The invoice reimbursement process is very cumbersome and requires manual entry of key information in the invoice, which wastes a lot of manpower and time. Therefore, it is particularly important to design an algorithm for intelligent identification of invoice information. Traditional algorithms can identify information from scanned invoice images. However, since in our country, most of the invoice information is Chinese characters, the current recognition algorithm has a certain degree of difficulty in identifying Chinese characters, and garbled characters will appear. Therefore, this article combines the CTPN text detection algorithm with the DesNets text recognition algorithm, and uses this algorithm to detect and recognize text on the information extracted from the invoice area image. Experiments show that the model outperforms the comparison model, with a recognition accuracy of up to 99.79%.
{"title":"Research on automatic identification algorithm of invoice information","authors":"Liangyu Jiao, Hui Li","doi":"10.1117/12.3014488","DOIUrl":"https://doi.org/10.1117/12.3014488","url":null,"abstract":"The invoice reimbursement process is very cumbersome and requires manual entry of key information in the invoice, which wastes a lot of manpower and time. Therefore, it is particularly important to design an algorithm for intelligent identification of invoice information. Traditional algorithms can identify information from scanned invoice images. However, since in our country, most of the invoice information is Chinese characters, the current recognition algorithm has a certain degree of difficulty in identifying Chinese characters, and garbled characters will appear. Therefore, this article combines the CTPN text detection algorithm with the DesNets text recognition algorithm, and uses this algorithm to detect and recognize text on the information extracted from the invoice area image. Experiments show that the model outperforms the comparison model, with a recognition accuracy of up to 99.79%.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The unidirectional LSTM based music generation model does not take into account the influence of future information when generating music. It solely focuses on learning the dependencies of the current moment on past information, resulting in music with poor stability and subpar quality. To address this issue, we have developed a music generation model based on bidirectional LSTM. During the training phase, this model effectively captures musical information from both past and future time steps, resulting in a probability distribution of musical elements that closely approximates real-world music. This, in turn, leads to enhanced structural stability and improved music quality in the generated compositions. Finally, we conducted validation experiments on our proposed approach, and the results unequivocally demonstrate its effectiveness.
{"title":"A music generation model based on Bi-LSTM","authors":"yong bai","doi":"10.1117/12.3014368","DOIUrl":"https://doi.org/10.1117/12.3014368","url":null,"abstract":"The unidirectional LSTM based music generation model does not take into account the influence of future information when generating music. It solely focuses on learning the dependencies of the current moment on past information, resulting in music with poor stability and subpar quality. To address this issue, we have developed a music generation model based on bidirectional LSTM. During the training phase, this model effectively captures musical information from both past and future time steps, resulting in a probability distribution of musical elements that closely approximates real-world music. This, in turn, leads to enhanced structural stability and improved music quality in the generated compositions. Finally, we conducted validation experiments on our proposed approach, and the results unequivocally demonstrate its effectiveness.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The three-dimensional laser scanning technology is a non-contact measurement of objects by using a three-dimensional scanner. It is a new surveying and mapping technology, also known as real-life replication technology. This technology can completely reconstruct the surface of the scanned object through the 360-degree rotation of the laser emitter. The accuracy of point cloud data is very high. Each three-dimensional data in the laser point cloud is the real three-dimensional data X, Y, and Z coordinates of the building target, which makes the post-processing data true and reliable, and its accuracy reaches 3mm ~ 6mm. The working principle of this technology is through reverse three-dimensional data acquisition and model reconstruction, on-site photography, texture mapping for the later period, can truly achieve 1 : 1 physical restoration, and then quickly reconstruct the three-dimensional model of the building, so as to obtain the building point, line, surface, body and other mapping data. Three-dimensional laser scanning technology has played a huge advantage in the reconstruction and protection of wooden structure drum tower.
