A fast algorithm for local minimum and maximum filters on rectangular and octagonal kernels
Pattern Recognition Letters
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
International Journal of Computer Vision
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
ACM SIGGRAPH 2008 papers
Beyond dynamic textures: a family of stochastic dynamical models for video with applications to computer vision
Case-Based Collective Inference for Maritime Object Classification
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Segmenting salient objects from images and videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Contrast restoration of weather degraded images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Small surface objects, usually containing important information, are difficult to be identified under realistic atmospheric conditions because of weather degraded image features. This paper describes a novel algorithm to overcome the problem, using depth-aware analysis. Because objects-participating local patches always contain low intensities in at least one color channel, we detect suspicious small surface objects using the dark channel prior. Then, we estimate the approximate depth map of maritime scenes from a single image, based on the theory of perspective projection. Finally, using the estimated depth map and the atmospheric scattering model, we design spatial-variant thresholds to identify small surface objects from noisy backgrounds, without contrast enhancement. Experiments show that the proposed method has real-time implementation, and it can outperform the state-of-the-art algorithms on the detection of distant small surface objects with only a few pixels.