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Journal articles on the topic 'Semantic segmentation'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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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11

Chen, Ling, Gang Xu, Nana Fu, Zhifeng Hu, Shuzhan Zheng, and Xiang Li. "Study on the 3D point cloud semantic segmentation method of fusion semantic edge detection." Journal of Physics: Conference Series 2216, no. 1 (2022): 012098. http://dx.doi.org/10.1088/1742-6596/2216/1/012098.

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Abstract With the continuous development of deep learning, semantic segmentation, as the basis of 3D scene understanding, has also been widely used in 3D point clouds. Semantic segmentation based on the point cloud has obvious advantages due to its rich data. Aiming at the problems of unclear segmentation target and unclear edge in point cloud semantic segmentation, a 3D point cloud semantic segmentation algorithm integrating edge detection was proposed. Firstly, complete global semantic features are obtained by the mainstream 3D point cloud semantic segmentation framework. Then, semantic edge

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Li, Yutong. "The Application of Semantic Segmentation on 2D images." Highlights in Science, Engineering and Technology 31 (February 10, 2023): 88–96. http://dx.doi.org/10.54097/hset.v31i.4818.

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A fundamental problem in computer vision is semantic segmentation, which calls for the algorithm to categorize each pixel in the picture and provide the precise details of the category. Semantic segmentation is being employed extensively in a variety of applications, including autonomous vehicles and medical imaging. An overview of similar semantic segmentation approaches is given in this study. First, this paper gives a brief overview of the history and vocabulary of semantic segmentation. The key datasets for semantic segmentation, conventional segmentation models, and fundamental deep learn

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Zhao, Jiaojiao, Jungong Han, Ling Shao, and Cees G. M. Snoek. "Pixelated Semantic Colorization." International Journal of Computer Vision 128, no. 4 (2019): 818–34. http://dx.doi.org/10.1007/s11263-019-01271-4.

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AbstractWhile many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from limited semantic understanding. To address this shortcoming, we propose to exploit pixelated object semantics to guide image colorization. The rationale is that human beings perceive and distinguish colors based on the semantic categories of objects. Starting from an autoregressive model, we generate image color distributions, from which diverse colored results are sampled. We propose two ways to incorporate object semanti

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Wang, Xiao Feng, and Jian Hua Li. "Semantic Color Retrieval of Color Image Based on Fuzzy Clustering." Applied Mechanics and Materials 380-384 (August 2013): 3469–73. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3469.

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As next generation of the web, the semantic web aims at a more intelligent web severing machines as well as people, based on radical notions of information sharing and acquisition. For color image segmentation, semantic color is our focus. One method of color partition is fuzzy clustering which has been widely used in image segmentation. However, the fuzzy clustering algorithm is parameter sensitive, and lack of availability because of its initial focus on physical features. To improve the above problems, a novel fuzzy clustering method based on semantic color retrieval for image segmentation

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Xing, Yongfeng, Luo Zhong, and Xian Zhong. "DARSegNet: A Real-Time Semantic Segmentation Method Based on Dual Attention Fusion Module and Encoder-Decoder Network." Mathematical Problems in Engineering 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/6195148.

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The convolutional neural network achieves excellent semantic segmentation results in artificially annotated datasets with complex scenes. However, semantic segmentation methods still suffer from several problems such as low use rate of the features, high computational complexity, and being far from practical real-time application, which bring about challenges for the image semantic segmentation. Two factors are very critical to semantic segmentation task: global context and multilevel semantics. However, generating these two factors will always lead to high complexity. In order to solve this,

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Xing, Yongfeng, Luo Zhong, and Xian Zhong. "DARSegNet: A Real-Time Semantic Segmentation Method Based on Dual Attention Fusion Module and Encoder-Decoder Network." Mathematical Problems in Engineering 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/6195148.

