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3D visual saliency and convolutional neural network for blind mesh quality assessment

Ilyass Abouelaziz, Aladine Chetouani, Mohammed El Hassouni, Longin Jan Latecki, Hocine Cherifi
2019 Neural computing & applications (Print)  
Keywords Mesh visual quality assessment Á Mean opinion score Á Mesh visual saliency Á Convolutional neural network & Ilyass Abouelaziz  ...  In this work, we propose a noreference convolutional neural network (CNN) framework to estimate the perceived visual quality of 3D meshes.  ...  Building on these works, we propose a novel NR-MVQ assessment method called SCNN-BMQA (3D visual saliency and CNN for blind mesh quality assessment).  ... 
doi:10.1007/s00521-019-04521-1 fatcat:zxk3nmtlbza5pbfyhohz7vt5zu

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
., +, TMM 2021 3022-3034 Blind Image Quality Assessment Based on Multi-scale KLT.  ...  ., +, TMM 2021 2820-2832 Distortion Blind Image Quality Assessment Based on Multi-scale KLT. Yang, C., +, Self-Adaptive Neural Module Transformer for Visual Question Answering.  ...  ., Low-Rank Pairwise Align- ment Bilinear Network For Few-Shot Fine-Grained Image Classification; TMM 2021 1666-1680 Huang, H., see 1855 -1867 Huang, H., see Jiang, X., TMM 2021 2602-2613 Huang, J.,  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

Front Matter: Volume 10341

2017 Ninth International Conference on Machine Vision (ICMV 2016)  
The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee.  ...  gesture recognition based on feature points extraction [10341-39] 10341 0I Methodology for mammal classification in camera trap images [10341-82] 10341 0J Convolutional neural networks with balanced  ...  transform [10341-80] 10341 0Q Fast integer approximations in convolutional neural networks using layer-by-layer training [10341-78] SESSION 3 VIDEO PROCESSING AND VISUALIZATION 10341 0R Real-time  ... 
doi:10.1117/12.2276832 dblp:conf/icmv/X16 fatcat:srr4hyfwpfcipadcvjc5jdll6i

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
Artusi, A., +, TIP 2020 1843-1855 End-to-End Blind Image Quality Prediction With Cascaded Deep Neural Network.  ...  Rizkallah, M., +, TIP 2020 3282-3295 QNet: An Adaptive Quantization Table Generator Based on Convolutional Neural Network. Yan, X., +, TIP 2020 9654-9664 Quality Prediction on Deep Generative Images.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Learning graph convolutional network for blind mesh visual quality assessment

Ilyass Abouelaziz, Aladine Chetouani, Mohammed El Hassouni, Hocine Cherifi, Longin Jan Latecki
2021 IEEE Access  
This representation 186 allows us to take advantage of graph properties to manipulate 187 the mesh itself and conceive a model-based method to assess 188 the visual quality blindly.  ...  CONCLUSION 605 In this work, we rely on a shallow graph convolutional 606 network to accurately estimate distorted meshes perceived 607 visual quality.  ...  This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/  ... 
doi:10.1109/access.2021.3094663 fatcat:mb5gsbpojbclhcbamepncq36cy

Editorial

M. N. Hoda
2020 International Journal of Information Technology  
The manuscript ''Deriving Tumour Detection Models using Convolutional Neural Networks from MRI of Human Brain Scans'', Kalaiselvi T. presents six convolutional neural network based models to find the best  ...  The manuscript, ''Highly Sensitive Lab-On-Chip with Deep Learning AI for Detection of Bacteria in Water'', Afzal Nehal Sheikh et al. introduces a novel convolutional neural network based mechanism to improve  ... 
doi:10.1007/s41870-020-00475-z pmid:32838123 pmcid:PMC7237872 fatcat:b2waemvy2jhbdouk6jhemudsky

2020 Index IEEE Transactions on Multimedia Vol. 22

2020 IEEE transactions on multimedia  
., TMM Dec. 2020 3115-3127 Jevremovic, A., see Kostic, Z., TMM July 2020 1904-1916 Ji, Q., see Wang, S., TMM April 2020 1084-1097 Jia, K., see 1345-1357 Jia, Y., see 2138-2148 Jian, M., Dong, J.,  ...  Liu, C., +, TMM July 2020 1785-1795 Blind Night-Time Image Quality Assessment: Subjective and Objective Approaches.  ...  ., +, TMM April 2020 1042-1054 Curve fitting A Single-Image Super-Resolution Method Based on Progressive-Iterative Approximation.  ... 
doi:10.1109/tmm.2020.3047236 fatcat:llha6qbaandfvkhrzpe5gek6mq

