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Image/Video Segmentation Based on Sensor Fusion
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Image/Video Segmentation Based on Sensor Fusion

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 5560

Special Issue Editor

Prof. Dr. Kaihua Zhang

E-Mail Website
Guest Editor
School of Information and Control, Nanjing University of Information Science and Technology, Nanjing, China
Interests: computer vision; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past decades, with the fast development of deep learning, image/video segmentation has achieved great advances as one of the hottest research topics in computer vision. Recent advancements have been made in applying deep learning in multi-modality signals for image/video segmentation, such as text and image for natural image/video segmentation, multi-modality images for medical image/video segmentation, and spectral and hyperspectral images for satellite image/video segmentation, to name a few. Especially with the introduction of the foundation model into segmentation, the segmentation anything model (SAM) has been proposed and pushed the boundaries of segmentation into a new era, which demonstrates astonishing performance on segmenting any objects in a variety of applications, such as visual object tracking, medical image segmentation, and satellite image segmentation, to name a few.

Nonetheless, several challenges still need to be addressed, particularly in the context of weakly annotated training sets, few-shot training samples, class imbalances, segmentation over large-size data, etc.

The objective of this Special Issue is to generate a comprehensive understanding of deep learning in image/video segmentation for both theoretical and practical implications. Both original research and review articles are welcome. Topics include, but are not limited to, the following:

  • Multi-modality natural/medical/satellite remote sensing image/video segmentation;
  • Few-shot learning on segmentation;
  • SAM-based image/video segmentation;
  • Applications of video segmentation on visual tracking, object detection, and object recognition.

Prof. Dr. Kaihua Zhang
Guest Editor

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Published Papers (3 papers)

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Research

17 pages, 3072 KiB  
Article
Mask-Pyramid Network: A Novel Panoptic Segmentation Method
by Peng-Fei Xian, Lai-Man Po, Jing-Jing Xiong, Yu-Zhi Zhao, Wing-Yin Yu and Kwok-Wai Cheung
Sensors 2024, 24(5), 1411; https://doi.org/10.3390/s24051411 - 22 Feb 2024
Cited by 1 | Viewed by 1668
Abstract
In this paper, we introduce a novel panoptic segmentation method called the Mask-Pyramid Network. Existing Mask RCNN-based methods first generate a large number of box proposals and then filter them at each feature level, which requires a lot of computational resources, while most [...] Read more.
In this paper, we introduce a novel panoptic segmentation method called the Mask-Pyramid Network. Existing Mask RCNN-based methods first generate a large number of box proposals and then filter them at each feature level, which requires a lot of computational resources, while most of the box proposals are suppressed and discarded in the Non-Maximum Suppression process. Additionally, for panoptic segmentation, it is a problem to properly fuse the semantic segmentation results with the Mask RCNN-produced instance segmentation results. To address these issues, we propose a new mask pyramid mechanism to distinguish objects and generate much fewer proposals by referring to existing segmented masks, so as to reduce computing resource consumption. The Mask-Pyramid Network generates object proposals and predicts masks from larger to smaller sizes. It records the pixel area occupied by the larger object masks, and then only generates proposals on the unoccupied areas. Each object mask is represented as a H × W × 1 logit, which fits well in format with the semantic segmentation logits. By applying SoftMax to the concatenated semantic and instance segmentation logits, it is easy and natural to fuse both segmentation results. We empirically demonstrate that the proposed Mask-Pyramid Network achieves comparable accuracy performance on the Cityscapes and COCO datasets. Furthermore, we demonstrate the computational efficiency of the proposed method and obtain competitive results. Full article
(This article belongs to the Special Issue Image/Video Segmentation Based on Sensor Fusion)
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19 pages, 26567 KiB  
Article
Fast Automatic Fuzzy C-Means Knitting Pattern Color-Separation Algorithm Based on Superpixels
by Xin Ru, Ran Chen, Laihu Peng and Weimin Shi
Sensors 2024, 24(1), 281; https://doi.org/10.3390/s24010281 - 3 Jan 2024
Cited by 1 | Viewed by 1365
Abstract
Patterns entered into knitting CAD have thousands or tens of thousands of different colors, which need to be merged by color-separation algorithms. However, for degraded patterns, the current color-separation algorithms cannot achieve the desired results, and the clustering quantity parameter needs to be [...] Read more.
Patterns entered into knitting CAD have thousands or tens of thousands of different colors, which need to be merged by color-separation algorithms. However, for degraded patterns, the current color-separation algorithms cannot achieve the desired results, and the clustering quantity parameter needs to be managed manually. In this paper, we propose a fast and automatic FCM color-separation algorithm based on superpixels, which first uses the Real-ESRGAN blind super-resolution network to clarify the degraded patterns and obtain high-resolution images with clear boundaries. Then, it uses the improved MMGR-WT superpixel algorithm to pre-separate the high-resolution images and obtain superpixel images with smooth and accurate edges. Subsequently, the number of superpixel clusters is automatically calculated by the improved density peak clustering (DPC) algorithm. Finally, the superpixels are clustered using fast fuzzy c-means (FCM) based on a color histogram. The experimental results show that not only is the algorithm able to automatically determine the number of colors in the pattern and achieve the accurate color separation of degraded patterns, but it also has lower running time. The color-separation results for 30 degraded patterns show that the segmentation accuracy of the color-separation algorithm proposed in this paper reaches 95.78%. Full article
(This article belongs to the Special Issue Image/Video Segmentation Based on Sensor Fusion)
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24 pages, 4750 KiB  
Article
Underwater Target Detection Based on Parallel High-Resolution Networks
by Zhengwei Bao, Ying Guo, Jiyu Wang, Linlin Zhu, Jun Huang and Shu Yan
Sensors 2023, 23(17), 7337; https://doi.org/10.3390/s23177337 - 23 Aug 2023
Cited by 9 | Viewed by 1848
Abstract
A parallel high-resolution underwater target detection network is proposed to address the problems of complex underwater scenes and limited target feature extraction capability. First, a high-resolution network (HRNet), a lighter high-resolution human posture estimation network, is used to improve the target feature representation [...] Read more.
A parallel high-resolution underwater target detection network is proposed to address the problems of complex underwater scenes and limited target feature extraction capability. First, a high-resolution network (HRNet), a lighter high-resolution human posture estimation network, is used to improve the target feature representation and effectively reduce the semantic information lost in the image during sampling. Then, the attention module (A-CBAM) is improved to capture complex feature distributions by modeling the two-dimensional space in the activation function stage through the introduction of the flexible rectified linear units (FReLU) activation function to achieve pixel-level spatial information modeling capability. Feature enhancement in the spatial and channel dimensions is performed to improve understanding of fuzzy targets and small target objects and to better capture irregular and detailed object layouts. Finally, a receptive field augmentation module (RFAM) is constructed to obtain sufficient semantic information and rich detail information to further enhance the robustness and discrimination of features and improve the detection capability of the model for multi-scale underwater targets. Experimental results show that the method achieves 81.17%, 77.02%, and 82.9% mean average precision (mAP) on three publicly available datasets, specifically underwater robot professional contest (URPC2020, URPC2018) and pattern analysis, statistical modeling, and computational learning visual object classes (PASCAL VOC2007), respectively, demonstrating the effectiveness of the proposed network. Full article
(This article belongs to the Special Issue Image/Video Segmentation Based on Sensor Fusion)
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