Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cast shadow segmentation using invariant color features
Computer Vision and Image Understanding
On the Removal of Shadows from Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shadow identification and classification using invariant color models
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Shadow detection in colour high-resolution satellite images
International Journal of Remote Sensing
An Efficient Gray-level Clustering Algorithm for Image Segmentation
CAR '09 Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics
An Efficient Hierarchical Method for Image Shadow Detection
WKDD '09 Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining
Morphological preprocessing method to thresholding degraded word images
Pattern Recognition Letters
Hierarchical shadow detection for color aerial images
Computer Vision and Image Understanding
Moving Cast Shadows Detection Using Ratio Edge
IEEE Transactions on Multimedia
| Hi-index | 0.00 |
Shadow detection in high spatial resolution remote sensing image is very critical for locating geographical targets. In this paper, we proposed a new shadow detection method using Affinity Propagation (AP) algorithm in the Hue-Saturation-Intensity (HSI) color space. Because the pixel matrix is a large-scale matrix, if we apply AP algorithm directly on the raw pixel space, it will be computation intensive to calculate the similarity matrix. To solve this problem, we propose to divide the matrix into several blocks and then applying AP to detect shadows in H, S and I components respectively. Then, three detected images are fused to obtain a final shadow detection result. Comparative experiments are performed for K-means and threshold segmentation methods. The experimental results show that higher detection accuracy of the proposed approach is obtained, and it can solve the problems of false dismissals of K-means and threshold segmentation method.