Relevant bibliographies by topics / Semantic segmentation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Semantic segmentation'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Semantic segmentation"

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Harika, Dr B., K. Himneesh, and M. Bharath. "Semantic Segmentation For Aerial Images." International Journal of Research Publication and Reviews 6, no. 4 (2025): 1547–63. https://doi.org/10.55248/gengpi.6.0425.1358.

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Ishikawa, Haruya, and Yoshimitsu Aoki. "Boosting Semantic Segmentation by Conditioning the Backbone with Semantic Boundaries." Sensors 23, no. 15 (2023): 6980. http://dx.doi.org/10.3390/s23156980.

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In this paper, we propose the Semantic-Boundary-Conditioned Backbone (SBCB) framework, an effective approach to enhancing semantic segmentation performance, particularly around mask boundaries, while maintaining compatibility with various segmentation architectures. Our objective is to improve existing models by leveraging semantic boundary information as an auxiliary task. The SBCB framework incorporates a complementary semantic boundary detection (SBD) task with a multi-task learning approach. It enhances the segmentation backbone without introducing additional parameters during inference or
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Hu, Lihe, Yi Zhang, Yang Wang, Huan Yang, and Shuyi Tan. "Salient Semantic Segmentation Based on RGB-D Camera for Robot Semantic Mapping." Applied Sciences 13, no. 6 (2023): 3576. http://dx.doi.org/10.3390/app13063576.

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Semantic mapping can help robots better understand the environment and is extensively studied in robotics. However, it is a challenge for semantic mapping that calibrates all the obstacles with semantics. We propose integrating two network models to realize the salient semantic segmentation used for mobile robot mapping, which differs from traditional segmentation methods. Firstly, we detected salient objects. The detection result was the grayscale image form, which was recognized and annotated by our trained model. Then, we projected the salient objects’ contour with semantics to the correspo
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Aksoy, Yağiz, Tae-Hyun Oh, Sylvain Paris, Marc Pollefeys, and Wojciech Matusik. "Semantic soft segmentation." ACM Transactions on Graphics 37, no. 4 (2018): 1–13. http://dx.doi.org/10.1145/3197517.3201275.

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Yeom, Sang-Sik, and Jong-Eun Ha. "3D Indoor Scene Semantic Segmentation using 2D Semantic Segmentation Projection." Journal of Institute of Control, Robotics and Systems 26, no. 11 (2020): 949–54. http://dx.doi.org/10.5302/j.icros.2020.20.0120.

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Kim, Sangtae, Daeyoung Park, and Byonghyo Shim. "Semantic-Aware Superpixel for Weakly Supervised Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 1142–50. http://dx.doi.org/10.1609/aaai.v37i1.25196.

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Weakly-supervised semantic segmentation aims to train a semantic segmentation network using weak labels. Among weak labels, image-level label has been the most popular choice due to its simplicity. However, since image-level labels lack accurate object region information, additional modules such as saliency detector have been exploited in weakly supervised semantic segmentation, which requires pixel-level label for training. In this paper, we explore a self-supervised vision transformer to mitigate the heavy efforts on generation of pixel-level annotations. By exploiting the features obtained
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Yu, Hao, Zhengyang Wang, Qingjie Zhou, et al. "Deep-Learning-Based Semantic Segmentation Approach for Point Clouds of Extra-High-Voltage Transmission Lines." Remote Sensing 15, no. 9 (2023): 2371. http://dx.doi.org/10.3390/rs15092371.

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The accurate semantic segmentation of point cloud data is the basis for their application in the inspection of extra high-voltage transmission lines (EHVTL). As deep learning evolves, point-wise-based deep neural networks have shown great potential for the semantic segmentation of EHVTL point clouds. However, EHVTL point cloud data are characterized by a large data volume and significant class imbalance. Therefore, the down-sampling method and point cloud feature extraction method used in current point-wise-based deep neural networks hardly meet the needs of computational accuracy and efficien
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Gaikwad, Akshay V., and Suyash Awate. "Deep Monte-Carlo EM for Semantic Segmentation using Weakly-and-Semi-Supervised Learning Using Very Few Expert Segmentations." Machine Learning for Biomedical Imaging 2, June 2024 (2024): 717–60. http://dx.doi.org/10.59275/j.melba.2024-2fgd.