Entropy-Based Algorithms for Signal Processing

Gwanggil Jeon, Abdellah Chehri
2020 Entropy  
Entropy, the key factor of information theory, is one of the most important research areas in computer science [...]  ...  [6] , "Blind Mesh Assessment Based on Graph Spectral Entropy and Spatial Features," authors propose a new blind mesh quality assessment method based on graph spectral entropy and spatial features, called  ...  Therefore, how to evaluate the visual quality of the 3D mesh is becoming a crucial problem, and it is necessary to design practical tools for blind 3D mesh quality assessment.  ... 
doi:10.3390/e22060621 pmid:33286393 fatcat:mj3ol3tqivbk5o7r7nogdv3f7m

Front Matter: Volume 10806

Xudong Jiang, Jenq-Neng Hwang
2018 Tenth International Conference on Digital Image Processing (ICDIP 2018)  
using a Base 36 numbering system employing both numerals and letters.  ...  A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  and tracking 10806 15 A lane detection system based on TDA2EG 10806 16 A maritime targets detection method based on hierarchical and multi-scale deep convolutional neural network 10806 17 System design  ... 
doi:10.1117/12.2510343 fatcat:maohjht2t5apneao4iivotwxey

Blind Mesh Assessment Based on Graph Spectral Entropy and Spatial Features

Lin, Yu, Chen, Jiang, Chen, Peng
2020 Entropy  
In this paper, we propose a new Blind Mesh Quality Assessment method based on Graph Spectral Entropy and Spatial features, called as BMQA-GSES. 3D mesh can be represented as graph signal, in the graph  ...  Therefore, how to evaluate the visual quality of 3D mesh is becoming an important problem and it is necessary to design effective tools for blind 3D mesh quality assessment.  ...  [17] proposed a blind MQA (BMQA) method using a convolutional neural network (CNN) based on the extraction of visual representative features.  ... 
doi:10.3390/e22020190 pmid:33285965 pmcid:PMC7516613 fatcat:w7jfkjxru5hnxcy7lg4p236ike

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
., +, TIP 2021 6459-6472 Cubemap-Based Perception-Driven Blind Quality Assessment for tion.  ...  ., +, TIP 2021 68-79 No-Reference Quality Assessment for Screen Content Images Using Visual Edge Model and AdaBoosting Neural Network.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Front Matter: Volume 10033

2016 Eighth International Conference on Digital Image Processing (ICDIP 2016)  
using a Base 36 numbering system employing both numerals and letters.  ...  A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  10033 2D Very high resolution images classification by fine tuning deep convolutional neural networks [10033-91] 10033 2E Fast image clustering based on convolutional neural network and binary K-means  ... 
doi:10.1117/12.2257252 fatcat:v2ipfp2mp5gedjypzpecahpo7e

Guest Editorial: Multimedia for Predictive Analytics

Sanjay Kumar Singh, Amit Kumar Singh, Basant Kumar, Subir Kumar Sarkar, Karm Veer Arya
2017 Multimedia tools and applications  
Joshi and Prakash present a blind image quality assessment technique with no-training.  ...  Singh and Om proposes a method for new born face recognition using Deep Convolutional Neural Network (CNN). The method is tested on IIT(BHU) newborn database and found a very interesting results.  ... 
doi:10.1007/s11042-017-5107-x fatcat:mgp6pimnbzcypneluv43dujqcm

A Novel Method for the Deblurring of Photogrammetric Images Using Conditional Generative Adversarial Networks

Pawel Burdziakowski
2020 Remote Sensing  
A data set for neural network training was developed based on real aerial images collected over the last few years.  ...  In this research, a new, rapid method based on generative adversarial networks (GANs) was applied for deblurring.  ...  Both the geometric, as well as the interpretive quality of developed photogrammetric models were improved using the neural method for reducing blur, based on generative adversarial networks.  ... 
doi:10.3390/rs12162586 fatcat:bjdcuh4udrcztayrbtrrhqqty4

Two-level training of a 3D U-Net for accurate segmentation of the intra-cochlear anatomy in head CT with limited ground truth training data

Rueben Banalagay, Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant, Dongqing Zhang, Bennett A. Landman, Elsa D. Angelini
2019 Medical Imaging 2019: Image Processing  
To tackle this problem, we use segmentations generated by the ASM-based method to pre-train the model and fine-tune it on a small image set for which accurate manual delineation is available.  ...  Using this method, we achieve better results than the ASM-based method.  ...  Deep learning-based methods, especially convolutional neural networks, have achieved impressive performances in a variety of image processing tasks.  ... 
doi:10.1117/12.2512529 pmid:31571720 pmcid:PMC6766587 fatcat:h4cocaujyzak5foxsik4ex64vy
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