دورية أكاديمية
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A short feature vector for image matching: The Log-Polar Magnitude feature descriptor.

التفاصيل البيبلوغرافية
العنوان: A short feature vector for image matching: The Log-Polar Magnitude feature descriptor.
المؤلفون: Matuszewski DJ; Science for Life Laboratory, Uppsala, Sweden.; Centre for Image Analysis, Uppsala University, Uppsala, Sweden., Hast A; Centre for Image Analysis, Uppsala University, Uppsala, Sweden., Wählby C; Science for Life Laboratory, Uppsala, Sweden.; Centre for Image Analysis, Uppsala University, Uppsala, Sweden., Sintorn IM; Science for Life Laboratory, Uppsala, Sweden.; Centre for Image Analysis, Uppsala University, Uppsala, Sweden.; Vironova AB, Stockholm, Sweden.
المصدر: PloS one [PLoS One] 2017 Nov 30; Vol. 12 (11), pp. e0188496. Date of Electronic Publication: 2017 Nov 30 (Print Publication: 2017).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Pattern Recognition, Automated*, Fourier Analysis
مستخلص: The choice of an optimal feature detector-descriptor combination for image matching often depends on the application and the image type. In this paper, we propose the Log-Polar Magnitude feature descriptor-a rotation, scale, and illumination invariant descriptor that achieves comparable performance to SIFT on a large variety of image registration problems but with much shorter feature vectors. The descriptor is based on the Log-Polar Transform followed by a Fourier Transform and selection of the magnitude spectrum components. Selecting different frequency components allows optimizing for image patterns specific for a particular application. In addition, by relying only on coordinates of the found features and (optionally) feature sizes our descriptor is completely detector independent. We propose 48- or 56-long feature vectors that potentially can be shortened even further depending on the application. Shorter feature vectors result in better memory usage and faster matching. This combined with the fact that the descriptor does not require a time-consuming feature orientation estimation (the rotation invariance is achieved solely by using the magnitude spectrum of the Log-Polar Transform) makes it particularly attractive to applications with limited hardware capacity. Evaluation is performed on the standard Oxford dataset and two different microscopy datasets; one with fluorescence and one with transmission electron microscopy images. Our method performs better than SURF and comparable to SIFT on the Oxford dataset, and better than SIFT on both microscopy datasets indicating that it is particularly useful in applications with microscopy images.
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تواريخ الأحداث: Date Created: 20171201 Date Completed: 20171226 Latest Revision: 20181113
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC5708636
DOI: 10.1371/journal.pone.0188496
PMID: 29190737
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0188496