{"title":"Application analysis of three-dimensional laser scanning technology in the protection of dong drum tower in Sanjiang county","authors":"Bin Huang","doi":"10.1117/12.3014534","DOIUrl":"https://doi.org/10.1117/12.3014534","url":null,"abstract":"The three-dimensional laser scanning technology is a non-contact measurement of objects by using a three-dimensional scanner. It is a new surveying and mapping technology, also known as real-life replication technology. This technology can completely reconstruct the surface of the scanned object through the 360-degree rotation of the laser emitter. The accuracy of point cloud data is very high. Each three-dimensional data in the laser point cloud is the real three-dimensional data X, Y, and Z coordinates of the building target, which makes the post-processing data true and reliable, and its accuracy reaches 3mm ~ 6mm. The working principle of this technology is through reverse three-dimensional data acquisition and model reconstruction, on-site photography, texture mapping for the later period, can truly achieve 1 : 1 physical restoration, and then quickly reconstruct the three-dimensional model of the building, so as to obtain the building point, line, surface, body and other mapping data. Three-dimensional laser scanning technology has played a huge advantage in the reconstruction and protection of wooden structure drum tower.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Coal gangue sorting is an important link in the process of coal mining and processing, which can effectively reduce the difficulty and cost of coal post-processing. Aiming at the problems of complicated sorting process and low sorting efficiency of coal gangue, a coal gangue sorting method based on deep learning was proposed. The method is based on the YOLO v7 deep learning algorithm, and it achieves real-time detection of coal gangue by creating a coal gangue dataset and training the detection model. By constructing a coal gangue sorting platform, the capture of target gangue has been achieved. The experimental results show that the mAP of YOLO v7 model is 96.70%, and the detection speed is 69fps, which has significant advantages compared to YOLO v5, SSD and Faster RCNN algorithms.
{"title":"Coal gangue sorting based on deep learning","authors":"Panliang Yang, Bin Zhu, Lianquan Ji, Peng Nie","doi":"10.1117/12.3014357","DOIUrl":"https://doi.org/10.1117/12.3014357","url":null,"abstract":"Coal gangue sorting is an important link in the process of coal mining and processing, which can effectively reduce the difficulty and cost of coal post-processing. Aiming at the problems of complicated sorting process and low sorting efficiency of coal gangue, a coal gangue sorting method based on deep learning was proposed. The method is based on the YOLO v7 deep learning algorithm, and it achieves real-time detection of coal gangue by creating a coal gangue dataset and training the detection model. By constructing a coal gangue sorting platform, the capture of target gangue has been achieved. The experimental results show that the mAP of YOLO v7 model is 96.70%, and the detection speed is 69fps, which has significant advantages compared to YOLO v5, SSD and Faster RCNN algorithms.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High resolution synthetic aperture radar system will produce aliasing problem in the sliding spotlight mode, we propose a solution based on sub-apertures splice to solving the program, the simulation and analysis of airborne mode are given. The method use the sub-aperture division, respectively, each aperture frequency shift, scaling, compression distance, azimuth compression performed prior to stitching, the data will be spliced after azimuth compression and windowing. Simulation results show that the algorithm can effectively solve the problem.
{"title":"Anti aliasing algorithm based on sub aperture stitching","authors":"Dekun Li","doi":"10.1117/12.3014409","DOIUrl":"https://doi.org/10.1117/12.3014409","url":null,"abstract":"High resolution synthetic aperture radar system will produce aliasing problem in the sliding spotlight mode, we propose a solution based on sub-apertures splice to solving the program, the simulation and analysis of airborne mode are given. The method use the sub-aperture division, respectively, each aperture frequency shift, scaling, compression distance, azimuth compression performed prior to stitching, the data will be spliced after azimuth compression and windowing. Simulation results show that the algorithm can effectively solve the problem.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to improve the accuracy and real-time performance of cotton top bud recognition, an improved YOLOv5s target real-time detection model is proposed. First, the SE module and the CBAM module in the attention mechanism are added to optimize the weight ratio of channel attention and spatial attention to improve accuracy; then the BiFPN structure of bidirectional weighted features is introduced to strengthen the fusion between high-level features and low-level features; finally, a new bounding box regression loss function EIoU is used for ablation experiments, and more position information of cotton buds can be obtained by reducing the bounding box loss. The experimental results show that, by applying the improved algorithm in the identification of cotton top buds, compared with the original YOLOv5s model, the accuracy of the C3SE-4l+BiFPN+EIoU model has increased by 7.9%, the recall rate has increased by 2.8%, and the average precision an increase of 5.7%. These improvements and optimizations provide a new idea and method, which can provide a more efficient solution for the identification of cotton top buds.
