On improving the accuracy of the Hough transform
Machine Vision and Applications
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape measures for content based image retrieval: a comparison
Information Processing and Management: an International Journal
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
An effective region-based image retrieval framework
Proceedings of the tenth ACM international conference on Multimedia
Graph Method for Generating Affine Moment Invariants
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Extended Hough transform for linear feature detection
Pattern Recognition
Real-time line detection through an improved Hough transform voting scheme
Pattern Recognition
An image retrieval system with automatic query modification
IEEE Transactions on Multimedia
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IEEE Transactions on Circuits and Systems for Video Technology
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
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According to the characteristics of airfield and harbor from remote sensing images, a method of large target recognition based on the combination of target region and shape features is presented. First, edge detection and improved Hough transform are used to select line segments, the region including regular-array line segments in image is considered as region of interesting (ROI). ROI detection is the base for recognition. Target geometry shape is extracted from ROI using optimum threshold segmentation, which removes location effect and improves efficiency. As calculating shape principal orientations, all shapes are rotated to the same horizontally right to avoid rotation effect. The features extracted from shape implement multi-levels representation with moment features, normalized moment of inertia, length-width ratio and compact ration. Finally, feature vectors are normalized to measure similarity between target and template. Experiments show that target regions can be located accurately using ROI detection and it is effective for target recognition. Besides, the extracted features have good invariability with respect to rotation, translation and scaling, and they comprise local and overall consistency of the target, therefore, the recognition results meet expectations well.