Detailed Review of the Scale Invariant Feature Transform (SIFT) Algorithm; Concepts, Indices and Applications

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Hossein Nejad Zahra; Agahi Hamed; Mahmoodzadeh Azar

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Journal: MACHINE VISION AND IMAGE PROCESSING Year:2020 | Volume:7 | Issue:1 Page(s): 165-190

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Detailed Review of the Scale Invariant Feature Transform (SIFT) Algorithm; Concepts, Indices and Applications

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Hossein Nejad Zahra | Agahi Hamed | Mahmoodzadeh Azar | Issue Writer Certificate

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Scale Invariant Feature Transform (SIFT) algorithm

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Abstract

The Scale Invariant Feature Transform (SIFT) algorithm is one of the most widely used algorithms in the machine vision field on which researchers have extensively studied and improved. SIFT is one of the common local Detectors used in image registration, image mosaicking, copy-move image forgery, and etc. In this review paper, along with introducing the SIFT algorithm, the applications, pros and cons, modifications, categories and new research approaches in this algorithm are discussed. In addition, via four experiments, different aspects of this algorithm have been evaluated. This paper can help image processing researchers when utilizing the SIFT algorithm. The aim of the authors was to explore all the aspects of this algorithm.

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APA: Copy

Hossein Nejad, Zahra, Agahi, Hamed, & Mahmoodzadeh, Azar. (2020). Detailed Review of the Scale Invariant Feature Transform (SIFT) Algorithm; Concepts, Indices and Applications. MACHINE VISION AND IMAGE PROCESSING, 7(1 ), 165-190. SID. https://sid.ir/paper/359878/en

Vancouver: Copy

Hossein Nejad Zahra, Agahi Hamed, Mahmoodzadeh Azar. Detailed Review of the Scale Invariant Feature Transform (SIFT) Algorithm; Concepts, Indices and Applications. MACHINE VISION AND IMAGE PROCESSING[Internet]. 2020;7(1 ):165-190. Available from: https://sid.ir/paper/359878/en

IEEE: Copy

Zahra Hossein Nejad, Hamed Agahi, and Azar Mahmoodzadeh, “Detailed Review of the Scale Invariant Feature Transform (SIFT) Algorithm; Concepts, Indices and Applications,” MACHINE VISION AND IMAGE PROCESSING, vol. 7, no. 1 , pp. 165–190, 2020, [Online]. Available: https://sid.ir/paper/359878/en

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