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Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To address the problem related to the fact that the 3D reconstruction model of Pytorch3D is prone to produce holes, broken corners, and sender ranges, a Pytorch 3D 3D reconstruction method based on Interpolation Loop subdivision is proposed. This method employs a 3 d model with texture firstly, and samples images from the target model from 20 perspectives and positions to synthesize data sets. Subsequently, ResNe-50 network used to learn image features and the 3D convolution module is used to extract 3D features. Then the mesh subdivided by the Interpolation Loop is used as the deformation mesh. And the extracted features are projected onto the mesh vertices. Finally, the deformation is realized by the mesh using the graph convolutional neural network. And it completes the 3D reconstruction Experimental results show that the proposed method can reconstruct target model well. And the reconstructed loss value and the Hausdorff distance are significantly unproved. Therefore, the mesh reconstructed by our method is closer to the target model, and us visualization effect is better. [ABSTRACT FROM AUTHOR]

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem that the existing image retrieval technology was not effective when applied to ancient Chinese characters, a deformable convolution is introduced to build a convolutional neural networks (CNN) model suitable for ancient Chinese character images. Firstly, the features of ancient Chinese character image dataset were extracted with the CNN model. Then, the dimension reduction was realized by the principal component analysis. Finally, the cosine similarity between the query image and the candidate images was calculated and the hit images were sorted for users. The mean average precision of the proposed method is 70.42%, and the average retrieval time is 3.15 s. The experimental results show that the model can effectively extract the features of ancient Chinese character images and improve the precision of the retrieval method, which has advantages in the field of ancient Chinese character image retrieval. [ABSTRACT FROM AUTHOR]

In order to solve the problem of that lacking of feature information and low accuracy of weed recognition under the influence of natural illumination, a weed recognition method based on depth convolution neural network and color migration was proposed. There were 1 334 weed images collected under different natural illumination conditions (including cloudy, reflective, inverted and cloudy weather), and other 1 436 images were collected without the influence of natural illumination. Firstly, the optimized Reinhard algorithm was used to process the color migration of the sample image affected by the natural illumination to reduce the influence factors of the light, and the weed image was grayed out by ExG index graying and Otsu threshold image segmentation Then, through feature extraction and fusion, the Inception-V3 classifier constructed by convolution neural network was used for training, and the recognition rate was (Calculated by Softmax. Finally, the weed recognition rate with or without color migration algorithm and ExG grayscale and the influence of light environment on weed recognition were compared. The results show that the proposed method can improve the accuracy of weed identification by 15.6% and achieve a recognition rate of 90.01%. [ABSTRACT FROM AUTHOR]