Results 11 to 20 of about 5,165 (221)
TSSP-UNet: A Two-Stage Weakly Supervised Pathological Image Segmentation With Point Annotations. [PDF]
IET Syst BiolDeep convolutional neural networks excel at image segmentation but face challenges with complex instance training and high‐precision annotation acquisition. This study proposes TSSP‐UNet, a two‐stage weakly supervised segmentation approach: the first stage trains a segmentation network with constraint and attention mechanisms plus a feature aggregation
Wang S +5 more
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Robust Superpixel Tracking [PDF]
IEEE Transactions on Image Processing, 2014 While numerous algorithms have been proposed for object tracking with demonstrated success, it remains a challenging problem for a tracker to handle large appearance change due to factors such as scale, motion, shape deformation, and occlusion. One of the main reasons is the lack of effective image representation schemes to account for appearance ...
Fan Yang 0016 +2 more
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SUPERPIXEL CLASSIFICATION OF HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGE BASED ON MULTI-SCALE CNN AND SCALE PARAMETER ESTIMATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 In recent years, considerable attention has been paid to integrate convolutional neural network (CNN) with land cover classification of high spatial resolution remote sensing image.
Y. Chen, D. Ming
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 The convolutional neural network (CNN) has a poor performance in nonuniform and edge regions due to the limitation of fixed receptive field. At the same time, feature stacking of input data can bring burden and overfitting to the network.
Ronghua Shang +5 more
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Temporally Consistent Superpixels [PDF]
2013 IEEE International Conference on Computer Vision, 2013 Super pixel algorithms represent a very useful and increasingly popular preprocessing step for a wide range of computer vision applications, as they offer the potential to boost efficiency and effectiveness. In this regards, this paper presents a highly competitive approach for temporally consistent super pixels for video content. The approach is based
Matthias Reso +3 more
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Scale‐adaptive superpixels for medical images
Quantitative Biology, 2022 Background Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing. Traditionally, size uniformity is one of the significant features of superpixels. However, in medical images, in which subjects scale varies
Limin Sun, Dongyang Ma, Yuanfeng Zhou
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Hybrid superpixel segmentation [PDF]
2015 International Conference on Image and Vision Computing New Zealand (IVCNZ), 2015 Superpixel over-segment image into meaningful clusters so that pixels in each cluster belong to one object. Many state-of-art superpixel algorithms have to make trade-offs between different concerns. As a result, algorithms that can produce good result in some situations fail in another.
Yuan Liu +3 more
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Adaptive Superpixel Segmentation of Marine SAR Images by Aggregating Fisher Vectors
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Superpixel segmentation is an important technique for image analysis. In this article, we develop a new superpixel segmentation approach and investigate its application on ship target detection in marine synthetic aperture radar (SAR) images.
Xueqian Wang +3 more
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A Hybrid Model Based on Superpixel Entropy Discrimination for PolSAR Image Classification
Remote Sensing, 2022 Superpixel segmentation is widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, the classification method using simple majority voting cannot easily handle evidence conflicts in a single superpixel.
Jili Sun, Lingdong Geng, Yize Wang
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IEEE Access, 2020 This paper addresses an image matching methodology designed for correspondence problem in computer vision. Firstly, a novel superpixel segmentation model driven by spatially constrained Student's-t mixture model (SMM) is proposed.
Pengyu Wang, Hongqing Zhu, Xiaofeng Ling
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