Comparison of Feature detection using SIFT and ORB detector

In Recent days there are many developments in computer vision applications like self-driving cars, pedestrian detection, traffic flow analysis, road condition monitoring, medical applications, industry applications, image reconstruction, image matching, and object detection. In this all applications...

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Bibliographic Details
Main Authors: Indhumathi, S., Clement, J.Christopher
Format: Conference Proceeding
Language:English
Subjects:
Detectors
Feature detection
Feature extraction
Image matching
Industry applications
Medical services
Object detection
ORB
Roads
SIFT
Online Access:Request full text
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Summary:In Recent days there are many developments in computer vision applications like self-driving cars, pedestrian detection, traffic flow analysis, road condition monitoring, medical applications, industry applications, image reconstruction, image matching, and object detection. In this all applications basic or the foundation of the subject is detection this is the reason our focus of the paper is on the detector. This article describes a detection of features in the image. Different method of detectors are available based on the application we can choose the specific detectors. The Deep learning algorithm also shows the enhancement in the detection performance. In this paper, our focus is on SIFT and ORB detector which is widely used in computer vision applications. Keypoint detection of SIFT and ORB was measured and compared. The computation time of the ORB and SIFT is also necessary parameter in the detection process. The proposed work measured the computation time of both the detectors with different keypoints.
ISSN:2159-3450
DOI:10.1109/TENCON55691.2022.9977522