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A Fourier Descriptor of 2D Shapes Based on Multiscale Centroid Contour Distances Used in Object Recognition in Remote
Yan Zheng1, Baolong Guo2, Zhijie Chen3
1Institute of Intelligent Control and Image Engineering, Xidian University, Taibai Road, Xi'an 710071, China. yanzheng@stu.xidian.edu.cn.
Sensors (Basel, Switzerland)
|January 27, 2019
Summary
This study introduces the Fourier descriptor based on multiscale centroid contour distance (FMSCCD), a novel shape descriptor for remote sensing. FMSCCD offers strong discriminability and low computational cost, making it practical for real-world applications.
Area of Science:
- Remote Sensing
- Image Analysis
- Computer Vision
Background:
- Shape descriptors are crucial for object identification in remote sensing imagery.
- Existing descriptors often exhibit high computational costs, limiting their practical use.
- There is a need for efficient and accurate shape descriptors.
Purpose of the Study:
- To propose a new shape descriptor, FMSCCD (Fourier descriptor based on multiscale centroid contour distance).
- To evaluate the discriminability and computational efficiency of FMSCCD.
- To demonstrate the compatibility of FMSCCD with other features.
Main Methods:
- Developed FMSCCD, a frequency domain descriptor integrating centroid contour distance (CCD) method, multiscale analysis, and Fourier transform.
- Tested FMSCCD on three distinct image databases.
- Compared FMSCCD's performance against existing shape descriptors.
Main Results:
- FMSCCD demonstrated strong discriminability in experimental evaluations.
- The descriptor exhibits a low computational cost, enhancing operational efficiency.
- FMSCCD showed excellent compatibility with other feature descriptors.
Conclusions:
- FMSCCD is a computationally efficient and highly discriminative shape descriptor for remote sensing.
- The proposed descriptor is suitable for practical applications due to its balance of performance and speed.
- FMSCCD offers a valuable addition to the toolkit for remote sensing image analysis.
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