Results 11 to 20 of about 11,596,145 (301)
Multi-Scale Salient Features for Analyzing 3D Shapes [PDF]
Journal of Computer Science and Technology, 2012 Extracting feature regions on mesh models is crucial for shape analysis and understanding. It can be widely used for various 3D content-based applications in graphics and geometry field. In this paper, we present a new algorithm of extracting multi-scale salient features on meshes. This is based on robust estimation of curvature on multiple scales. The
Yong-Liang Yang, Chao-Hui Shen
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Learning multi-scale features for foreground segmentation [PDF]
Pattern Analysis and Applications, 2019 Foreground segmentation algorithms aim segmenting moving objects from the background in a robust way under various challenging scenarios. Encoder-decoder type deep neural networks that are used in this domain recently perform impressive segmentation results.
Long Ang Lim, Hacer Yalim Keles
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Multi-scale phase-based local features [PDF]
2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003 Local feature methods suitable for image feature based object recognition and for the estimation of motion and structure are composed of two steps, namely the 'where' and 'what' steps. The 'where' step (e.g., interest point detector) must select image points that are robustly localizable under common image deformations and whose neighborhoods are ...
Gustavo Carneiro 0001, Allan D. Jepson
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Multi-scale Feature Aggregation for Crowd Counting
CoRR, 2022 Convolutional Neural Network (CNN) based crowd counting methods have achieved promising results in the past few years. However, the scale variation problem is still a huge challenge for accurate count estimation. In this paper, we propose a multi-scale feature aggregation network (MSFANet) that can alleviate this problem to some extent.
Xiaoheng Jiang +8 more
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Insights into Imaging, 2023 Purpose To investigate the effectiveness of an automatic analysis framework based on 3D-CT multi-scale features in predicting Ki67 expression levels in substantial renal cell carcinoma (RCC).
Huancheng Yang +10 more
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A Few Shot Classification Methods Based on Multiscale Relational Networks
Applied Sciences, 2022 Learning information from a single or a few samples is called few-shot learning. This learning method will solve deep learning’s dependence on a large sample. Deep learning achieves few-shot learning through meta-learning: “how to learn by using previous
Wenfeng Zheng +6 more
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Remote Sensing, 2021 Multifarious hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have been gradually proposed and achieve a promising classification performance.
Dongxu Liu +6 more
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Multi-scale features fusion module.
, 2022 Multi-scale features fusion module.
Junnian Wang (13970197) +4 more
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Evaluating Precipitation Features and Rainfall Characteristics in a Multi‐Scale Modeling Framework
Journal of Advances in Modeling Earth Systems, 2020 Cloud and precipitation systems are simulated with a multi‐scale modeling framework (MMF) and compared over the Tropics and Subtropics against the Tropical Rainfall Measuring Mission (TRMM) Radar‐defined Precipitation Features (RPFs) product.
Jiun‐Dar Chern +4 more
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CLOUD DETECTION BY FUSING MULTI-SCALE CONVOLUTIONAL FEATURES [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks.
Z. Li +6 more
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