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Typical methods for semantic image segmentation rely on large training sets comprising per-pixel semantic segmentations. In medical-imaging applications, obtaining a large number of expert segmentations can be difficult because of the underlying demands on the experts’ time and the budget. However, in many such applications, it is much easier to obtain image-level information indicating the class labels of the objects of interest present in the image. We propose a novel deep-neural-network (DNN) framework for the semantic segmentation of images relying on weakly-and-semi-supervised learning fr
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Xu, S., and Z. Zhang. "JSMNET: IMPROVING INDOOR POINT CLOUD SEMANTIC AND INSTANCE SEGMENTATION THROUGH SELF-ATTENTION AND MULTISCALE FUSION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 195–201. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-195-2023.

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Abstract. The semantic understanding of indoor 3D point cloud data is crucial for a range of subsequent applications, including indoor service robots, navigation systems, and digital twin engineering. Global features are crucial for achieving high-quality semantic and instance segmentation of indoor point clouds, as they provide essential long-range context information. To this end, we propose JSMNet, which combines a multi-layer network with a global feature self-attention module to jointly segment three-dimensional point cloud semantics and instances. To better express the characteristics of
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Han, Shuangquan, and Zhihong Xi. "Dynamic Scene Semantics SLAM Based on Semantic Segmentation." IEEE Access 8 (2020): 43563–70. http://dx.doi.org/10.1109/access.2020.2977684.

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Dissertations / Theses on the topic "Semantic segmentation"

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Zou, Wenbin. "Semantic-oriented Object Segmentation." Thesis, Rennes, INSA, 2014. http://www.theses.fr/2014ISAR0007/document.

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Cette thèse porte sur les problèmes de segmentation d’objets et la segmentation sémantique qui visent soit à séparer des objets du fond, soit à l’attribution d’une étiquette sémantique spécifique à chaque pixel de l’image. Nous proposons deux approches pour la segmentation d’objets, et une approche pour la segmentation sémantique. La première approche est basée sur la détection de saillance. Motivés par notre but de segmentation d’objets, un nouveau modèle de détection de saillance est proposé. Cette approche se formule dans le modèle de récupération de la matrice de faible rang en exploitant
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Johnson, M. A. "Semantic segmentation and image search." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.605649.

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Understanding the meaning behind visual data is increasingly important as the quantity of digital images in circulation explodes, and as computing in general and the Internet in specific shifts quickly towards an increasingly visual presentation of data. However, the remarkable amount of variance inside categories (e.g. different kinds of chairs) combined with the occurrence of similarity between categories (e.g. similar breeds of cats and dogs) makes this problem incredibly difficult to solve. In particular, the <i>semantic segmentation</i> of images into contiguous regions of similar interpr
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Kernell, Björn. "Improving Photogrammetry using Semantic Segmentation." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148491.

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3D reconstruction is the process of constructing a three-dimensional model from images. It contains multiple steps where each step can induce errors. When doing 3D reconstruction of outdoor scenes, there are some types of scene content that regularly cause problems and affect the resulting 3D model. Two of these are water, due to its fluctuating nature, and sky because of it containing no useful (3D) data. These areas cause different problems throughout the process and do generally not benefit it in any way. Therefore, masking them early in the reconstruction chain could be a useful step in an
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Malec, Stanislaw. "Semantic Segmentation with Carla Simulator." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105287.

