Results 51 to 60 of about 495 (155)
Dual-Scale Pixel Aggregation Transformer for Change Detection in Multitemporal Remote Sensing Images
Remote SensingTransformers have recently been applied to change detection (CD) of multitemporal remote sensing images because of their ability to model global information.
Kai Zhang +5 more
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Using Deep Learning in Infrared Images to Enable Human Gesture Recognition for Autonomous Vehicles
IEEE Access, 2020 The realization of a novel human gesture recognition algorithm is essential to enable the effective collision avoidance of autonomous vehicles. Compared to visible spectrum cameras, the use of infrared imaging can enable more robust human gesture ...
Keke Geng, Guodong Yin
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Multi-scale guided filtering integrated with superpixel and patch shift
Tongxin xuebao, 2022 In order to avoid the phenomenon that edges were easily blurred during filtering, a multi-scale guided filtering integrated with superpixel and patch shift was proposed.Firstly, the bilateral filtering was applied to an input image to get more accurate ...
Jianwu LONG, Jiangzhou ZHU
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IEEE Access, 2017 This paper presents a region-based relaxed multiple kernel collaborative representation method for the spatial-spectral classification of hyperspectral images. The proposed method consists of three steps.
Jianjun Liu +3 more
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2015 IEEE Winter Conference on Applications of Computer Vision, 2015 Superpixels and supervoxels play an important role in many computer vision applications, such as image segmentation, object recognition, and video analysis. In this paper, we propose a new hierarchical edge-weighted centroidal Voronoi tessellation (HEWCVT) method for generating superpixels/supervoxels in multiple scales.
Youjie Zhou, Lili Ju, Song Wang 0002
openaire +2 more sources
Hybrid CNN-GCN Network for Hyperspectral Image Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingIn recent years, convolutional neural networks (CNNs) have been impressive due to their excellent feature representation abilities, but it is difficult to learn long-distance spatial structures information. Unlike CNN, graph convolutional networks (GCNs)
Cuiping Shi, Diling Liao, Liguo Wang
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Automated Detection of Red Lesions Using Superpixel Multichannel Multifeature
, 2017 Red lesions can be regarded as one of the earliest lesions in diabetic retinopathy (DR) and automatic detection of red lesions plays a critical role in diabetic retinopathy diagnosis.
Wenyou Du +5 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingIn recent years, graph convolutional networks (GCNs) have been introduced for hyperspectral image (HSI) classification due to their ability to effectively process the inherent graph structure of HSI data.
Suyi Li +4 more
doaj +1 more source
, 2023 Convolutional neural networks (CNNs) for hyperspectral image (HSI) classification have generated good progress. Meanwhile, graph convolutional networks (GCNs) have also attracted considerable attention by using unlabeled data, broadly and explicitly ...
Zhou, Hao +11 more
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Superpixel cut for figure-ground image segmentation
, 2016 Figure-ground image segmentation has been a challenging problem in computer vision. Apart from the difficulties in establishing an effective framework to divide the image pixels into meaningful groups, the notions of figure and ground often need to be ...
Rosenhahn, Bodo, Yang, Michael Ying
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