Line Segments based Rotation Invariant Descriptor for Disparate Images

Computer vision-based real-time applications demand robust image matching approaches due to disparity in images. This can be achieved using descriptor vector with scale and rotation invariance capability. This paper presents a rotation invariant descriptor vector formation based on line point dualit...

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Bibliographic Details
Main Authors: Sharma, Teena, Agrawal, Pooja, Sahoo, Piyush, Verma, Nishchal K., Vasikarla, Shantaram
Format: Conference Proceeding
Language:English
Subjects:
Biological neural networks
descriptor
Detector
Detectors
Feature extraction
Image matching
Image segmentation
Lighting
Line segment
multi-layered neural network
Online Access:Request full text
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Summary:Computer vision-based real-time applications demand robust image matching approaches due to disparity in images. This can be achieved using descriptor vector with scale and rotation invariance capability. This paper presents a rotation invariant descriptor vector formation based on line point duality. The proposed descriptor uses a simple consistent method of key point detection. For obtaining the descriptor vector, line segments present in the input image are used. These line segments are located within a region of interest around obtained key points in the input image. The obtained descriptor vector is used for matching of disparate images. Experiments are carried out for four different image sets with rotation at the range of angles to validate the performance of the proposed descriptor in real-time. For comparative study, normalized match ratio is computed using multi-layered neural network with two hidden layers.
ISSN:2332-5615
DOI:10.1109/AIPR.2018.8707401