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Understanding Convolution for Semantic Segmentation [article]

Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, Garrison Cottrell
2018 arXiv   pre-print
Here we show how to improve pixel-wise semantic segmentation by manipulating convolution-related operations that are of both theoretical and practical value.  ...  Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems.  ...  Acknowledgments We thank the members of TuSimple and Gary's Unbelievable Research Unit (GURU) for comments on this work.  ... 
arXiv:1702.08502v3 fatcat:cgmtdmjhozechgh3qipjr73egy

Street Scene understanding via Semantic Segmentation Using Deep Learning

Amani Noori, Shaimaa Shaker, Raghad A. Azeez
2022 Engineering and Technology Journal  
This paper provides a model for semantic segmentation of outdoor sense to classify each object in the scene.  ...  Image semantic segmentation is an important task for Autonomous driving and Mobile robotics applications because it introduces enormous information needed for safe navigation and complex reasoning.  ...  Semantic image segmentation is an important topic for understanding visual scenes. It is a classification for the multilabeling problem.  ... 
doi:10.30684/etj.v40i4.2120 fatcat:kacjh5b4pbbddm2yu6dhm7io3a

Semantic Segmentation for Aerial Images: A Literature Review

Yongki Christian Sanjaya, Alexander Agung Santoso Gunawan, Edy Irwansyah
2020 Engineering, Mathematics and Computer Science Journal (EMACS)  
Semantic image segmentation is the process of understanding the role of each pixel in an image. Over time, the model for completing Semantic Image Segmentation has developed very rapidly.  ...  Segmentation, and can also be used as a reference for developing new Semantic Image Segmentation models in the future  ...  Understanding Convolution for Semantic Segmentation This model proposes the use of the DUC-HDC [11] method to solve the semantic image segmentation problem.  ... 
doi:10.21512/emacsjournal.v2i3.6737 fatcat:5zncush42bbj7lrckjpfn34nsu

Pointwise Convolutional Neural Networks [article]

Binh-Son Hua, Minh-Khoi Tran, Sai-Kit Yeung
2018 arXiv   pre-print
In this paper, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds.  ...  Our fully convolutional network design, while being surprisingly simple to implement, can yield competitive accuracy in both semantic segmentation and object recognition task.  ...  We thank Quang-Hieu Pham for helping with the 2D-to-3D semantic segmentation experiment and proofreading the paper, Quang-Trung Truong and Benjamin Kang Yue Sheng for their kind support for the neural  ... 
arXiv:1712.05245v2 fatcat:4di5ten6mrbjfadjaxla4sopta

A Fast Panoptic Segmentation Network for Self-Driving Scene Understanding

Abdul Majid, Sumaira Kausar, Samabia Tehsin, Amina Jameel
2022 Computer systems science and engineering  
Scene understanding systems typically involves detection and segmentation of different natural and manmade things.  ...  The proposed method presents Panoptic Segmentation on Cityscapes Dataset. Mobilenet-V2 is used as a backbone for feature extraction that is pre-trained on ImageNet.  ...  Funding Statement: The authors received no specific funding for this study. Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.  ... 
doi:10.32604/csse.2022.022590 fatcat:nhwya7l2f5czpfxjelcggcdl6i

Pointwise Convolutional Neural Networks

Binh-Son Hua, Minh-Khoi Tran, Sai-Kit Yeung
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
In this paper, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds.  ...  Our fully convolutional network design, while being surprisingly simple to implement, can yield competitive accuracy in both semantic segmentation and object recognition task.  ...  We thank Quang-Hieu Pham for helping with the 2D-to-3D semantic segmentation experiment and proofreading the paper, Quang-Trung Truong and Benjamin Kang Yue Sheng for their kind support for the neural  ... 
doi:10.1109/cvpr.2018.00109 dblp:conf/cvpr/HuaTY18 fatcat:if2w3kzlunbq3b76yw33xot6n4

Efficient Hybrid DCT-Wiener Algorithm Based Deep Learning Approach For Semantic Shape Segmentation

Kaustubh V. Sakhare, Vibha Vyas
2022 Iraqi Journal of Science  
Semantic segmentation is effective in numerous object classification tasks such as autonomous vehicles and scene understanding.  ...  With the advent in the deep learning domain, lots of efforts are seen in applying deep learning algorithms for semantic segmentation.  ...  Hybrid DCT-Wiener based deep learning approach for semantic shape segmentation DCT-AWWF Encoder Framework: The encoder for semantic segmentation comprises of Wiener based adaptive weight filter, convolution  ... 
doi:10.24996/ijs.2022.63.2.31 fatcat:zkeb47jftbdxxc6d5igh3iwlha

Multi-Scale Convolutional Features Network for Semantic Segmentation in Indoor Scenes

Yanran Wang, Qingliang Chen, Shilang Chen, Junjun Wu
2020 IEEE Access  
Semantic segmentation is one of the most fundamental techniques for visual intelligence, which plays a vital role for indoor service robotic tasks such as scene understanding, autonomous navigation and  ...  INDEX TERMS Semantic segmentation, convolutional neural networks (CNN), hidden convolutional features, dilated convolution, indoor service robots.  ...  For more information, see https://creativecommons.org/licenses/by/4.0/ a growing interest in visual scene understanding of indoor environments, which is a remarkably challenging benchmark for semantic  ... 
doi:10.1109/access.2020.2993570 fatcat:wp5a55izazeexnskv4c3dncfme

