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Deep and Wide Multiscale Recursive Networks for Robust Image Labeling [article]

Gary B. Huang, Viren Jain
2013 arXiv   pre-print
Experiments in the challenging domain of connectomic reconstruction of neural circuity from 3d electron microscopy data show that these "Deep And Wide Multiscale Recursive" (DAWMR) networks lead to new  ...  For the specific image labeling problem of boundary prediction, we also introduce a novel example weighting algorithm that improves segmentation accuracy.  ...  Acknowledgements: We thank Zhiyuan Lu for sample preparation, Shan Xu and Harald Hess for FIB-SEM imaging, and Corey Fisher and Chris Ordish for data annotation.  ... 
arXiv:1310.0354v3 fatcat:z3hwqmj6kzaqlogiawxddolak4

Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network [article]

Rui Xu and Tiantian Liu and Xinchen Ye and Yen-Wei Chen
2020 arXiv   pre-print
Besides, a recursive refinement iteratively applies the same network over the previous segmentation results for progressively boosting the performance while increasing no extra network parameters.  ...  diagnosis system on retinal images.  ...  For the STARE and CHASE DB1 datasets, we randomly select 10 images for testing, and use the rest for training.  ... 
arXiv:2004.12776v1 fatcat:zu7bjtqhm5b3bbo47t7p24zww4

Attention-Guided Label Refinement Network for Semantic Segmentation of Very High Resolution Aerial Orthoimages

Jianfeng Huang, Xinchang Zhang, Ying Sun, Qinchuan Xin
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In this article, we propose an attention-guided label refinement network (ALRNet) for improved semantic labeling of VHR images.  ...  Results demonstrated that ALRNet had shown promising segmentation performance in comparison with state-of-the-art deep learning networks.  ...  ACKNOWLEDGMENT The authors would like to thank the ISPRS for making the Vaihingen and Potsdam datasets available and organizing the semantic labeling contest.  ... 
doi:10.1109/jstars.2021.3073935 fatcat:lrzyxfg2ujba7hinlpogrsv6rq

Deep Local Binary Patterns [article]

Kelwin Fernandes, Jaime S. Cardoso
2017 arXiv   pre-print
We validate the relevance of the proposed idea in several datasets from a wide range of applications. Deep LBP improved the performance of traditional and multiscale LBP in all cases.  ...  Being robust to several properties such as invariance to illumination translation and scaling, LBPs achieved state-of-the-art results in several applications.  ...  Portugal Regional Op-erational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF), and also by Fundação para a Ciência e a  ... 
arXiv:1711.06597v1 fatcat:4gy3uwfgj5dq7eu3dwy2gilnk4

LA-DeepLab V3+: A Novel Counting Network for Pigs

Chengqi Liu, Jie Su, Longhe Wang, Shuhan Lu, Lin Li
2022 Agriculture  
Third, a recursive cascade method was used to optimize the fusion of high- and low-frequency features for mining potential semantic information.  ...  First, an image segmentation model of a small sample of pigs was established based on the DeepLab V3+ deep learning method to reduce the training cost and obtain initial features.  ...  Acknowledgments: We thank all of the funders and all of the reviewers. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/agriculture12020284 fatcat:xfdb4tq3tfclzbxsgvdjmcpplq

Stack Attention-Pruning Aggregates Multiscale Graph Convolution Networks for Hyperspectral Remote Sensing Image Classification

Na Liu, Bin Zhang, Qiuhuan Ma, Qingqing Zhu, Xiaoling Liu
2021 IEEE Access  
INDEX TERMS Hyperspectral remote sensing image classification, stack attention-pruning, multiscale graph convolution networks, longdistances joint interaction, multiscale spatial-temporal information,  ...  images and capture the rich structural semantics.  ...  For example, Li et al. [30] used deep confidence network to capture high-level abstract features of images and image classification. Shi et al.  ... 
doi:10.1109/access.2021.3061489 fatcat:jqsobopyxnhb7ptvcldvepwk5e

TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching [article]

Mostafa El-Khamy, Haoyu Ren, Xianzhi Du, Jungwon Lee
2019 arXiv   pre-print
We further propose an end-to-end deep multitask tele-wide stereo matching neural network (MT-TW-SMNet), which simultaneously learns the SMDE task for the overlapped Tele FOV and the single image inverse  ...  Moreover, we design multiple methods for the fusion of the SMDE and SIDE networks.  ...  We deploy a tele-wide stereo-matching network (TW-SMNet) to work on the left wide-image and the right tele-image to generate a disparity map for the wide FOV.  ... 
arXiv:1906.04463v1 fatcat:sdqb3pjqc5ag3cxb2f46erhaaq

MFAUNet: Multiscale feature attentive U‐Net for cardiac MRI structural segmentation

Dapeng Li, Yanjun Peng, Yanfei Guo, Jindong Sun
2021 IET Image Processing  
The accurate and robust automatic segmentation of cardiac structures in magnetic resonance imaging (MRI) is significant in calculating cardiac clinical functional indices, and diagnosing heart diseases  ...  The experimental results indicate that the method achieved competitive segmentation performance with the three datasets, which verifies the robustness and generalisability of the proposed network.  ...  Although recently many deep learning models are proposed for image segmentation and classification [25, 26] , UNet architectures are still widely used in the field of medical image analysis.  ... 
doi:10.1049/ipr2.12406 fatcat:yvny5qozr5cz5fcedcydtcahmi

