scholarly journals The Effect of 3D Image Virtual Reconstruction Based on Visual Communication

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Li Xu ◽  
Ling Bai ◽  
Lei Li

Considering the problems of poor effect, long reconstruction time, large mean square error (MSE), low signal-to-noise ratio (SNR), and structural similarity index (SSIM) of traditional methods in three-dimensional (3D) image virtual reconstruction, the effect of 3D image virtual reconstruction based on visual communication is proposed. Using the distribution set of 3D image visual communication feature points, the feature point components of 3D image virtual reconstruction are obtained. By iterating the 3D image visual communication information, the features of 3D image virtual reconstruction in visual communication are decomposed, and the 3D image visual communication model is constructed. Based on the calculation of the difference of 3D image texture feature points, the spatial position relationship of 3D image feature points after virtual reconstruction is calculated to complete the texture mapping of 3D image. The deep texture feature points of 3D image are extracted. According to the description coefficient of 3D image virtual reconstruction in visual communication, the virtual reconstruction results of 3D image are constrained. The virtual reconstruction algorithm of 3D image is designed to realize the virtual reconstruction of 3D image. The results show that when the number of samples is 200, the virtual reconstruction time of this paper method is 2.1 s, and the system running time is 5 s; the SNR of the virtual reconstruction is 35.5 db. The MSE of 3D image virtual reconstruction is 3%, and the SSIM of virtual reconstruction is 1.38%, which shows that this paper method can effectively improve the ability of 3D image virtual reconstruction.

Author(s):  
Abbas F. H. Alharan ◽  
Hayder K. Fatlawi ◽  
Nabeel Salih Ali

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jing Yin ◽  
Jong Hoon Yang

In order to solve the problems of poor user experience and low human-computer interaction efficiency, this paper designs a 3D image virtual reconstruction system based on visual communication effects. First, the functional framework diagram and hardware structure diagram of the image 3D reconstruction system are given. Then, combined with the basic theory of visual communication design, the characteristics of different elements in the three-dimensional image system and reasonable visual communication forms are analyzed, and design principles are proposed to improve user experience and communication efficiency. After the input image is preprocessed by median filtering, a three-dimensional reconstruction algorithm based on the image sequence is used to perform a three-dimensional reconstruction of the preprocessed image. The performance of the designed system was tested in a comparison form. We optimize the original hardware structure, expand the clock module, and use the chip to improve the data processing efficiency; in the two-dimensional image; we read the main information, through data conversion, display it in three-dimensional form, select the feature area, extract the image feature, calculate the key physical coordinate points, complete the main code compilation, use visual communication technology to feed back the display visual elements to the 3D image, and complete the design of the 3D image virtual reconstruction system. The test results showed that the application of visual communication technology to the virtual reconstruction of 3D images can effectively remove noise and make the edge area of the image clear, which can meet the needs of users compared with the reconstruction results of the original system. Visual C++ and 3DMAX are used as the system design platform, and three-dimensional image visualization and roaming are realized through OpenGL. Experimental results show that the designed system has better reconstruction accuracy and user satisfaction.


2012 ◽  
Vol 605-607 ◽  
pp. 2240-2244 ◽  
Author(s):  
Qing Liu ◽  
Xi Ping Liu ◽  
Li Jun Zhang ◽  
Li Min Zhao

In order to effectively extract Chinese herbal medicine (CHM) image feature information, and automatically identify the CHM images, a method of CHM image feature extraction and recognition based on gray level co-occurrence matrix (GLCM) is put forward. Firstly, on the basis of the acquired colour CHM image is converted to the gray-scale image, the four texture feature parameters, angular second moment (ASM),inertia moment (IM),entropy and correlation are extracted utilizing the GLCM, and then CHM image recognition is carried out by using those feature values with resistance geometric distortion. The experimental results show that the method of generating GLCM and extraction of image texture features can effectively identify the CHM image, which can bring significance to the modern recognition and identification of CHM.


2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.


Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1839
Author(s):  
Yutong Zhang ◽  
Jianmei Song ◽  
Yan Ding ◽  
Yating Yuan ◽  
Hua-Liang Wei

Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ziting Zhao ◽  
Tong Liu ◽  
Xudong Zhao

Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for getting a better accuracy of the bughole classification. A texture feature deriving from the Gabor filter and gray-level run lengths is extracted in concrete surface images. Interpretable variables, which are also the components of the feature, are selected according to a presented cumulative voting strategy. An ensemble classifier with its base classifier automatically assigned is provided to detect whether a surface void exists in an image or not. Experimental results on 1000 image samples indicate the effectiveness of our method with a comparable prediction accuracy and model explicable.


Author(s):  
Jenicka S.

Texture feature is a decisive factor in pattern classification problems because texture features are not deduced from the intensity of current pixel but from the grey level intensity variations of current pixel with its neighbors. In this chapter, a new texture model called multivariate binary threshold pattern (MBTP) has been proposed with five discrete levels such as -9, -1, 0, 1, and 9 characterizing the grey level intensity variations of the center pixel with its neighbors in the local neighborhood of each band in a multispectral image. Texture-based classification has been performed with the proposed model using fuzzy k-nearest neighbor (fuzzy k-NN) algorithm on IRS-P6, LISS-IV data, and the results have been evaluated based on confusion matrix, classification accuracy, and Kappa statistics. From the experiments, it is found that the proposed model outperforms other chosen existing texture models.


2013 ◽  
Vol 42 (11) ◽  
pp. 1375-1380 ◽  
Author(s):  
庞春颖 PANG Chunying ◽  
刘记奎 LIU Jikui

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