Panoramic Image System Design in the Nearly Dark Environment

2013 ◽  
Vol 464 ◽  
pp. 304-309 ◽  
Author(s):  
Geng Chen ◽  
Qing Li ◽  
Hao Zhang
Keyword(s):  
Principal Component Analysis ◽  
Imaging System ◽  
Principal Component ◽  
Image Mosaic ◽  
Panoramic Image ◽  
Image System ◽  
Fusion Algorithm ◽  
Canny Operator ◽  
Image Edge ◽  
Wireless Module

Aiming at the need of borehole and ruins exploration in the nearly dark environment, a Panoramic Image System is proposed in this paper. consisting of a reciprocating motor, a steering gear with complete rotation, a miniature camera, a depth transducer and radio frequency wireless module. The camera was pushed to the specified location of the pipe by the motor and then the steering gear was rotated for panorama image acquisition. In order to achieve a seamless panoramic image without distortion, a fusion algorithm based on Principal Component Analysis (PCA) and an image mosaic algorithm for image edge extracting based on Canny operator were proposed. The imaging system has good usability and applicability.

scholarly journals Research on Image Fusion Algorithm Based on Nonsubsampled Shear Wave Transform and Principal Component Analysis

2022 ◽  
Vol 2146 (1) ◽  
pp. 012025
Author(s):  
Huiying Jia
Keyword(s):  
Principal Component Analysis ◽  
Computer Technology ◽  
Remote Monitoring ◽  
Principal Component ◽  
Development Trend ◽  
Fusion Algorithm ◽  
Monitoring Technology ◽  
Related Research ◽  
Development Status ◽  
Design And Implementation

Abstract With the development of network remote monitoring technology, its application in all walks of life has become more and more extensive. The application of remote monitoring technology makes the development of all walks of life more intelligent and informatized. This paper analyzes the development status of remote monitoring technology, and studies the design and implementation of remote network monitoring system based on computer technology. It also conducts related research on the future development trend of remote monitoring technology.

scholarly journals Image Fusion Algorithm Based on Contourlet Transform and PCNN for Detecting Obstacles in Forests

2015 ◽  
Vol 15 (1) ◽  
pp. 116-125 ◽  
Author(s):  
Zheng Yu ◽  
Lei Yan ◽  
Ning Han ◽  
Jinhao Liu
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Wavelet Transform ◽  
Image Fusion ◽  
Principal Component ◽  
Experimental Results ◽  
Contourlet Transform ◽  
Detection Accuracy ◽  
Fusion Algorithm ◽  
Infrared Thermal Images

Abstract In this paper the image fusion algorithm based on Contourlet transform and Pulse Coupled Neural Network (PCNN) was proposed to improve the performance of the image fusion in the detection accuracy of obstacles in forests. At the same time, the wavelet transform and the Principal Component Analysis (PCA) were simulated for comparison with the proposed algorithm. Then visible and infrared thermal images were collected in a forest. The experimental results have shown that the fused images using the method proposed provided a better understanding of the reality, enhanced images’ clarity and eliminated factors which provided shelters for targets.

Remote sensing image fusion via RPCA and adaptive PCNN in NSST domain

2018 ◽  
Vol 16 (05) ◽  
pp. 1850037 ◽  
Author(s):  
Kang Zhang ◽  
Yongdong Huang ◽  
Cheng Zhao
Keyword(s):  
Remote Sensing ◽  
Principal Component Analysis ◽  
Image Fusion ◽  
High Frequency ◽  
Principal Component ◽  
Fusion Rule ◽  
Remote Sensing Image ◽  
Component Analysis ◽  
Fusion Algorithm ◽  
Remote Sensing Image Fusion

In order to improve fused image quality of multi-spectral (MS) image and panchromatic (PAN) image, a new remote sensing image fusion algorithm based on robust principal component analysis (RPCA) and non-subsampled shearlet transform (NSST) is proposed. First, the first principle component PC1 of MS image is extracted via principal component analysis (PCA). Then, the component PC1 and PAN image are decomposed by NSST to get the low and high frequency subbands, respectively. For the low frequency subband, the sparse matrix of PAN image by RPCA decomposition is used to guide the fusion rule; for the high frequency subbands, the fusion rule employed is based on adaptive PCNN model. Finally, the fusion image is obtained by inverse NSST transform and inverse PCA transform. The experimental results illustrate that the proposed fusion algorithm outperforms other classical fusion algorithms (PCA, Curvelet, NSCT, NSST and NSCT-PCNN) in terms of visual quality and objective evaluation in whole, and achieve better fusion performance.

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