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The convolutional neural network achieves excellent semantic segmentation results in artificially annotated datasets with complex scenes. However, semantic segmentation methods still suffer from several problems such as low use rate of the features, high computational complexity, and being far from practical real-time application, which bring about challenges for the image semantic segmentation. Two factors are very critical to semantic segmentation task: global context and multilevel semantics. However, generating these two factors will always lead to high complexity. In order to solve this,

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Grobe, M., C. Münzenmayer, H. Kuziela, K. Spinnler, and T. Wittenberg. "A Semantic Approach to Segmentation of Overlapping Objects." Methods of Information in Medicine 43, no. 04 (2004): 343–53. http://dx.doi.org/10.1055/s-0038-1633889.

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Summary Objectives: This paper aims at introducing a novel approach for segmentation of overlapping objects and at demonstrating its applicability to medical images. Methods: This work details a novel approach enhancing the known theory of full-segmentation of an image into regions by lifting it to a semantic segmentation into objects. Our theory allows the formal description of partitioning an image into regions on the first level and allowing the occurrence of overlaps and occlusions of objects on a second, semantic level. Possible applications for the use of this ‘semantical segmentation‘ a

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Chen, Shu, Lei Xu, Beiji Zou, and Jing Chen. "Semantic Relocation Parallel Network for Semantic Segmentation." Journal of Computer-Aided Design & Computer Graphics 34, no. 03 (2022): 373–81. http://dx.doi.org/10.3724/sp.j.1089.2022.18909.

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19

Fu, Siming, Hualiang Wang, Haoji Hu, et al. "Class semantic enhancement network for semantic segmentation." Journal of Visual Communication and Image Representation 96 (October 2023): 103924. http://dx.doi.org/10.1016/j.jvcir.2023.103924.

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Tian, Liang, Xiaorou Zhong, and Ming Chen. "Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model." Scientific Programming 2021 (November 3, 2021): 1–11. http://dx.doi.org/10.1155/2021/9491376.

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Accurate remote sensing image segmentation can guide human activities well, but current image semantic segmentation methods cannot meet the high-precision semantic recognition requirements of complex images. In order to further improve the accuracy of remote sensing image semantic segmentation, this paper proposes a new image semantic segmentation method based on Generative Adversarial Network (GAN) and Fully Convolutional Neural Network (FCN). This method constructs a deep semantic segmentation network based on FCN, which can enhance the receptive field of the model. GAN is integrated into FC

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21

Liu, Xiwen, Yong He, Jue Li, Rui Yan, Xiaoyu Li, and Hui Huang. "A Comparative Review on Enhancing Visual Simultaneous Localization and Mapping with Deep Semantic Segmentation." Sensors 24, no. 11 (2024): 3388. http://dx.doi.org/10.3390/s24113388.

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Visual simultaneous localization and mapping (VSLAM) enhances the navigation of autonomous agents in unfamiliar environments by progressively constructing maps and estimating poses. However, conventional VSLAM pipelines often exhibited degraded performance in dynamic environments featuring mobile objects. Recent research in deep learning led to notable progress in semantic segmentation, which involves assigning semantic labels to image pixels. The integration of semantic segmentation into VSLAM can effectively differentiate between static and dynamic elements in intricate scenes. This paper pr

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Ghimire, Aashish, Aman Mahaseth, Ramesh Thapa, Suraj Ale Magar Ale Magar, Sushil Kumar Singh, and Salik Ram Khanal. "Leather Defect Segmentation Using Semantic Segmentation Algorithms." Journal of Artificial Intelligence and Capsule Networks 4, no. 2 (2022): 131–38. http://dx.doi.org/10.36548/jaicn.2022.2.005.

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Leather is one of the essential materials in our life. It can be used widely to make different industrial products. Products made from leather are strong, expensive and durable which lasts for decades. So, It is very important for the industry to make a defect free product for their maximum profit and good customer feedback. Quality inspection is one of the important processes in the textile industry. It is done manually in most of the industry which is time taking, expensive, less accurate and requires lots of people. The main aim of our research work is to replace the manual process with aut

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Veganzones, Miguel, Ana Cisnal, Eusebio de la Fuente, and Juan Carlos Fraile. "Training Fully Convolutional Neural Networks for Lightweight, Non-Critical Instance Segmentation Applications." Applied Sciences 14, no. 23 (2024): 11357. https://doi.org/10.3390/app142311357.

