کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6937438 | 1449735 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A Novel perspective invariant feature transform for RGB-D images
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
RGB-D cameras have been attracting increasing researches for solving traditional problems in the domain of computer vision and robotics. Among the existing local features, most are proposed for the color channel or depth channel separately, while little attention has been paid to designing new composite features based on the physical characteristics. In this work, we propose a novel perspective invariant feature transform (PIFT) for RGB-D images. We integrate the color and depth information together making full use of the intrinsic characteristics of the two types of information to enhance the robustness and adaptability to large spatial variations of local appearance. The depth information is used to project the feature patch to its tangent plane to make it consistent with different views. It also helps to filter out the “fake keypoints” which are unstable in 3D space. Binary descriptors are then generated in the feature patches using a color coding method. Experiments on publicly available RGB-D datasets show that the proposed method has the best precision and the second best recall rate comparing against state-of-the-art local features, when applied to feature matching with large spatial variations.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Vision and Image Understanding - Volume 167, February 2018, Pages 109-120
Journal: Computer Vision and Image Understanding - Volume 167, February 2018, Pages 109-120
نویسندگان
Qinghua Yu, Jie Liang, Junhao Xiao, Huimin Lu, Zhiqiang Zheng,