Thursday 28th of March 2024
 

Performance Assessment of Feature Detector-Descriptor Combination


A. M. M. Madbouly, M.Wafy and Mostafa-Sami M. Mostafa

Features detection and description among multiple images are widely used in many applications, e.g., feature matching, object categorization, 3D construction, image retrieval and object recognition. This paper evaluates combination performance of different feature detectors and descriptors. It will compare performance of detectors and descriptors combination on images under rotate, scale constraints and distortion such as illumination on different scene (bedroom, industrial and CALsuburb). An experimental result shows MinEigen detector has best result in number of detected key-points when handle rotate, scale and illumination and not affected with scene. SURF without external detector is the best when handle rotate and scale constraint in different levels and scene. FAST/SURF and Harris/FREAK are best combined against illumination distortion in different levels. This review introduces a brief introduction for providing a new research in feature detection field to find appropriate method according to their condition.

Keywords: local feature, detectors, descriptors Component, FREAK, SURF, BRISK, MSER, MinEigen.

Download Full-Text


ABOUT THE AUTHORS

A. M. M. Madbouly
Ph.D. student

M.Wafy
professor of computer science in information technology department,Faculty of Computers and Information, Helwan university, cairo, Egypt

Mostafa-Sami M. Mostafa
professor of computer science in Faculty of Computers and Information, Helwan university, Cairo, Egypt


IJCSI Published Papers Indexed By:

 

 

 

 
+++
About IJCSI

IJCSI is a refereed open access international journal for scientific papers dealing in all areas of computer science research...

Learn more »
Join Us
FAQs

Read the most frequently asked questions about IJCSI.

Frequently Asked Questions (FAQs) »
Get in touch

Phone: +230 911 5482
Email: info@ijcsi.org

More contact details »