At present, 3D modeling technology is simplified due to the improvement of image segmentation algorithm and feature point detection and other related technologies, so it is widely used in major museums. For example, s...
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At present, 3D modeling technology is simplified due to the improvement of image segmentation algorithm and feature point detection and other related technologies, so it is widely used in major museums. For example, science and technology museums can display emerging scientific and technological models by using 3D modeling, while museums and art galleries can display works of art and antiques by using 3D modeling. By using 3D modeling to display, tourists can effectively enhance the sense of substitution. 3D sculpture is a brand new product under this technical background, which can enhance the vividness of the exhibition of works in the Expo Park, thus bringing tourists a better tour experience. Based on this background, this study introduces the image segmentation algorithm to adjust and optimize 3d sculpture works, and introduces the main optimization process. Then, modeling, modification and model reconstruction are carried out through the collection and calculation of multi angle pictures. In this process, file content import, model data import and 3d object image import are required. The simulation results show that the optimized 3d model can effectively improve the frame rate performance of the model, but also reduce the volume, number of faces and vertex points of the model to a certain extent. It is an effective and innovative research work to optimize 3d sculpture by using image segmentation algorithm. This paper introduces the image segmentation algorithm into the field of 3d sculpture to achieve technological innovation.
With the continuous progress of Internet technology, so there is a higher demand for English translation. In this context, this paper realizes the design and improvement of the online English translation system by com...
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With the continuous progress of Internet technology, so there is a higher demand for English translation. In this context, this paper realizes the design and improvement of the online English translation system by combining the image segmentation algorithm. After the experimental test of the algorithm of this system, if there is a lot of salt-and-pepper noise in the target image in the actual system application process, the method designed in this paper can reflect a better processing effect. Based on the analysis of performance parameters, this method can effectively process Gaussian noise images with high precision. After the completion of the system design, this paper tests the system. From the test and operation results, the software has reached the design objectives and completed the work tasks. The English translation online teaching system can form a safe, stable and high-bandwidth English translation teaching network and can realize the connection of the whole network. Users can share various software and hardware resources, completed the design of a translation platform that can realize rapid English communication, share experience and discuss, to effectively improve the learner's initiative. In this paper, an effective online teaching system is designed by introducing image segmentation algorithm into the field of English translation.
To improve the boundary processing ability and anti-noise performance of image segmentation algorithm?a neutrosophic fuzzy clustering algorithm based on non-local information is proposed here. Initially, the proposed ...
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To improve the boundary processing ability and anti-noise performance of image segmentation algorithm?a neutrosophic fuzzy clustering algorithm based on non-local information is proposed here. Initially, the proposed approach uses the data distribution of deterministic subset to determine the clustering centre of the fuzzy subset. Besides, the fuzzy non-local pixel correlation is introduced into the neutrosophic fuzzy mean clustering algorithm. The experimental results on synthetic images, medical images and natural images show that the proposed method is more robust and more accurate than the existing clustering segmentation methods.
To address the problem of the traditional human posture recognition system being easily affected by environmental factors such as noise, a human posture recognition system based on Internet of Things technology and an...
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To address the problem of the traditional human posture recognition system being easily affected by environmental factors such as noise, a human posture recognition system based on Internet of Things technology and an image segmentation algorithm was designed. To do this, we selected the attitude sensor, designed the attitude sensor structure based on micro electro mechanical system technology, and had a variety of selectable data output modes. A 36 V power supply was selected as the circuit design index to provide sufficient power for the system. The main control chip was designed, and its state information description and external output were installed in a single chip or the same component. The denoising process based on image segmentation algorithm was designed, the error correction model was constructed, the measured values were normalized, and the imagesegmentation model was constructed. We extracted the node features of human body posture, used the image segmentation algorithm to construct the structure diagram of human body posture nodes, located the local recognition area of the node structure map, and designed the human body posture recognition system. The experimental results show that the designed human gesture recognition system has better effect and better recognition performance.
imagesegmentation is a process of partitioning an image into non-overlapping regions. Existing unsupervised imagesegmentation methods include level set, automatic thresholding and region-based CV mode and so on. How...