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Augmented reality applications involving human interaction with virtual objects often rely on segmentation-based hand detection techniques. Semantic segmentation can then be enhanced with instance-specific information to model complex interactions between objects, but extracting such information typically increases the computational load significantly. This study proposes a training strategy that enables conventional semantic segmentation networks to preserve some instance information during inference. This is accomplished by introducing pixel weight maps into the loss calculation, increasing

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Sanjaya, Yongki Christian, Alexander Agung Santoso Gunawan, and Edy Irwansyah. "Semantic Segmentation for Aerial Images: A Literature Review." Engineering, MAthematics and Computer Science (EMACS) Journal 2, no. 3 (2020): 133–39. http://dx.doi.org/10.21512/emacsjournal.v2i3.6737.

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Semantic image segmentation is one of the fundamental applications of computer vision which can also be called pixel-level classification. 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. Due to this rapid growth, many models related to Semantic Image Segmentation have been produced and have also been used or applied in many domains such as medical areas and intelligent transportation. Therefore, our motivation in making this paper is to contribute to the w

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Zhu, Hesen, Shuang Song, Yunlong Gao, Guifang Shao, and Qingyuan Zhu. "Street tree segmentation method combining image semantic segmentation and point cloud clustering." Journal of Physics: Conference Series 2897, no. 1 (2024): 012035. https://doi.org/10.1088/1742-6596/2897/1/012035.

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Abstract As a common object in the park scene, the 3D information of street trees is crucial for digital cities, and extracting individual trees from point cloud data accurately and efficiently is a hotspot and a difficult point-in-point cloud processing. Aiming at the interference problem of complex non-tree objects and dynamic objects in the park environment, a tree point cloud segmentation method combining a deep learning network framework with a point cloud clustering algorithm is proposed in this paper. First, this paper applies the YOLOv8 to the semantic segmentation of camera images, an

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., P. S. Gunde, and S. K. Shirgave . "Survey On Semantic Segmentation." International Journal of Computer Sciences and Engineering 6, no. 12 (2018): 603–6. http://dx.doi.org/10.26438/ijcse/v6i12.603606.

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Orlova, Svetlana, and Alexander Lopota. "Scene recognition for confined spaces in mobile robotics: current state and tendencies." Robotics and Technical Cybernetics 10, no. 1 (2022): 14–24. http://dx.doi.org/10.31776/rtcj.10102.

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The article discusses the problem of scene recognition for mobile robotics. Subtasks that have to be solved to implement a high-level understanding of the environment are considered. The basis here is an understanding of the geometry and semantics of the scene, which can be decomposed into subtasks of robot localization, mapping and semantic analysis. Simultaneous localization and mapping (SLAM) techniques have already been successfully applied and, although they have some as yet unresolved problems for dynamic environments, do not present a problem for this issue. The focus of the work is on

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Ding, Y., X. Zheng, H. Xiong, and Y. Zhang. "SEMANTIC SEGMENTATION OF INDOOR 3D POINT CLOUD WITH SLENET." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 785–91. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-785-2019.

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<p><strong>Abstract.</strong> With the rapid development of new indoor sensors and acquisition techniques, the amount of indoor three dimensional (3D) point cloud models was significantly increased. However, these massive “blind” point clouds are difficult to satisfy the demand of many location-based indoor applications and GIS analysis. The robust semantic segmentation of 3D point clouds remains a challenge. In this paper, a segmentation with layout estimation network (SLENet)-based 2D&amp;ndash;3D semantic transfer method is proposed for robust segmentation of image-bas

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Yoo, S., C. Ko, G. Sohn, and H. Lee. "YUTO SEMANTIC: A LARGE SCALE AERIAL LIDAR DATASET FOR SEMANTIC SEGMENTATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 209–15. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-209-2023.

