature-based techniques matching procedure to fail. Our approach is evaluated extensively to verify its effectiveness with data sets acquired in underwater environment. A new approach based on color invariant feature detection and window-based correlation matching significantly improves the matching reliability.KeywordsUnderwater stereo imagesColor image normalizationSIFTCross-correlationSimilarity measure