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imagesegmentation is a process of partitioning an image into non-overlapping regions. Existing unsupervised imagesegmentation methods include level set, automatic thresholding and region-based CV mode and so on. However, imagesegmentation as a key technology in the field of image processing has not been solved indeed, especially for images with complex texture. For this reason, the authors proposed a novel image segmentation algorithm based on NSST and the vector-valued Chan-Vese (C-V) model. First, they obtained a multi-scale representation by exploiting the non-subsampled shearlet transform (NSST) to extract multi-dimensional data in the image. Afterwards, they gave the vector-valued C-V model, and applied it to all subbands of NSST, which are treated as a vector-valued image. By comparing with other class methods, the experimental results show that the proposed method has better visual effects and lower error rates. But at the same time, it is a little time consuming. The proposed method is reasonable and effective, by taking full advantages of each subband's directional information during its diffusion process, compared with traditional C-V model.
Fuzzy clustering algorithm is the main method of imagesegmentation, but it can't be widely used in various fields. Therefore, an image segmentation algorithm based on improved fuzzy clustering was proposed in thi...
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Fuzzy clustering algorithm is the main method of imagesegmentation, but it can't be widely used in various fields. Therefore, an image segmentation algorithm based on improved fuzzy clustering was proposed in this paper. The fuzzy clustering theory and analysis method were described in detail, and the research of image segmentation algorithm was discussed in this paper. And then the method of sub graph decomposition and region merging was used to improve the clustering method of fuzzy C mean clustering image segmentation algorithm and the algorithm was verified by an example. The results showed that the algorithm was feasible. Compared with other existing algorithms, the algorithm had more advantages in running time and imagesegmentation accuracy.
Remote sensing and satellite information's are very useful for the geospatial analysis of surface and subsurface features. In the modern day-to-day scenario, the landforms and its features to be monitored and mana...
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ISBN:
(纸本)9781538694831
Remote sensing and satellite information's are very useful for the geospatial analysis of surface and subsurface features. In the modern day-to-day scenario, the landforms and its features to be monitored and managed is a very challenging one and Remote Sensing plays a crucial role in the society. Analysis of Satellite imageries using geospatial techniques is playing a vibrant role in the earth science system. Earth contains rivers, streams, tanks, water bodies as natural formations and delineating the features itself with the image segmentation algorithm is an elegant task over remote sensing imagery. Feature extraction and imagesegmentation are extensively used in the field of medicine for X-ray and scan images, satellite imaging, pattern identification, features and face interpretation and various applications of engineering.
In order to improve the segmentation accuracy of adhered rice images, an automatic segmentationalgorithm for adhered rice images based on background skeleton features is proposed. The experimental results show that t...
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ISBN:
(数字)9781728161068
ISBN:
(纸本)9781728161075
In order to improve the segmentation accuracy of adhered rice images, an automatic segmentationalgorithm for adhered rice images based on background skeleton features is proposed. The experimental results show that the proposed algorithm can adapt well to the adhesion segmentation of different grains under complex condition. Compared with the classic distance transformation watershed algorithm and the improved watershed algorithm, algorithm accuracy has improved a lot, and the formed grain segmentation boundary is smoother, with less influence on the shape.
Clustering analysis is an unsupervised classification method,which classifies the samples by classifying the *** the absence of prior knowledge,the imagesegmentation can be done by cluster *** paper gives the concept...
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Clustering analysis is an unsupervised classification method,which classifies the samples by classifying the *** the absence of prior knowledge,the imagesegmentation can be done by cluster *** paper gives the concept of clustering analysis and two common cluster algorithms,which are FCM and K-Means *** applications of the two clustering algorithms in imagesegmentation are explored in the paper to provide some references for the relevant researchers.
An automatic segmentationalgorithm which combine visual attention mechanism and GrabCut is presented, in order to meet the automation needs of imagesegmentation. Firstly, using visual attention mechanism to get the ...
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ISBN:
(纸本)9781510822030
An automatic segmentationalgorithm which combine visual attention mechanism and GrabCut is presented, in order to meet the automation needs of imagesegmentation. Firstly, using visual attention mechanism to get the initial target areas, and then compensate the edge of the areas, finally using GrabCut to get accurate segmentation results. The experimental results show that this method can finish the imagesegmentation automatically and has good accuracy.
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