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Autonomous vehicles perform semantic segmentation to orient themselves, but training neural networks for semantic segmentation requires large amounts of labeled data. A hand-labeled real-life dataset requires considerable effort to create, so we instead turn to virtual simulators where the segmented labels are known to generate large datasets virtually for free. This work investigates how effective synthetic datasets are in driving scenarios by collecting a dataset from a simulator and testing it against a real-life hand-labeled dataset. We show that we can get a model up and running faster by
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Gulshan, Varun. "From interactive to semantic image segmentation." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:706b648a-e5e7-4334-a456-0f0b5701dbc4.

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This thesis investigates two well defined problems in image segmentation, viz. interactive and semantic image segmentation. Interactive segmentation involves power assisting a user in cutting out objects from an image, whereas semantic segmentation involves partitioning pixels in an image into object categories. We investigate various models and energy formulations for both these problems in this thesis. In order to improve the performance of interactive systems, low level texture features are introduced as a replacement for the more commonly used RGB features. To quantify the improvement obta
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Gao, Jizhou. "VISUAL SEMANTIC SEGMENTATION AND ITS APPLICATIONS." UKnowledge, 2013. http://uknowledge.uky.edu/cs_etds/14.

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This dissertation addresses the difficulties of semantic segmentation when dealing with an extensive collection of images and 3D point clouds. Due to the ubiquity of digital cameras that help capture the world around us, as well as the advanced scanning techniques that are able to record 3D replicas of real cities, the sheer amount of visual data available presents many opportunities for both academic research and industrial applications. But the mere quantity of data also poses a tremendous challenge. In particular, the problem of distilling useful information from such a large repository of
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Raza, Syed H. "Temporally consistent semantic segmentation in videos." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53455.

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The objective of this Thesis research is to develop algorithms for temporally consistent semantic segmentation in videos. Though many different forms of semantic segmentations exist, this research is focused on the problem of temporally-consistent holistic scene understanding in outdoor videos. Holistic scene understanding requires an understanding of many individual aspects of the scene including 3D layout, objects present, occlusion boundaries, and depth. Such a description of a dynamic scene would be useful for many robotic applications including object reasoning, 3D perception, video analy
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Chen, Yifu. "Deep learning for visual semantic segmentation." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS200.

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Dans cette thèse, nous nous intéressons à la segmentation sémantique visuelle, une des tâches de haut niveau qui ouvre la voie à une compréhension complète des scènes. Plus précisément, elle requiert une compréhension sémantique au niveau du pixel. Avec le succès de l’apprentissage approfondi de ces dernières années, les problèmes de segmentation sémantique sont abordés en utilisant des architectures profondes. Dans la première partie, nous nous concentrons sur la construction d’une fonction de coût plus appropriée pour la segmentation sémantique. En particulier, nous définissons une nouvelle
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Lotz, Max. "Depth Inclusion for Classification and Semantic Segmentation." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233371.

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The  majority  of  computer  vision  algorithms  only  use  RGB  images  to  make  inferencesabout  the  state  of  the  world.  With  the  increasing  availability  of  RGB-D  cameras  it  is  im-portant  to  examine  ways  to  effectively  fuse  this  extra  modality  for  increased  effective-ness.  This  paper  examines  how  depth  can  be  fused  into  CNNs  to  increase  accuracy  in  thetasks  of  classification  and  semantic  segmentation,  as  well  as  examining  how  this  depthshould  best  be  effectively  encoded  prior  to  inclusion  in  the  network.  Concatenating  depthas 
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Jain, Shipra. "Pushing the boundary of Semantic Image Segmentation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290304.

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The state-of-the-art object detection and image classification methods can perform impressively on more than 9k classes. In contrast, the number of classes in semantic segmentation datasets are fairly limited. This is not surprising , when the restrictions caused by the lack of labeled data and high computation demand are considered. To efficiently perform pixel-wise classification for c number of classes, segmentation models use cross-entropy loss on c-channel output for each pixel. The computational demand for such prediction turns out to be a major bottleneck for higher number of classes. T
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Books on the topic "Semantic segmentation"

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Guo, Ju, and C. C. Jay Kuo. Semantic Video Object Segmentation for Content-Based Multimedia Applications. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1503-6.

