A New Feature Descriptor for Multimodal Image Registration Using Phase Congruency

Autor: Shuangming Zhao, Guorong Yu
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Normalization (statistics)
feature matching
Computer science
0211 other engineering and technologies
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
lcsh:Chemical technology
log-Gabor filter
Biochemistry
Article
Analytical Chemistry
Phase congruency
Histogram
Computer Science::Multimedia
0202 electrical engineering
electronic engineering
information engineering
Feature descriptor
lcsh:TP1-1185
Electrical and Electronic Engineering
Invariant (mathematics)
Instrumentation
021101 geological & geomatics engineering
business.industry
Pattern recognition
Spectral bands
Atomic and Molecular Physics
and Optics
phase congruency
Multimodal image
Computer Science::Computer Vision and Pattern Recognition
020201 artificial intelligence & image processing
Artificial intelligence
Precision and recall
business
Zdroj: Sensors
Volume 20
Issue 18
Sensors (Basel, Switzerland)
Sensors, Vol 20, Iss 5105, p 5105 (2020)
ISSN: 1424-8220
DOI: 10.3390/s20185105
Popis: Images captured by different sensors with different spectral bands cause non-linear intensity changes between image pairs. Classic feature descriptors cannot handle this problem and are prone to yielding unsatisfactory results. Inspired by the illumination and contrast invariant properties of phase congruency, here, we propose a new descriptor to tackle this problem. The proposed descriptor generation mainly involves three steps. (1) Images are convolved with a bank of log-Gabor filters with different scales and orientations. (2) A window of fixed size is selected and divided into several blocks for each keypoint, and an oriented magnitude histogram and the orientation of the minimum moment of a phase congruency-based histogram are calculated in each block. (3) These two histograms are normalized respectively and concatenated to form the proposed descriptor. Performance evaluation experiments on three datasets were carried out to validate the superiority of the proposed method. Experimental results indicated that the proposed descriptor outperformed most of the classic and state-of-art descriptors in terms of precision and recall within an acceptable computational time.
Databáze: OpenAIRE
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