کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7132178 | 1461705 | 2017 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Target recognition of log-polar ladar range images using moment invariants
ترجمه فارسی عنوان
تشخیص هدف از تصاویر محدوده لارا ورودی قطبی با استفاده از ابزارهای لحظه ای
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی برق و الکترونیک
چکیده انگلیسی
The ladar range image has received considerable attentions in the automatic target recognition field. However, previous research does not cover target recognition using log-polar ladar range images. Therefore, we construct a target recognition system based on log-polar ladar range images in this paper. In this system combined moment invariants and backpropagation neural network are selected as shape descriptor and shape classifier, respectively. In order to fully analyze the effect of log-polar sampling pattern on recognition result, several comparative experiments based on simulated and real range images are carried out. Eventually, several important conclusions are drawn: (i) if combined moments are computed directly by log-polar range images, translation, rotation and scaling invariant properties of combined moments will be invalid (ii) when object is located in the center of field of view, recognition rate of log-polar range images is less sensitive to the changing of field of view (iii) as object position changes from center to edge of field of view, recognition performance of log-polar range images will decline dramatically (iv) log-polar range images has a better noise robustness than Cartesian range images. Finally, we give a suggestion that it is better to divide field of view into recognition area and searching area in the real application.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Optics and Lasers in Engineering - Volume 88, January 2017, Pages 301-312
Journal: Optics and Lasers in Engineering - Volume 88, January 2017, Pages 301-312
نویسندگان
Wenze Xia, Shaokun Han, Jie Cao, Haoyong Yu,