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SIFT and ORB performance assessment for object identification in different test cases

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Abstract

Computer vision is a catch-all term for a variety of applications. This makes it a good research environment for new ideas and concepts. Feature extraction is considered an essential step in such applications. Several research studies introduced the Scale-Invariant Feature Transform (SIFT) as a benchmark method to extract visual features from objects inside images. This ensures the need for a deep study of SIFT in a variety of settings. Hence, this paper presents an assessment of SIFT from different perspectives that are not explicitly expressed in the literature. In addition, it presents an illustration of the majority of Oriented FAST and Rotated BRIEF (ORB) feature extraction characteristics to facilitate the choice procedure between SIFT and ORB. Several experimental cases are included, each of which evaluates the performance of such methods from distinct and different aspects. At first, the paper presents an assessment of these methods to identify objects inside geometrically–affine-transformed images. This is done by comparing how well their gathered feature descriptors from images perform against one another. Second, this paper presents an evaluation of the invariance of these methods to the changes in illumination. Furthermore, the computational and asymptotic complexity of such methods is investigated to examine its impact on the complexity of any feature-based system. Finally, the efficiency of these methods is verified by assessing their ability to support real-time applications, through the evaluation of their time and space complexities over all investigated test scenarios.

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Authors and Affiliations

  1. Department of Communications and Computers Engineering, Higher Institute of Engineering, El-Shorouk Academy, El-Shorouk City, Egypt

    Eman S. Sabry & Salah Elagooz

  2. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Fathi E. Abd El-Samie

  3. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia

    Fathi E. Abd El-Samie

  4. Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Nirmeen A. El-Bahnasawy

  5. Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Ghada M. El-Banby

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  1. Eman S. Sabry
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  2. Salah Elagooz
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  4. Nirmeen A. El-Bahnasawy
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Correspondence to Eman S. Sabry.

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Sabry, E.S., Elagooz, S., El-Samie, F.E.A. et al. SIFT and ORB performance assessment for object identification in different test cases. J Opt (2023). https://doi.org/10.1007/s12596-023-01170-5

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