An optimized scale-invariant feature transform (SIFT) using chamfer distance in image matching
Other Title(s)
تحسين خوارزمية مقياس المعادلة الثابت (SIFT) باستخدام مقياس شامفر في مطابقة الصور
Dissertant
al-Sharbaji, Tamara Abd al-Qadir
Thesis advisor
al-Kabnah, Khalid Abd al-Hafiz
University
Amman Arab University
Faculty
Collage of Computer Sciences and Informatics
Department
Department of Computer Science
University Country
Jordan
Degree
Master
Degree Date
2018
English Abstract
Computer vision is one of the most important elements used in the field of computer science so that it serves several fields, including the matching of images.
Matching multiple images is a main step in computer vision.
This can be reached by tracking the main points common to these images, and can determine the match between them.
Image matching among multiple images is a core and vital step in the field of computer vision.
This can be achieved by tracking correspondence over key points in an image and then matching among them can be identified.
ScaleInvariant Feature Transform (SIFT) is an image matching algorithm used to match objects of two images by extracting the feature points of target objects in each image.
SIFT suffers from long processing time due to embedded calculations which reduces the overall speed of the technique.
This research aims to enhance SIFT processing time by using Chamfer Distance Algorithm to find the distance between image descriptors instead of using Euclidian Distance Algorithm used in SIFT.
Chamfer Distance Algorithm requires less computational time than Euclidian Distance Algorithm because it selects the shortest path between any two points when the distance is computed.
To validate and evaluate the enhanced algorithm, A data set with (412) images including: (100) images with different degrees of rotation, (100) images with different intensity levels, (112) images with different measurement levels and (100) distorted images to different degrees were used; these images were applied according to four different criteria including (the number of key point in the original image, the number of key point in the testing image, the matching rate, the processing time).
The simulation results showed that the enhanced SIFT outperforms the ORB and the original SIFT in term of the processing time, and it reduces the overall processing time of the classical SIFT by (41%).
Main Subjects
Information Technology and Computer Science
Topics
No. of Pages
149
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Theoretical framework and literature review.
Chapter Three : The proposed scheme.
Chapter Four : Results and analysis.
Chapter Five : Conclusions and future works.
References.
American Psychological Association (APA)
al-Sharbaji, Tamara Abd al-Qadir. (2018). An optimized scale-invariant feature transform (SIFT) using chamfer distance in image matching. (Master's theses Theses and Dissertations Master). Amman Arab University, Jordan
https://search.emarefa.net/detail/BIM-932937
Modern Language Association (MLA)
al-Sharbaji, Tamara Abd al-Qadir. An optimized scale-invariant feature transform (SIFT) using chamfer distance in image matching. (Master's theses Theses and Dissertations Master). Amman Arab University. (2018).
https://search.emarefa.net/detail/BIM-932937
American Medical Association (AMA)
al-Sharbaji, Tamara Abd al-Qadir. (2018). An optimized scale-invariant feature transform (SIFT) using chamfer distance in image matching. (Master's theses Theses and Dissertations Master). Amman Arab University, Jordan
https://search.emarefa.net/detail/BIM-932937
Language
English
Data Type
Arab Theses
Record ID
BIM-932937