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Abstract. Creating virtual duplicates of the real world has garnered significant attention due to its applications in areas such as autonomous driving, urban planning, and urban mapping. One of the critical tasks in the computer vision community is semantic segmentation of outdoor collected point clouds. The development and research of robust semantic segmentation algorithms heavily rely on precise and comprehensive benchmark datasets. In this paper, we present the York University Teledyne Optech 3D Semantic Segmentation Dataset (YUTO Semantic), a multi-mission large-scale aerial LiDAR dataset

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Zhang, Wenbo, Lu Zhang, Ping Hu, Liqian Ma, Yunzhi Zhuge, and Huchuan Lu. "Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10166–75. https://doi.org/10.1609/aaai.v39i10.33103.

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Injecting semantics into 3D Gaussian Splatting (3DGS) has recently garnered significant attention. While current approaches typically distill 3D semantic features from 2D foundational models (e.g., CLIP and SAM) to facilitate novel view segmentation and semantic understanding, their heavy reliance on 2D supervision can undermine cross-view semantic consistency and necessitate complex data preparation processes, therefore hindering view-consistent scene understanding. In this work, we present FreeGS, an unsupervised semantic-embedded 3DGS framework that achieves view-consistent 3D scene underst

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Yang, J., and Z. Kang. "INDOOR SEMANTIC SEGMENTATION FROM RGB-D IMAGES BY INTEGRATING FULLY CONVOLUTIONAL NETWORK WITH HIGHER-ORDER MARKOV RANDOM FIELD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 717–24. http://dx.doi.org/10.5194/isprs-archives-xlii-4-717-2018.

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<p><strong>Abstract.</strong> Indoor scenes have the characteristics of abundant semantic categories, illumination changes, occlusions and overlaps among objects, which poses great challenges for indoor semantic segmentation. Therefore, we in this paper develop a method based on higher-order Markov random field model for indoor semantic segmentation from RGB-D images. Instead of directly using RGB-D images, we first train and perform RefineNet model only using RGB information for generating the high-level semantic information. Then, the spatial location relationship from dept

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Chen, Lijia, Honghui Chen, Yanqiu Xie, Tianyou He, Jing Ye, and Yushan Zheng. "An Efficient and Light Transformer-Based Segmentation Network for Remote Sensing Images of Landscapes." Forests 14, no. 11 (2023): 2271. http://dx.doi.org/10.3390/f14112271.

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High-resolution image segmentation for landscape applications has garnered significant attention, particularly in the context of ultra-high-resolution (UHR) imagery. Current segmentation methodologies partition UHR images into standard patches for multiscale local segmentation and hierarchical reasoning. This creates a pressing dilemma, where the trade-off between memory efficiency and segmentation quality becomes increasingly evident. This paper introduces the Multilevel Contexts Weighted Coupling Transformer (WCTNet) for UHR segmentation. This framework comprises the Mult-level Feature Weigh

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Li, Xiangtai, Houlong Zhao, Lei Han, Yunhai Tong, Shaohua Tan, and Kuiyuan Yang. "Gated Fully Fusion for Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11418–25. http://dx.doi.org/10.1609/aaai.v34i07.6805.

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Semantic segmentation generates comprehensive understanding of scenes through densely predicting the category for each pixel. High-level features from Deep Convolutional Neural Networks already demonstrate their effectiveness in semantic segmentation tasks, however the coarse resolution of high-level features often leads to inferior results for small/thin objects where detailed information is important. It is natural to consider importing low level features to compensate for the lost detailed information in high-level features. Unfortunately, simply combining multi-level features suffers from

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Shen, Xu, Liguo Weng, Min Xia, and Haifeng Lin. "Multi-Scale Feature Aggregation Network for Semantic Segmentation of Land Cover." Remote Sensing 14, no. 23 (2022): 6156. http://dx.doi.org/10.3390/rs14236156.