Scene Understanding Based on High-Order Potentials and Generative Adversarial Networks

Xiaoli Zhao, Guozhong Wang, Jiaqi Zhang, Xiang Zhang
2018 Advances in Multimedia  
In this study, we propose a semantic segmentation framework based on classic generative adversarial nets (GAN) to train a fully convolutional semantic segmentation model along with an adversarial network  ...  We realize the high-order potentials by substituting adversarial network for CRF model, which can continuously improve the consistency and details of the segmented semantic image until it cannot discriminate  ...  Cityscapes [1] is a dataset for semantic urban scene understanding which was released in 2016.  ... 
doi:10.1155/2018/8207201 fatcat:tjaj4et2orejjfxa3vcml6dtqu

Joint Semantic and Motion Segmentation for dynamic scenes using Deep Convolutional Networks [article]

Nazrul Haque, N Dinesh Reddy, K. Madhava Krishna
2017 arXiv   pre-print
Dynamic scene understanding is a challenging problem and motion segmentation plays a crucial role in solving it.  ...  We deduce semantic and motion labels by integrating optical flow as a constraint with semantic features into dilated convolution network.  ...  Krishna Murthy for proofreading and paper editing. We are also grateful to Parv Parkhiya and Aman Bansal for help with dataset annotation on KITTI Tracking benchmark.  ... 
arXiv:1704.08331v1 fatcat:72zs5w4sajazbazxj5ezuog6li

A novel dilated convolutional neural network model for road scene segmentation

Yachao Zhang, Yuxia Yuan
2018 EAI Endorsed Transactions on Scalable Information Systems  
Among them, semantic segmentation can assign category information to each pixel of image, which is the most commonly used method in automatic driving scene understanding.  ...  However, most commonly used semantic segmentation algorithms cannot achieve a good balance between speed and precision.  ...  At present, semantic segmentation is a common method for road scene understanding.  ... 
doi:10.4108/eai.27-1-2022.173164 fatcat:m77wldqfdvglnn7cemxszimuca

Real-Time Semantic Understanding and Segmentation of Urban Scenes for Vehicle Visual Sensors by Optimized DCNN Algorithm

Yanyi Li, Jian Shi, Yuping Li
2022 Applied Sciences  
Overall, the research not only provides technical support for the development of real-time semantic understanding and segmentation of DCNN algorithms but also contributes to the development of artificial  ...  The experimental results show that: (1) On the Cityscapes dataset, the ESNet structure achieves 70.7% segmentation accuracy for the 19 semantic categories set, and 87.4% for the seven large grouping categories  ...  All authors gave their approval of the version submitted for publication. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12157811 fatcat:pltch2hgjvfsreea5r5mwktjf4

A Residual Encoder-Decoder Network for Semantic Segmentation in Autonomous Driving Scenarios

Y.G Naresh, Suzanne Little, Noel E. Oconnor
2018 2018 26th European Signal Processing Conference (EUSIPCO)  
In this paper, we propose an encoder-decoder based deep convolutional network for semantic segmentation in autonomous driving scenarios. The architecture of the proposed model is based on VGG16 [1].  ...  Residual learning is introduced to preserve the context while decreasing the size of feature maps between the stacks of convolutional layers.  ...  RELATED WORK Semantic segmentation enables partitioning a scene and recognition of various entities of the scene to understand the context between entities.  ... 
doi:10.23919/eusipco.2018.8553161 dblp:conf/eusipco/NareshLO18 fatcat:uuy76zrzcjcjzb2qyrmklb3ziu

Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles

Kewei Wang, Fuwu Yan, Bin Zou, Luqi Tang, Quan Yuan, Chen Lv
2019 Sensors  
Inferring the occluded road area requires a comprehensive understanding of the geometry and the semantics of the visible scene.  ...  neural network called OFRSNet (occlusion-free road segmentation network) that learns to predict occluded portions of the road in the semantic domain by looking around foreground objects and visible road  ...  Acknowledgments: Thanks for the help of reviewers and editors. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s19214711 pmid:31671547 pmcid:PMC6864472 fatcat:gtc4ojxm3fcazne6avq4hmcgvu

Real-Time Semantic Segmentation of 3D Point Cloud for Autonomous Driving

Dongwan Kang, Anthony Wong, Banghyon Lee, Jungha Kim
2021 Electronics  
As examples, cameras are used for a high-level understanding of a scene, radar is applied to weather-resistant distance perception, and LiDAR is used for accurate distance recognition.  ...  The convolutional neural network method used by a conventional camera can be easily applied to the projection method.  ...  Semantic segmentation is a field of computer vision. Scene understanding can be divided mainly into classification, detection, and segmentation.  ... 
doi:10.3390/electronics10161960 fatcat:akv7vp3wzrhbndauqqbxffq2wi
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