{"title":"Improved YOLOv5s recognition of cotton top buds with fusion of attention and feature weighting","authors":"Lei Yin, Jian Wu, Qikong Liu, Wenxiong Wu","doi":"10.1117/12.3014605","DOIUrl":"https://doi.org/10.1117/12.3014605","url":null,"abstract":"In order to improve the accuracy and real-time performance of cotton top bud recognition, an improved YOLOv5s target real-time detection model is proposed. First, the SE module and the CBAM module in the attention mechanism are added to optimize the weight ratio of channel attention and spatial attention to improve accuracy; then the BiFPN structure of bidirectional weighted features is introduced to strengthen the fusion between high-level features and low-level features; finally, a new bounding box regression loss function EIoU is used for ablation experiments, and more position information of cotton buds can be obtained by reducing the bounding box loss. The experimental results show that, by applying the improved algorithm in the identification of cotton top buds, compared with the original YOLOv5s model, the accuracy of the C3SE-4l+BiFPN+EIoU model has increased by 7.9%, the recall rate has increased by 2.8%, and the average precision an increase of 5.7%. These improvements and optimizations provide a new idea and method, which can provide a more efficient solution for the identification of cotton top buds.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The operational level and service life of oil-immersed bushings are related to the moisture content in their cellulosic insulation layers. The authors proposed a novel method based on Taylor series modeling and grey relational analysis to assess the moisture content in the cellulosic insulation of oil-immersed bushings. Unlike conventional methods relying solely on frequency domain spectroscopy (FDS) data from certain frequency ranges, this method uses Taylor series to model the full spectrum and extracts a set of moisture-related feature parameters, thus avoiding dependence on specific measurement points or ranges. To establish the relationship between feature parameters and moisture levels, a database was constructed in this study. Grey relational analysis was used to propose an alternative method for assessing the moisture in bushing cellulosic insulation. Results show the average relative error between the estimated and measured moisture was less than 15.7%. Overall, the proposed method in this study provides an effective approach for assessing the moisture in the cellulosic insulation of oil-immersed bushings.
{"title":"Moisture detection of oil-immersed bushing insulation based on color level analysis","authors":"Hongmin Ren, Denan Wang, Danni Duan","doi":"10.1117/12.3014683","DOIUrl":"https://doi.org/10.1117/12.3014683","url":null,"abstract":"The operational level and service life of oil-immersed bushings are related to the moisture content in their cellulosic insulation layers. The authors proposed a novel method based on Taylor series modeling and grey relational analysis to assess the moisture content in the cellulosic insulation of oil-immersed bushings. Unlike conventional methods relying solely on frequency domain spectroscopy (FDS) data from certain frequency ranges, this method uses Taylor series to model the full spectrum and extracts a set of moisture-related feature parameters, thus avoiding dependence on specific measurement points or ranges. To establish the relationship between feature parameters and moisture levels, a database was constructed in this study. Grey relational analysis was used to propose an alternative method for assessing the moisture in bushing cellulosic insulation. Results show the average relative error between the estimated and measured moisture was less than 15.7%. Overall, the proposed method in this study provides an effective approach for assessing the moisture in the cellulosic insulation of oil-immersed bushings.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Concrete will produce cracks under the long-term action of loads and affect the building safety, due to the unsatisfactory accuracy and efficiency of manual detection of concrete cracks, a concrete crack recognition and detection method based on improved CNN and DoG operator is proposed. Firstly, the recognition ability is trained by CNN to locate the valuable images from the image dataset, and then the locating crack images are greyscaled, denoised using bilateral filtering method, considering that the filtering will make the image edges blurred, the DoG operator is used to detect the completeness of the edges, and then the image is binary transformed by selecting thresholds through the one-dimensional Otus segmentation method, and the binary map is opened by the open operation, to fill in the broken parts within the cracks and protect the crack edges, and finally the length, width, and rotation angle of the crack are calculated by mapping the complete straight lines present in the crack through Hough space. The experimental results show that the method can accurately identify and detect crack features of different shapes with superior detection accuracy.
混凝土在荷载的长期作用下会产生裂缝，影响建筑物的安全，由于人工检测混凝土裂缝的精度和效率不理想，因此提出了一种基于改进型 CNN 和 DoG 算子的混凝土裂缝识别检测方法。首先，通过 CNN 训练识别能力，从图像数据集中定位出有价值的图像，然后对定位出的裂缝图像进行灰度化处理，使用双边滤波方法去噪，考虑到滤波会使图像边缘模糊，使用 DoG 算子检测边缘的完整性、然后通过一维 Otus 分割方法选择阈值对图像进行二值化转换，并通过打开操作打开二值图，以填充裂缝内的破碎部分并保护裂缝边缘，最后通过 Hough 空间映射裂缝中存在的完整直线，计算出裂缝的长度、宽度和旋转角度。实验结果表明，该方法能准确识别和检测不同形状的裂纹特征，检测精度高。
