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Study the Performance of Image Descriptor for Building Panoramic Image

دراسة أداء واصفات النقاط المميزة في بناء صورة بانورامية

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 Publication date 2016
  fields Mechatronics
and research's language is العربية
 Created by Shamra Editor




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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.


Artificial intelligence review:
Research summary
يقدم هذا البحث تقييماً لكواشف وواصفات النقاط المميزة في الصور عند استخدامها لبناء صورة بانورامية، وذلك لاستخدامها لاحقاً في بناء مشهد ثلاثي الأبعاد ضمن بيئة داخلية. تم مناقشة أداء الواصفات (SIFT, SURF, BRIEF, ORB, BRISK) عند استخدامها مع الكواشف المناسبة لها على قاعدة بيانات ملتقطة بواسطة كاميرا RGB-D في بيئة داخلية. استخدمت خوارزمية اختبار التقاطع وخوارزمية RANSAC لإيجاد مصفوفة الانتقال بين الصور. تم تحليل سرعة الكواشف والواصفات وسرعة عملية المطابقة ومتوسط عدد النقاط المميزة المستخلصة والحساسية والدقة لكل واصف. استخدمت قاعدة بيانات جامعة أوكسفورد لتحديد أفضل الواصفات التي تتعامل مع تغيرات الدوران والإضاءة. خلصت الدراسة إلى أن الكاشف SIFT يعطي أفضل نتائج في التطبيقات التي لا تهتم بمدة التنفيذ، بينما يعتبر الزوج SURF/BRISK أفضل زوج واصف/كاشف يمكن استخدامه في تطبيقات الزمن الحقيقي ويعطي نتائج منافسة جداً لنتائج الكاشف SIFT.
Critical review
دراسة نقدية: على الرغم من أن البحث يقدم تقييماً شاملاً لأداء الكواشف والواصفات المختلفة، إلا أنه يركز بشكل كبير على الأداء في بيئات داخلية محددة باستخدام كاميرا RGB-D. قد يكون من المفيد توسيع نطاق الدراسة لتشمل بيئات خارجية وظروف إضاءة متنوعة. بالإضافة إلى ذلك، يمكن تحسين الدراسة من خلال اختبار الواصفات والكواشف على مجموعة أوسع من قواعد البيانات وتطبيقات مختلفة. كما أن التركيز على تحسين الأداء في الزمن الحقيقي يمكن أن يكون مفيداً في التطبيقات العملية المتنوعة.
Questions related to the research
  1. ما هي الكواشف والواصفات التي تم تقييمها في البحث؟

    تم تقييم الكواشف والواصفات التالية: SIFT, SURF, BRIEF, ORB, BRISK.

  2. ما هي الخوارزميات المستخدمة لإيجاد مصفوفة الانتقال بين الصور؟

    استخدمت خوارزمية اختبار التقاطع وخوارزمية RANSAC لإيجاد مصفوفة الانتقال بين الصور.

  3. ما هي قاعدة البيانات المستخدمة لتحديد أفضل الواصفات التي تتعامل مع تغيرات الدوران والإضاءة؟

    استخدمت قاعدة بيانات جامعة أوكسفورد لتحديد أفضل الواصفات التي تتعامل مع تغيرات الدوران والإضاءة.

  4. ما هو الزوج الأفضل من الواصفات والكواشف للاستخدام في تطبيقات الزمن الحقيقي؟

    الزوج SURF/BRISK يعتبر الأفضل للاستخدام في تطبيقات الزمن الحقيقي ويعطي نتائج منافسة جداً لنتائج الكاشف SIFT.


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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