Feature tracking in video and sonar subsea sequences with applications
Computer Vision and Image Understanding - Special issue on underwater computer vision and pattern recognition
Comprehensive Colour Image Normalization
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Illuminant and gamma comprehensive normalisation in log RGB space
Pattern Recognition Letters - Special issue: Colour image processing and analysis
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
CSIFT: A SIFT Descriptor with Color Invariant Characteristics
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Efficient Non-Maximum Suppression
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Stereo Vision Based Motion Estimation for Underwater Vehicles
ICICTA '09 Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation - Volume 03
Robust Stereo Matching Using Adaptive Normalized Cross-Correlation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Tracking Evaluation for Pose Estimation in Underwater Environments
CRV '11 Proceedings of the 2011 Canadian Conference on Computer and Robot Vision
Description of interest regions with center-symmetric local binary patterns
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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In this paper, we introduce LBP-SURF, a local image descriptor for underwater environment, which is very efficient to extract color invariant and texture based features of underwater images. The current state-of-the-art feature descriptors viz. SIFT, SURF, DAISY, GLOH and variants are well known techniques for detecting and describing features of objects captured in the out-of-water environment. These standard descriptors have been proven to be the most robust to geometric variations. Nearly, all these geometrical invariant approaches avoid dealing with color images due to the color constancy problem. In underwater images, variation in color is very high compared to variations in geometrical properties due to propagation properties of light. The literature survey reveals that, the texture parameters that remain constant for the scene patch for the whole underwater image sequence. This motivated us to consider texture and color invariant features of underwater images, instead of using the gray-based geometrical invariant features. We normalize the color image using comprehensive color image normalization method to render the color values changed by the various radiometric factors of underwater environment. Our method uses Speeded Up Robust Features (SURF) to detect interest points from the normalized image. The texture features are extracted, and description is stored using Center-Symmetric Local Binary Patterns (CS-LBP) descriptor. The combination of SURF and CS-LBP, called LBP-SURF is evaluated extensively to verify its effectiveness with datasets acquired in underwater environment.