Authors:
Hitesh Ganjoo
;
Venkateswarlu Karnati
;
Pramod Kumar
and
Raju Gupta
Affiliation:
Newgen Software Technologies Limited, India
Keyword(s):
SIFT, RANSAC, Blending, ANMS, GLOH, Probability Density Function and Image Registration.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Image Registration
;
Matching Correspondence and Flow
;
Medical Image Analysis
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Tracking of People and Surveillance
Abstract:
The paper addresses the issues in accuracy of various image-stitching algorithms used in the industry today on different types of real-time images. Our paper proposes a stitching algorithm for stitching images in one dimension. The most robust image stitching algorithms make use of feature descriptors to achieve invariance to image zoom, rotation and exposure change. The use of invariant feature descriptors in image matching and alignment makes them more accurate and reliable for a variety of images under different real-time conditions. We assess the accuracy of one such industrial tool, [AUTOSTICH], for our dataset and its underlying Scale Invariant Feature Transform (SIFT) descriptors. The tool’s performance is low in certain scenarios. Our proposed automatic stitching process can be broadly divided into 3 stages: Feature Point Extraction, Points Refinement, and Image Transformation & Blending. Our approach builds on the underlying way a casual end-user captures images through came
ras for panoramic image stitching. We have tested the proposed approach on a variety of images and the results show that the algorithm performs well in all scenarios.
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