3D Virtual Reality Implementation of Tourist Attractions Based on the Deep Belief Neural Network

Fuli Song, Syed Hassan Ahmed
2021 Computational Intelligence and Neuroscience  
The results show that the algorithm has excellent performance in target recognition and target reconstruction, and deep belief networks improve the accuracy by 0.57% and 0.81% and the accuracy by 0.21%  ...  and 2.06%, respectively, compared with the current optimal algorithm in target recognition of 12 and 20 view regular projection images.  ...  Acknowledgments e study was supported by "Xijing College Research Project: Research on the Development and Innovation Ideas of Shaanxi Industrial Heritage-Type Cultural and Creative Products from the Perspective  ... 
doi:10.1155/2021/9004797 pmid:34552628 pmcid:PMC8452428 fatcat:6wcptctz25aolarxvgddklnhnu

Multitask Deep Neural Networks for Tele-Wide Stereo Matching

Mostafa El-Khamy, Haoyu Ren, Xianzhi Du, Jungwon Lee
2020 IEEE Access  
We deploy a tele-wide stereo-matching network (TW-SMNet) to work on the left wide-image and the right tele-image to generate a disparity map for the wide FOV.  ...  These end- to-end networks exploit multiscale features and hierarchical relationships between the earlier and later neural network layers for disparity estimation.  ...  He has co-authored more than 130 papers and holds over 300 patents in the US and many more worldwide. Dr. Lee is an IEEE Fellow.  ... 
doi:10.1109/access.2020.3029085 fatcat:jlqgl6cbbveblnx5jdjztehqpa

A hierarchical Markovian model for multiscale region-based classification of vector-valued images

A. Katartzis, I. Vanhamel, H. Sahli
2005 IEEE Transactions on Geoscience and Remote Sensing  
We propose a new classification method for vector-valued images, based on (i) a causal Markovian model, defined on the hierarchy of a multiscale region adjacency tree (MRAT), and (ii) a set of non-parametric  ...  The image classification is treated as a hierarchical labeling of the MRAT, using a finite set of interpretation labels (e.g. land cover classes).  ...  (FWO, Belgium) "Multiscale Stochastic Models for Image Denoising and Segmentation" project FWOAL264.  ... 
doi:10.1109/tgrs.2004.842405 fatcat:yj2c3d6r2zc2ximvlurd5cycp4

Working memory inspired hierarchical video decomposition with transformative representations [article]

Binjie Qin, Haohao Mao, Ruipeng Zhang, Yueqi Zhu, Song Ding, Xu Chen
2022 arXiv   pre-print
decomposed nonlocal patches into orthogonal subspaces for heterogeneous vessel retrieval and interference suppression.  ...  Specifically, robust PCA unrolling networks acting as a structure-regularized sensor layer decompose XCA into sparse/low-rank structured representations to separate moving contrast-filled vessels from  ...  ACKNOWLEDGMENTS The authors would like to thank all the cited authors for providing the source codes used in this work and the anonymous reviewers for their valuable comments on the manuscript.  ... 
arXiv:2204.10105v3 fatcat:ifzpeay2qjfvbaznwruwc4dz5m

Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images

Lianfa Li
2019 Remote Sensing  
The studies of deep learning for semantic segmentation of remotely sensed images are limited.  ...  This paper presents a novel flexible autoencoder-based architecture of deep learning that makes extensive use of residual learning and multiscaling for robust semantic segmentation of remotely sensed land-use  ...  Acknowledgments: The support of Nvidia Corporation through the donation of the Titan Xp GPUs used in this research and Jiajie Wu's support for this study are gratefully acknowledged.  ... 
doi:10.3390/rs11182142 fatcat:fcwid63pqfampp5ryrvlsfdbde

Image Super-Resolution Using Lightweight Multiscale Residual Dense Network

Shilin Li, Ming Zhao, Zhengyun Fang, Yafei Zhang, Hongjie Li, Wonho Jhe
2020 International Journal of Optics  
In order to solve the problem, we propose a multiscale residual dense network (MRDN) for image super-resolution. This network is constructed based on the residual dense network.  ...  It can integrate the multiscale information of the image and avoid losing too much information in the deep level of the network, while extracting more information under different receptive fields.  ...  Kim et al. a deeply-recursive convolutional network (DRCN) [14] and a very deep convolutional network (VDSR) [25] for image super-resolution.  ... 
doi:10.1155/2020/2852865 fatcat:gyu5u2kfg5hdhf2ff3ewvcupny

A Cascaded R-CNN with Multiscale Attention and Imbalanced Samples for Traffic Sign Detection

Jianming Zhang, Zhipeng Xie, Juan Sun, Xin Zou, Jin Wang
2020 IEEE Access  
INDEX TERMS Traffic sign detection, convolutional neural network, attention, object detection, Multiscale.  ...  Then, we propose a multiscale attention method to obtain the weighted multiscale features by dot-product and softmax, which is summed to fine the features to highlight the traffic sign features and improve  ...  [46] enhance the robustness of corruption and disturbance of image data in view of the fact that deep learning network is disturbed by many forms of image corruption, such as snow, VOLUME 8, 2020 blur  ... 
doi:10.1109/access.2020.2972338 fatcat:oiyel6vctfa3jblwt3dvu6lnva
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