Proceedings of the 2022 5th International Conference on Image and Graphics Processing最新文献_第6页

Precision Inspection and Evaluation System for Paper Packaging of Cigarettes 卷烟纸包装精度检测与评价系统

Proceedings of the 2022 5th International Conference on Image and Graphics Processing Pub Date : 2022-01-07 DOI: 10.1145/3512388.3512390

Jian Gong, Haipeng Li, Dan Xu, Kangjian He, Hao Zhang, Chao Zhang, Z. Luo

{"title":"Precision Inspection and Evaluation System for Paper Packaging of Cigarettes","authors":"Jian Gong, Haipeng Li, Dan Xu, Kangjian He, Hao Zhang, Chao Zhang, Z. Luo","doi":"10.1145/3512388.3512390","DOIUrl":"https://doi.org/10.1145/3512388.3512390","url":null,"abstract":"In order to improve the adaptability of packaging paper and the high-speed packaging machine, we designed a set of non-contact precision inspection and evaluation system for the packaging paper products. A macro laser scanning sensor is installed on bi-axial linear motor for sampling the full dimension of paper on the negative pressure adsorption platform, the uniform sampling density is designed higher than 1100 spots/ mm2, and theoretical error of depth data is less than 0.02mm, this method solved two defects in contacting measurement: the data can't cover object's surface completely, and it is hard to avoid deformation of the measured object. Based on full covered data, this paper proposed the algorithms of Image visualization, indentation detection, contour fitting, 3D reconstruction, and a norm of parameters based on local frequency changes was designed by consulting with industrial demand. The experiment shows that this method can significantly reflect the micro differences. The system could export measurement report and provides an interactive measurement in 3D visualization window, so this work is helpful at development of process control in printing industry, such as defectiveness detection, adaptability verification.","PeriodicalId":434878,"journal":{"name":"Proceedings of the 2022 5th International Conference on Image and Graphics Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123414474","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}

引用次数: 0

Using Double Bit Range Estimation for CABAC in VVC 用双比特距离估计实现VVC中的CABAC

Proceedings of the 2022 5th International Conference on Image and Graphics Processing Pub Date : 2022-01-07 DOI: 10.1145/3512388.3512400

Ka‐Hou Chan, S. Im

引用次数: 2

Channel-Spatial Attention Network for Lunar Image Super-Resolution 面向月球图像超分辨率的通道-空间注意网络

Proceedings of the 2022 5th International Conference on Image and Graphics Processing Pub Date : 2022-01-07 DOI: 10.1145/3512388.3512436

Yabo Duan, Huaizhan Li, Kefei Zhang, Shubi Zhang, Suqin Wu

{"title":"Channel-Spatial Attention Network for Lunar Image Super-Resolution","authors":"Yabo Duan, Huaizhan Li, Kefei Zhang, Shubi Zhang, Suqin Wu","doi":"10.1145/3512388.3512436","DOIUrl":"https://doi.org/10.1145/3512388.3512436","url":null,"abstract":"High-resolution images of the lunar surface are generally used to study the lunar soil and terrain. Nonetheless, acquiring higher-resolution images involves greater memory and calculation power, which is a challenge for lunar landers or rovers. In this study, a deep convolution neural network single image super-resolution reconstruction method based on channel-space attention is proposed to achieve the mapping of low-resolution lunar surface images to high-resolution lunar surface images. An enhanced block named feature fusion block is used for feature extraction. Furthermore, a channel-spatial attention module including efficient channel attention module and enhanced spatial attention module can extract more discriminative channel features and critical spatial features. Last, the model utilizes local implicit image function to predict RGB values of the images. The images of lander terrain camera and rover panoramic camera carried by Chang'e 3 and Chang'e 4 are used to training and validating models, and the Apollo rover images are used to test. The experiment demonstrates the superiority of the proposed novel model over the comparative method by using images of the Apollo project as the test dataset. Compared with the traditional methods, the PSNR value of the proposed method is improved by about 0.26dB in the 4x super-resolution reconstruction experiment.","PeriodicalId":434878,"journal":{"name":"Proceedings of the 2022 5th International Conference on Image and Graphics Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125388648","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}

引用次数: 0

Multi-scale Semantic Representation and Supervision for Remote Sensing Change Detection 遥感变化检测的多尺度语义表示与监督

Proceedings of the 2022 5th International Conference on Image and Graphics Processing Pub Date : 2022-01-07 DOI: 10.1145/3512388.3512394