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Jay, Kuo C. C., ed. Semantic video object segmentation for content-based multimedia applications. Kluwer Academic Publishers, 2001.

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Guo, Ju. Semantic video object segmentation for content-based multimedia applications. Kluwer Academic Publishers, 2001.

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Guo, Ju. Semantic Video Object Segmentation for Content-Based Multimedia Applications. Springer US, 2002.

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Ayed, Ismail Ben. High-Order Models in Semantic Image Segmentation. Elsevier Science & Technology Books, 2021.

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Volpi, Riccardo, and Boris Chidlovskii. Semantic Image Segmentation: Two Decades of Research. Now Publishers, 2022.

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High-Order Models in Semantic Image Segmentation. Elsevier Science & Technology, 2029.

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Li, Jiaojiao, Qian Du, Jocelyn Chanussot, et al., eds. Remote Sensing Image Classification and Semantic Segmentation. MDPI, 2024. http://dx.doi.org/10.3390/books978-3-7258-1366-7.

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Unger, Herwig, Phayung Meesad, and Chalermpol Tapsai. Thai Natural Language Processing: Word Segmentation, Semantic Analysis, and Application. Springer International Publishing AG, 2020.

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Unger, Herwig, Phayung Meesad, and Chalermpol Tapsai. Thai Natural Language Processing: Word Segmentation, Semantic Analysis, and Application. Springer International Publishing AG, 2021.

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Book chapters on the topic "Semantic segmentation"

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Latif, Nouman, Muhammad Saadi, and Demostenes Zegarra Rodriguez. "Semantic Segmentation." In Optical and Wireless Communications. CRC Press, 2025. https://doi.org/10.1201/9781003472506-7.

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Wang, Xiaogang. "Semantic Object Segmentation." In Video Segmentation and Its Applications. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9482-0_3.

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Sagerer, Gerhard, and Heinrich Niemann. "Segmentation." In Semantic Networks for Understanding Scenes. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4899-1913-7_2.

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Shotton, Jamie, and Pushmeet Kohli. "Semantic Image Segmentation." In Computer Vision. Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_251.

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Poncelet, Jocelyn, Pierre-Antoine Jean, François Trousset, and Jacky Montmain. "Semantic Customers’ Segmentation." In Internet Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34770-3_26.

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Yang, Bisheng, Zhen Dong, Fuxun Liang, and Xiaoxin Mi. "Point Cloud Semantic Segmentation." In Ubiquitous Point Cloud. CRC Press, 2024. http://dx.doi.org/10.1201/9781003486060-10.

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Balaska, Vasiliki, Loukas Bampis, and Antonios Gasteratos. "Graph-Based Semantic Segmentation." In Advances in Service and Industrial Robotics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00232-9_60.

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Henderson, Thomas C. "Other Semantic Feature Segmentation." In Analysis of Engineering Drawings and Raster Map Images. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-8167-7_8.

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Athanasiadis, Thanos, Phivos Mylonas, and Yannis Avrithis. "A Context-Based Region Labeling Approach for Semantic Image Segmentation." In Semantic Multimedia. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11930334_17.

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Guo, Ju, and C. C. Jay Kuo. "Automatic Segmentation." In Semantic Video Object Segmentation for Content-Based Multimedia Applications. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1503-6_3.

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Conference papers on the topic "Semantic segmentation"

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Dhar, Anindha, Diganta Sikdar, Yasir Arafat Prodhan, and Shourov Joarder. "Skin Cancer Semantic Segmentation." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013785.

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Ma, Wenbo, Yu Xie, Congyan Wang, Kaipeng Zheng, and Mingkai Chen. "Semantic Segmentation Facilitates Semantic Communication in Surveillance Video." In 2024 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2024. http://dx.doi.org/10.1109/iccc62479.2024.10681878.

