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- Vol. 39, Issue 8, 0801001 (2019)
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- Vol. 39, Issue 8, 0815002 (2019)
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- Vol. 39, Issue 8, 0815003 (2019)
ing at the problems of low accuracy and high false alarm rate caused by artificial targets in the process of optical remote sensing image docked ship detection. This paper proposes a new method based on edge line gradient features and aggregation channel features for docked ship detection. The multi-structural and multiscale element morphological filters are used to realize the division of sea and land. According to the rectangular shape characteristics of the port in remote sensing images, the edge gradient tangent angle and the port concave and convex features are defined to locate the port,obtaining collection of port region of interest. The aggregation channel features of ships will be extracted and used to train the classifier for the docked ships by AdaBoost algorithm. The trained classifier is used to confirm the real ships in the port. Compared with traditional HOG feature and Haar feature, the proposed algorithm has better detection effect, and its precision and recall rate are greatly improved.
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- Vol. 39, Issue 8, 0815004 (2019)
- Publication Date: Aug. 05, 2019
- Vol. 39, Issue 8, 0815005 (2019)
ing at the problems of memory waste and low efficiency in three-dimensional (3D) object recognition algorithm based on original point pair feature (PPF), a 3D object recognition algorithm based on enhanced point pair feature (EPPF) is proposed. By multiplying the fourth component of the original PPF with a sign function, a more distinguishing PPF is obtained, which eliminates the ambiguity of the original PPF. Considering the self-occlusion of the 3D model of the target to be identified, the large numbers of redundant point pairs existing in the target 3D model hash table are eliminated by means of the viewpoint visibility constraint between the point pairs, which reduces the memory overhead and improves the accuracy and efficiency of the 3D object recognition algorithm. The experimental results on the open dataset and the actual collected dataset show that the proposed 3D object recognition algorithm can improve recognition accuracy and recognition efficiency.
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- Vol. 39, Issue 8, 0815006 (2019)
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