Haoming He, Jiadong Yang, Qiang Chen

{"title":"Multi-scale Semantic Representation and Supervision for Remote Sensing Change Detection","authors":"Haoming He, Jiadong Yang, Qiang Chen","doi":"10.1145/3512388.3512394","DOIUrl":"https://doi.org/10.1145/3512388.3512394","url":null,"abstract":"Deep Convolutional Neural Networks have been adopted for remote sensing change detection that focused on how to migrate semantic segmentation networks designed for a single image to remote sensing change detection tasks. These networks tend to have an accuracy of the large region rather than boundary quality and small region quality. In this paper, we propose a novel architecture, Siamese Change Detection Network (SCD-Net), and a new hybrid loss, Multi-Scale Perceptual (MSP) Loss, for bi-temporal remote sensing change detection. Specifically, the architecture is composed of a densely supervised Encoder-Decoder network in which, unlike existing work, we add an up-sampling path to the encoder in charge of building multi-level strong semantic feature maps. In this way, the comparison of low-level feature maps is based on global information prior instead of only local information. The Multi-Scale Perceptual (MSP) Loss consists of Tversky loss and a variant of Focal loss. It is applied to the output results of the network at different scales to be able to learn the changed regions at different scales effectively. Equipped with MSP loss, the proposed SCD-Net can effectively segment the change regions and accurately predict the fine structures with accurate boundaries. Experimental results on two public datasets show that our method outperforms the state-of-the-art methods in F1 score.","PeriodicalId":434878,"journal":{"name":"Proceedings of the 2022 5th International Conference on Image and Graphics Processing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124142317","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}

引用次数: 0

An Analysis on Face Recognition using Principal Component Analysis Approach 基于主成分分析法的人脸识别分析

Proceedings of the 2022 5th International Conference on Image and Graphics Processing Pub Date : 2022-01-07 DOI: 10.1145/3512388.3512411

Wenyu Zhang, Burra Venkata Durga Kumar, K. Ooi, Jia Yew Teh, Iftekhar Salam, Mohammad Arif Sobhan Bhuiyan

{"title":"An Analysis on Face Recognition using Principal Component Analysis Approach","authors":"Wenyu Zhang, Burra Venkata Durga Kumar, K. Ooi, Jia Yew Teh, Iftekhar Salam, Mohammad Arif Sobhan Bhuiyan","doi":"10.1145/3512388.3512411","DOIUrl":"https://doi.org/10.1145/3512388.3512411","url":null,"abstract":"Face recognition is a type of biometric recognition based on human facial feature information. Human face images or videos can be automatically collected using a high definition camera. Advanced technologies then can be used for face recognition by tracking on collected images to detect human faces. The facial recognition algorithm can cut out the main facial area after detecting the face and find the key facial feature points, and input it into the recognition algorithm after processing. To extract and compare the facial features, the recognition algorithm is used to the complete the final classification. This research is to study the face recognition using PCA (Principal Component Analysis. The PCA-based is used to eigenface recognition, Hotelling transform in PCA is used to obtain the main components of the face distribution, i.e. the feature vectors (eigenfaces). The face images from the training library and the face images to be recognized are projected onto this space separately to match the recognized image output based on the principle of minimum geometric distance. The face angle, face mask, and face expression factors are selected for testing against face recognition. Hence questions and hypotheses are formulated to verify whether the recognition rate of face recognition is influenced by these factors for this recognition method. Based on the results of the analysis it was that face angle, face masking have a positive effect on face recognition. Furthermore, according to the analysis, it can be concluded that face masking has the highest significance for face recognition.","PeriodicalId":434878,"journal":{"name":"Proceedings of the 2022 5th International Conference on Image and Graphics Processing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124958236","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}

引用次数: 0

Spectral Context-aware Transformer for Cholangiocarcinoma Hyperspectral Image Segmentation 用于胆管癌高光谱图像分割的光谱环境感知转换器

Proceedings of the 2022 5th International Conference on Image and Graphics Processing Pub Date : 2022-01-07 DOI: 10.1145/3512388.3512419

Nanying Li, Jiaqi Xue, S. Jia

引用次数: 1

Proceedings of the 2022 5th International Conference on Image and Graphics Processing 2022年第五届图像和图形处理国际会议论文集

Proceedings of the 2022 5th International Conference on Image and Graphics Processing Pub Date : 1900-01-01 DOI: 10.1145/3512388

引用次数: 0