8 March 2018 Ship detection based on rotation-invariant HOG descriptors for airborne infrared images
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 1060912 (2018) https://doi.org/10.1117/12.2285307
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
Infrared thermal imagery is widely used in various kinds of aircraft because of its all-time application. Meanwhile, detecting ships from infrared images attract lots of research interests in recent years. In the case of downward-looking infrared imagery, in order to overcome the uncertainty of target imaging attitude due to the unknown position relationship between the aircraft and the target, we propose a new infrared ship detection method which integrates rotation invariant gradient direction histogram (Circle Histogram of Oriented Gradient, C-HOG) descriptors and the support vector machine (SVM) classifier. In details, the proposed method uses HOG descriptors to express the local feature of infrared images to adapt to changes in illumination and to overcome sea clutter effects. Different from traditional computation of HOG descriptor, we subdivide the image into annular spatial bins instead of rectangle sub-regions, and then Radial Gradient Transform (RGT) on the gradient is applied to achieve rotation invariant histogram information. Considering the engineering application of airborne and real-time requirements, we use SVM for training ship target and non-target background infrared sample images to discriminate real ships from false targets. Experimental results show that the proposed method has good performance in both the robustness and run-time for infrared ship target detection with different rotation angles.
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Guojing Xu, Jinyan Wang, Shengxiang Qi, "Ship detection based on rotation-invariant HOG descriptors for airborne infrared images", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060912 (8 March 2018); doi: 10.1117/12.2285307; https://doi.org/10.1117/12.2285307
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