کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382427 660761 2015 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Visual orientation inhomogeneity based scale-invariant feature transform
ترجمه فارسی عنوان
تغییر نگرش مقیاس ناسازگار بصری جهت ناهمگونی
کلمات کلیدی
ناهماهنگی جهت، توزیع دنیای واقعی، مقیاس غیر قابل تغییر بودن ویژگی، تبعیض حداقل
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• Provide the evidence of existence of the least important visual orientation.
• Novel algorithm with high efficiency is proposed to detect and describe local feature.
• Better performance for detection and matching, comparable performance for recognition.

Scale-invariant feature transform (SIFT) is an algorithm to detect and describe local features in images. In the last fifteen years, SIFT plays a very important role in multimedia content analysis, such as image classification and retrieval, because of its attractive character on invariance. This paper intends to explore a new path for SIFT research by making use of the findings from neuroscience. We propose a more efficient and compact scale-invariant feature detector and descriptor by simulating visual orientation inhomogeneity in human system. We validate that visual orientation inhomogeneity SIFT (V-SIFT) can achieve better or at least comparable performance with less computation resource and time cost in various computer vision tasks under real world conditions, such as image matching and object recognition. This work also illuminates a wider range of opportunities for integrating the inhomogeneity of visual orientation with other local position-dependent detectors and descriptors.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 42, Issue 13, 1 August 2015, Pages 5658–5667
نویسندگان
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