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Land cover semantic segmentation is an important technique in land. It is very practical in land resource protection planning, geographical classification, surveying and mapping analysis. Deep learning shows excellent performance in picture segmentation in recent years, but there are few semantic segmentation algorithms for land cover. When dealing with land cover segmentation tasks, traditional semantic segmentation networks often have disadvantages such as low segmentation precision and weak generalization due to the loss of image detail information and the limitation of weight distribution.

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Jiang, Qunyan, Juying Dai, Ting Rui, et al. "Detail Guided Multilateral Segmentation Network for Real-Time Semantic Segmentation." Applied Sciences 12, no. 21 (2022): 11040. http://dx.doi.org/10.3390/app122111040.

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With the development of unmanned vehicles and other technologies, the technical demand for scene semantic segmentation is more and more intense. Semantic segmentation requires not only rich high-level semantic information, but also rich detail information to ensure the accuracy of the segmentation task. Using a multipath structure to process underlying and semantic information can improve efficiency while ensuring segmentation accuracy. In order to improve the segmentation accuracy and efficiency of some small and thin objects, a detail guided multilateral segmentation network is proposed. Fir

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XU, CHENLIANG, RICHARD F. DOELL, STEPHEN JOSÉ HANSON, CATHERINE HANSON, and JASON J. CORSO. "A STUDY OF ACTOR AND ACTION SEMANTIC RETENTION IN VIDEO SUPERVOXEL SEGMENTATION." International Journal of Semantic Computing 07, no. 04 (2013): 353–75. http://dx.doi.org/10.1142/s1793351x13400114.

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Existing methods in the semantic computer vision community seem unable to deal with the explosion and richness of modern, open-source and social video content. Although sophisticated methods such as object detection or bag-of-words models have been well studied, they typically operate on low level features and ultimately suffer from either scalability issues or a lack of semantic meaning. On the other hand, video supervoxel segmentation has recently been established and applied to large scale data processing, which potentially serves as an intermediate representation to high level video semant

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Shu, Ruijun, and Shengjie Zhao. "Multi-Resolution Learning and Semantic Edge Enhancement for Super-Resolution Semantic Segmentation of Urban Scene Images." Sensors 24, no. 14 (2024): 4522. http://dx.doi.org/10.3390/s24144522.

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Super-resolution semantic segmentation (SRSS) is a technique that aims to obtain high-resolution semantic segmentation results based on resolution-reduced input images. SRSS can significantly reduce computational cost and enable efficient, high-resolution semantic segmentation on mobile devices with limited resources. Some of the existing methods require modifications of the original semantic segmentation network structure or add additional and complicated processing modules, which limits the flexibility of actual deployment. Furthermore, the lack of detailed information in the low-resolution

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Guo, Yu, Guigen Nie, Wenliang Gao, and Mi Liao. "2D Semantic Segmentation: Recent Developments and Future Directions." Future Internet 15, no. 6 (2023): 205. http://dx.doi.org/10.3390/fi15060205.

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Semantic segmentation is a critical task in computer vision that aims to assign each pixel in an image a corresponding label on the basis of its semantic content. This task is commonly referred to as dense labeling because it requires pixel-level classification of the image. The research area of semantic segmentation is vast and has achieved critical advances in recent years. Deep learning architectures in particular have shown remarkable performance in generating high-level, hierarchical, and semantic features from images. Among these architectures, convolutional neural networks have been wid

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Yao, M. M., X. M. Li, W. X. Wang, L. F. Xie, and S. J. Tang. "SEMANTIC SEGMENTATION OF INDOOR 3D POINT CLOUDS BY JOINT OPTIMIZATION OF GEOMETRIC FEATURES AND NEURAL NETWORKS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W2-2022 (October 14, 2022): 305–10. http://dx.doi.org/10.5194/isprs-annals-x-4-w2-2022-305-2022.