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Lin, Ci-Siang, Chien-Yi Wang, Yu-Chiang Frank Wang, and Min-Hung Chen. "Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00849.

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Thangarasu, N., Jagmeet Sohal, B. Umamaheswari, Jagtej Singh, R. Manjunatha, and Ganesh C. Shelke. "Semantic Segmentation for Image Understanding." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725788.

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Cheng, Yansheng, Jinzhong Xu, Lele Xu, Lili Guo, and Ye Li. "Semantic-guided Nematode Instance Segmentation." In 2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA). IEEE, 2024. http://dx.doi.org/10.1109/aiea62095.2024.10692870.

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Nakajima, Akira, and Hiroyuki Kobayashi. "Semantic Segmentation with GLCM Images." In 21st International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013072200003822.

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Aloui, Rahma, Pranav Martini, Pandu Devarakota, Apurva Gala, and Shishir Shah. "Gam-UNet for Semantic Segmentation." In 20th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013182000003912.

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Nash, Will, Tom Drummond, and Nick Birbilis. "Deep Learning AI for Corrosion Detection." In CORROSION 2019. NACE International, 2019. https://doi.org/10.5006/c2019-13267.

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Abstract Visual inspection is a vital component of asset management that stands to benefit from automation. Using artificial intelligence to assist inspections can increase safety, reduce access costs, provide objective classification, and integrate with digital asset management systems. The work presented herein investigates the impact of dataset size on Deep Learning for automatic detection of corrosion on steel assets. Dataset creation is typically one of the first steps when applying Machine Learning methods to a new task; and the real-world performance of models hinges on the quality and
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Wu, Yuanchen, Xiaoqiang Li, Songmin Dai, Jide Li, Tong Liu, and Shaorong Xie. "Hierarchical Semantic Contrast for Weakly Supervised Semantic Segmentation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/171.

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Weakly supervised semantic segmentation (WSSS) with image-level annotations has achieved great processes through class activation map (CAM). Since vanilla CAMs are hardly served as guidance to bridge the gap between full and weak supervision, recent studies explore semantic representations to make CAM fit for WSSS and demonstrate encouraging results. However, they generally exploit single-level semantics, which may hamper the model to learn a comprehensive semantic structure. Motivated by the prior that each image has multiple levels of semantics, we propose hierarchical semantic contrast (HSC
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Hou, Xiaoshuai, Chunmei Xie, Fengyi Li, and Yang Nan. "Cascaded Semantic Segmentation for Kidney and Tumor." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.002.

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Reports on the topic "Semantic segmentation"

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Vu, Tuan-Hung. Zero-shot Semantic Segmentation. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.s35o4gav.1.

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Vu, Tuan-Hung. ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.5bktj0o6.1.

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Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNe
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Kwon, Christine, and Maggie Wigness. Enhanced Annotation for Semantic Segmentation on Unstructured Video Sequences for Robotic Navigation. DEVCOM Army Research Laboratory, 2021. http://dx.doi.org/10.21236/ad1137228.

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Fisher, Andmorgan, Timothy Middleton, Jonathan Cotugno, et al. Use of convolutional neural networks for semantic image segmentation across different computing systems. Engineer Research and Development Center (U.S.), 2020. http://dx.doi.org/10.21079/11681/35881.

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Panta, Manisha, Padam Thapa, Md Hoque, et al. Application of deep learning for segmenting seepages in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49453.

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Seepage is a typical hydraulic factor that can initiate the breaching process in a levee system. If not identified and treated on time, seepages can be a severe problem for levees, weakening the levee structure and eventually leading to collapse. Therefore, it is essential always to be vigilant with regular monitoring procedures to identify seepages throughout these levee systems and perform adequate repairs to limit potential threats from unforeseen levee failures. This paper introduces a fully convolutional neural network to identify and segment seepage from the image in levee systems. To th
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Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose t
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