{"title":"Concrete crack identification and detection method based on improved CNN and DoG operator","authors":"Haohua Luo, Yulun Wu, Yaoyang Liang, Jinshuai Ren, Zhiming Wang, Yilu Huang","doi":"10.1117/12.3014461","DOIUrl":"https://doi.org/10.1117/12.3014461","url":null,"abstract":"Concrete will produce cracks under the long-term action of loads and affect the building safety, due to the unsatisfactory accuracy and efficiency of manual detection of concrete cracks, a concrete crack recognition and detection method based on improved CNN and DoG operator is proposed. Firstly, the recognition ability is trained by CNN to locate the valuable images from the image dataset, and then the locating crack images are greyscaled, denoised using bilateral filtering method, considering that the filtering will make the image edges blurred, the DoG operator is used to detect the completeness of the edges, and then the image is binary transformed by selecting thresholds through the one-dimensional Otus segmentation method, and the binary map is opened by the open operation, to fill in the broken parts within the cracks and protect the crack edges, and finally the length, width, and rotation angle of the crack are calculated by mapping the complete straight lines present in the crack through Hough space. The experimental results show that the method can accurately identify and detect crack features of different shapes with superior detection accuracy.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The detection data of energy consumption and carbon emissions may be affected by equipment failure, sensor error, incomplete data collection and other factors, resulting in low detection accuracy. Based on this, an online detection method of energy consumption and carbon emissions measurement based on hybrid particle swarm optimization algorithm is proposed. Analyze the measurement principles of energy consumption and carbon emissions. On this basis, collect the measurement data of energy consumption and carbon emissions in real time, use semi-supervised learning to extract the measurement operation data, calculate the Angle between the newly generated optimization solution and the reference direction vector, and use it as the attribute space of particle update, and assign all optimization target values, and use the hybrid particle swarm optimization algorithm. The on-line measurement process based on hybrid particle swarm optimization algorithm is completed. The experimental results show that the proposed method has advantages in all aspects of performance index, AUC value is higher than 0.9, detection time is lower than 8s.
{"title":"Energy consumption and carbon emission measurement online detection method based on hybrid particle swarm optimization algorithm","authors":"Tianyi Zhang, Huaiying Shang, Angang Zheng","doi":"10.1117/12.3014377","DOIUrl":"https://doi.org/10.1117/12.3014377","url":null,"abstract":"The detection data of energy consumption and carbon emissions may be affected by equipment failure, sensor error, incomplete data collection and other factors, resulting in low detection accuracy. Based on this, an online detection method of energy consumption and carbon emissions measurement based on hybrid particle swarm optimization algorithm is proposed. Analyze the measurement principles of energy consumption and carbon emissions. On this basis, collect the measurement data of energy consumption and carbon emissions in real time, use semi-supervised learning to extract the measurement operation data, calculate the Angle between the newly generated optimization solution and the reference direction vector, and use it as the attribute space of particle update, and assign all optimization target values, and use the hybrid particle swarm optimization algorithm. The on-line measurement process based on hybrid particle swarm optimization algorithm is completed. The experimental results show that the proposed method has advantages in all aspects of performance index, AUC value is higher than 0.9, detection time is lower than 8s.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to understand the obstacle distance measurement method of UAV, a research on obstacle distance measurement method of UAV based on image processing technology is proposed. This paper proposes an obstacle detection algorithm based on edge extraction. The outline of the obstacle can be obtained by extracting the edge features in the image, and the size, shape and position of the obstacle can be obtained by using a rectangle to frame the outline. Then the distance of the obstacle can be extracted from the contour of the obstacle by using the depth map generated by the binocular camera. The millimeter wave radar in front is used to fuse ranging with the central area of binocular camera image to improve the update frequency of obstacle distance. Finally, the effectiveness of the obstacle avoidance control strategy is verified by the obstacle avoidance flight test, and the UAV can choose the optimal obstacle avoidance direction and successfully bypass when encountering obstacles. The comparison of obstacle avoidance results shows that the obstacle avoidance method in this paper is advanced and has certain engineering application value.
{"title":"Research on obstacle distance measurement method of UAV based on image processing technology","authors":"mingxia Lin","doi":"10.1117/12.3014383","DOIUrl":"https://doi.org/10.1117/12.3014383","url":null,"abstract":"In order to understand the obstacle distance measurement method of UAV, a research on obstacle distance measurement method of UAV based on image processing technology is proposed. This paper proposes an obstacle detection algorithm based on edge extraction. The outline of the obstacle can be obtained by extracting the edge features in the image, and the size, shape and position of the obstacle can be obtained by using a rectangle to frame the outline. Then the distance of the obstacle can be extracted from the contour of the obstacle by using the depth map generated by the binocular camera. The millimeter wave radar in front is used to fuse ranging with the central area of binocular camera image to improve the update frequency of obstacle distance. Finally, the effectiveness of the obstacle avoidance control strategy is verified by the obstacle avoidance flight test, and the UAV can choose the optimal obstacle avoidance direction and successfully bypass when encountering obstacles. The comparison of obstacle avoidance results shows that the obstacle avoidance method in this paper is advanced and has certain engineering application value.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140512105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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