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Abstract. Indoor navigation, indoor robotics, and other deep applications of interior space can be realized through semantic segmentation of 3D point clouds. We propose a semantic segmentation method for point clouds that uses geometric features of point clouds and neural networks to address the problem of incomplete and inconsistent segmentation objectives in existing semantic segmentation methods. Using neural networks, semantic labels are extracted from indoor structural information as the first step. The paper proposes a probabilistic model to cross-validate the initial segmentation result

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Gao, Ruicheng, and Yue Qi. "Monocular Object-Level SLAM Enhanced by Joint Semantic Segmentation and Depth Estimation." Sensors 25, no. 7 (2025): 2110. https://doi.org/10.3390/s25072110.

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SLAM is regarded as a fundamental task in mobile robots and AR, implementing localization and mapping in certain circumstances. However, with only RGB images as input, monocular SLAM systems suffer problems of scale ambiguity and tracking difficulty in dynamic scenes. Moreover, high-level semantic information can always contribute to the SLAM process due to its similarity to human vision. Addressing these problems, we propose a monocular object-level SLAM system enhanced by real-time joint depth estimation and semantic segmentation. The multi-task network, called JSDNet, is designed to predict

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Tang, Jingjing, Li Wang, Jing Huang, Aiye Shi, and Lizhong Xu. "Image Semantic Recognition and Segmentation Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (September 30, 2022): 1–16. http://dx.doi.org/10.1155/2022/2439371.

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Semantic feature recognition in colour images is required for identifying uneven patterns in object detection and classification. The semantic features are identified by segmenting the colorimetric sensor array features through machine learning paradigms. Semantic segmentation is a method for identifying distinct elements in an image. This can be considered a task involving image classification at the pixel level. This article introduces a semantic feature-dependent array segmentation method (SFASM) to improve recognition accuracy due to irregular semantics. The proposed method incorporates a

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Xu, Huaiyuan, Xiaodong Chen, Huaiyu Cai, Yi Wang, Haitao Liang, and Haotian Li. "Semantic Matching Based on Semantic Segmentation and Neighborhood Consensus." Applied Sciences 11, no. 10 (2021): 4648. http://dx.doi.org/10.3390/app11104648.

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Establishing dense correspondences across semantically similar images is a challenging task, due to the large intra-class variation caused by the unconstrained setting of images, which is prone to cause matching errors. To suppress potential matching ambiguity, NCNet explores the neighborhood consensus pattern in the 4D space of all possible correspondences, which is based on the assumption that the correspondence is continuous in space. We retain the neighborhood consensus constraint, while introducing semantic segmentation information into the features, which makes them more distinguishable

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Xu, Shibiao, Jiaxi Sun, Jiguang Zhang, Weiliang Meng, and Xiaopeng Zhang. "Lightweight Semantic Architecture Modeling by 3D Feature Line Detection." Remote Sensing 15, no. 8 (2023): 1957. http://dx.doi.org/10.3390/rs15081957.

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Existing architecture semantic modeling methods in 3D complex urban scenes continue facing difficulties, such as limited training data, lack of semantic information, and inflexible model processing. Focusing on extracting and adopting accurate semantic information into a modeling process, this work presents a framework for lightweight modeling of buildings that joints point clouds semantic segmentation and 3D feature line detection constrained by geometric and photometric consistency. The main steps are: (1) Extraction of single buildings from point clouds using 2D-3D semi-supervised semantic

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Rao, Yunbo, Menghan Zhang, Zhanglin Cheng, Junmin Xue, Jiansu Pu, and Zairong Wang. "Semantic Point Cloud Segmentation Using Fast Deep Neural Network and DCRF." Sensors 21, no. 8 (2021): 2731. http://dx.doi.org/10.3390/s21082731.

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Accurate segmentation of entity categories is the critical step for 3D scene understanding. This paper presents a fast deep neural network model with Dense Conditional Random Field (DCRF) as a post-processing method, which can perform accurate semantic segmentation for 3D point cloud scene. On this basis, a compact but flexible framework is introduced for performing segmentation to the semantics of point clouds concurrently, contribute to more precise segmentation. Moreover, based on semantics labels, a novel DCRF model is elaborated to refine the result of segmentation. Besides, without any s

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Chen, Jian, and Fen Luo. "A Survey of Image Semantic Segmentation Algorithm Based on Deep Learning." Academic Journal of Science and Technology 5, no. 1 (2023): 13–14. http://dx.doi.org/10.54097/ajst.v5i1.5248.

