Application of Pseudo-color Image Feature-Level Fusion in Nondestructive Testing of Wire Ropes

2020 ◽  
Vol 20 (5) ◽  
pp. 1541-1553
Author(s):  
Juwei Zhang ◽  
Shiliang Lu ◽  
Jinbao Chen
Keyword(s):  
Nondestructive Testing ◽  
Color Image ◽  
Image Feature ◽  
Wire Ropes ◽  
Feature Level Fusion ◽  
Pseudo Color ◽  
Level Fusion

scholarly journals Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Tao Zhou ◽  
Huiling Lu ◽  
Junjie Zhang ◽  
Hongbin Shi
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Pulmonary Nodules ◽  
Image Feature ◽  
Ct Image ◽  
3 Dimensional ◽  
Detection Model ◽  
Proposed Model ◽  
Feature Level Fusion ◽  
Level Fusion

In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets. Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features. Secondly, these features are reduced for five times with rough set based on feature-level fusion. Thirdly, a grid optimization model is used to optimize the kernel function of support vector machine (SVM), which is used as a classifier to identify pulmonary nodules. Finally, lung CT images of 70 patients with pulmonary nodules are collected as the original samples, which are used to verify the effectiveness and stability of the proposed model by four groups’ comparative experiments. The experimental results show that the effectiveness and stability of the proposed model based on rough set feature-level fusion are improved in some degrees.

The Review of Feature Level Fusion of Multi-Focused Images Using Wavelets

2010 ◽  
Vol 2 (1) ◽  
pp. 28-38 ◽  
Author(s):  
K. Kannan ◽  
S. Arumuga Perumal ◽  
K. Arulmozhi
Keyword(s):  
Feature Level Fusion ◽  
Level Fusion

scholarly journals Restoration method of sootiness mural images based on dark channel prior and Retinex by bilateral filter

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Ning Cao ◽  
Shuqiang Lyu ◽  
Miaole Hou ◽  
Wanfu Wang ◽  
Zhenhua Gao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Environmental Changes ◽  
Color Image ◽  
Visual Quality ◽  
Bilateral Filter ◽  
Dark Channel Prior ◽  
Infrared Band ◽  
Dark Channel ◽  
Restoration Method ◽  
Pseudo Color

AbstractEnvironmental changes and human activities can cause serious degradation of murals, where sootiness is one of the most common problems of ancient Chinese indoor murals. In order to improve the visual quality of the murals, a restoration method is proposed for sootiness murals based on dark channel prior and Retinex by bilateral filter using hyperspectral imaging technology. First, radiometric correction and denoising through band clipping and minimum noise fraction rotation forward and inverse transform were applied to the hyperspectral data of the sootiness mural to produce its denoised reflectance image. Second, a near-infrared band was selected from the reflectance image and combined with the green and blue visible bands to produce a pseudo color image for the subsequent sootiness removal processing. The near-infrared band is selected because it is better penetrating the sootiness layer to a certain extent comparing to other bands. Third, the sootiness covered on the pseudo color image was preliminarily removed by using the method of dark channel prior and by adjusting the brightness of the image. Finally, the Retinex by bilateral filter was performed on the image to get the final restored image, where the sootiness was removed. The results show that the images restored by the proposed method are superior in variance, average gradient, information entropy and gray scale contrast comparing to the results from the traditional methods of homomorphic filtering and Gaussian stretching. The results also show the highest score in comprehensive evaluation of edges, hue and structure; thus, the method proposed can support more potential studies or sootiness removal in real mural paintings with more detailed information. The method proposed shows strong evidence that it can effectively reduce the influence of sootiness on the moral images with more details that can reveal the original appearance of the mural and improve its visual quality.

scholarly journals A Multi-Model Stereo Similarity Function Based on Monogenic Signal Analysis in Poisson Scale Space

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Jinjun Li ◽  
Hong Zhao ◽  
Chengying Shi ◽  
Xiang Zhou
Keyword(s):  
Color Image ◽  
Image Features ◽  
Image Feature ◽  
Similarity Function ◽  
Local Orientation ◽  
Stereo Images ◽  
Interest Points ◽  
Monogenic Signal ◽  
Local Color ◽  
Instantaneous Phase

A stereo similarity function based on local multi-model monogenic image feature descriptors (LMFD) is proposed to match interest points and estimate disparity map for stereo images. Local multi-model monogenic image features include local orientation and instantaneous phase of the gray monogenic signal, local color phase of the color monogenic signal, and local mean colors in the multiscale color monogenic signal framework. The gray monogenic signal, which is the extension of analytic signal to gray level image using Dirac operator and Laplace equation, consists of local amplitude, local orientation, and instantaneous phase of 2D image signal. The color monogenic signal is the extension of monogenic signal to color image based on Clifford algebras. The local color phase can be estimated by computing geometric product between the color monogenic signal and a unit reference vector in RGB color space. Experiment results on the synthetic and natural stereo images show the performance of the proposed approach.

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