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Register a new userStudy the Performance of Image Descriptor for Building Panoramic Image
دراسة أداء واصفات النقاط المميزة في بناء صورة بانورامية
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Tishreen University ورقة بحثية
Publication date
2016
fields
Mechatronics
and research's language is
العربية
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Shamra Editor
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تم في هذا البحث إجراء تقييم لكواشف و واصفات النقاط المميزة في الصور عند استخدامهم من أجل بناء صورة بانورامية و ذلك لاستخدامها لاحقاً في بحث نقوم فيه ببناء مشهد ثلاثي البعد ضمن بيئة داخلية. نُوقش عمل كل من الواصفات (SIFT, SURF, BRIEF, ORB, BRISK, FREAK)، عند استخدامها مع الكواشف الملائمة لها على قاعدة بيانات ملتقطة بواسطة كاميرا RGB-D لبيئة داخلية. اُستخدمت خوارزمية اختبار التقاطع و خوارزمية RANSAC (إجماع العينات العشوائية) لإيجاد مصفوفة الانتقال بين الصور. نوقشت نتائج كل من: سرعة الكواشف و سرعة الواصفات و سرعة عملية المطابقة و متوسط عدد النقاط المميزة المستخلصة و الحساسية و الدقة لكل الواصفات. كما استُخدمت قاعدة بيانات جامعة أوكسفورد لتبيان أفضل الواصفات لتتعامل مع تغيرات الدوران و الإضاءة التي يمكن أن تنتج عن تغير زاوية الإضاءة في الصور. خلُصت الدراسة إلى أن الكاشف SIFT يعطي أفضل نتائج ضمن التطبيقات التي لا تهتم بمدة تنفيذ العملية، و يعتبر الزوج SURF/BRISK أفضل زوج واصف/كاشف يمكن استخدامه في تطبيقات الزمن الحقيقي و يعطي نتائج منافسة جداً لنتائج الكاشفSIFT.
In this paper an evaluation of image keypoints detectors and descriptors is presented when used for building panoramic image. The descriptors: (SIFT, SURF, BRIEF, ORB, BRISK, and FREAK) were discussed, when used with the appropriate keypoints detectors on database taken indoors by RGB-D camera. Crosscheck and RANSAC (RANdom Sample Consensus) algorithms were used to find transform matrix between images. The speed of keypoints detectors and descriptors, the matching speed, the average of extracted keypoints, recall and precision were investigated. Oxford dataset was used to find the best descriptor for dealing with rotation and illumination changes that might occur due to changes in illumination angle. The obtained results showed that SIFT was the keypoint descriptor with the highest performance in non-real time applications. The SURF/BRISK was the best detector/descriptor which can be used in real time applications with comparable SIFT's results.
References used
ALAHI, A.; ORTIZ, R.; VANDERGHEYNST, P. FREAK: Fast retina keypoint, IEEE Conference on Computer Vision and Pattern Recognition. 2012, 510-517
BAY, H.; ESS, A.; TUYTELAARS, T.; GOOL, L. V. Speeded-up robust features (SURF). Computer Vision and Image Understanding, vol. 110, no. 3, 2008, 346 – 359
BEKELE, D.; TEUTSCH, M.; SCHUCHERT, T. Evaluation of binary keypoint descriptors. IEEE Signal Processing Society; Institute of Electrical and Electronics Engineers, 2013, 3652-3656
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