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Image semantic segmentation technology is one of the core research contents in the field of computer vision, and has a wide range of applications in production and life. With the improvement of computer performance and the continuous development of deep learning technology, researchers have increasingly high research enthusiasm for the performance of image semantic segmentation. This paper summarizes the research status of image semantic segmentation based on deep learning and introduces the common datasets used in the field of semantic segmentation. Finally, we point out the existing problems

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Yu, Hongqi, Sixian Chan, Xiaolong Zhou, and Xiaoqin Zhang. "SGFormer: Semantic-Geometry Fusion Transformer for Multi-modal 3D Panoptic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 9 (2025): 9616–25. https://doi.org/10.1609/aaai.v39i9.33042.

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Modern methods for autonomous driving perception widely adopt multi-modal fusion to enhance 3D scene understanding. However, existing methods suffer from inferior semantic extraction in image encoders that treat all pixels equally, ignoring contextual differences. The generated multi-modal representations also typically lack comprehensive semantic and spatial geometry information, which is crucial for the 3D panoptic segmentation task. In this paper, we propose a novel Semantic-Geometry Fusion Transformer (SGFormer) that extracts adaptive semantic contexts, aggregates geometric information and

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Xu, Xinying, Guiqing Li, Gang Xie, Jinchang Ren, and Xinlin Xie. "Weakly Supervised Deep Semantic Segmentation Using CNN and ELM with Semantic Candidate Regions." Complexity 2019 (March 14, 2019): 1–12. http://dx.doi.org/10.1155/2019/9180391.

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The task of semantic segmentation is to obtain strong pixel-level annotations for each pixel in the image. For fully supervised semantic segmentation, the task is achieved by a segmentation model trained using pixel-level annotations. However, the pixel-level annotation process is very expensive and time-consuming. To reduce the cost, the paper proposes a semantic candidate regions trained extreme learning machine (ELM) method with image-level labels to achieve pixel-level labels mapping. In this work, the paper casts the pixel mapping problem into a candidate region semantic inference problem

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Li, Shengyuan, and Xuefeng Zhao. "A Performance Improvement Strategy for Concrete Damage Detection Using Stacking Ensemble Learning of Multiple Semantic Segmentation Networks." Sensors 22, no. 9 (2022): 3341. http://dx.doi.org/10.3390/s22093341.

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Semantic segmentation network-based methods can detect concrete damage at the pixel level. However, the performance of a single semantic segmentation network is often limited. To improve the concrete damage detection performance of a semantic segmentation network, a stacking ensemble learning-based concrete crack detection method using multiple semantic segmentation networks is proposed. To realize this method, a database including 500 images and their labels with concrete crack and spalling is built and divided into training and testing sets. At first, the training and prediction of five sema

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Song, Xiaona, Haichao Liu, Lijun Wang, et al. "A Semantic Segmentation Method for Road Environment Images Based on Hybrid Convolutional Auto-Encoder." Traitement du Signal 39, no. 4 (2022): 1235–45. http://dx.doi.org/10.18280/ts.390416.

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Deep convolutional neural networks (CNNs) have presented amazing performance in the task of semantic segmentation. However, the network model is complex, the training time is prolonged, the semantic segmentation accuracy is not high and the real-time performance is not good, so it is difficult to be directly used in the semantic segmentation of road environment images of autonomous vehicles. As one of the three models of deep learning, the auto-encoder (AE) has powerful data learning and feature extracting capabilities from the raw data itself. In this study, the network architecture of auto-e

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Chen, Guanke, Haibin Li, Yaqian Li, Wenming Zhang, and Tao Song. "Parallel segmentation network for real-time semantic segmentation." Engineering Applications of Artificial Intelligence 148 (May 2025): 110487. https://doi.org/10.1016/j.engappai.2025.110487.

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