کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10326481 | 678070 | 2016 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Scale invariant representation of imbalanced points
ترجمه فارسی عنوان
مقیاس ناپیوستگی نقاط ناپایدار
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کلمات کلیدی
مقیاس، نکته جالب نقطه ناپایدار، تطابق،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Imbalance oriented candidate selection was introduced as an alternative of non-maximum suppression, aiming to improve the localization accuracy. To distinguish interest points detected via non-maximum suppression, we call interest points detected via imbalance oriented selection imbalanced points. Scale assignment for imbalanced points is not straightforward because of a dilemma of involving non-maximum suppression. The scale space theory, a popular scale assignment scheme, requests non-maximum suppression to detect extreme points from scale spaces, while imbalanced points are expected to be free of non-maximum suppression in order to maintain the localization accuracy. In this paper, we propose a bypass strategy that circumvents the above dilemma by establishing an association between an imbalanced point and a certain interest point with a known scale (e.g., Lowe׳s keypoints and Hessian-Laplace). Furthermore, we propose a hybrid representation of imbalanced points for a two-layer matching scheme, where the first-layer matching is based on discriminant SIFT-type descriptors of imbalanced points, and the second-layer matching is based on patch-type descriptors. Experiments show the effectiveness of the proposed scale assignment and hybrid representation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 173, Part 3, 15 January 2016, Pages 1422-1435
Journal: Neurocomputing - Volume 173, Part 3, 15 January 2016, Pages 1422-1435
نویسندگان
Qi Li, Yongyi Gong,