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TransitNet: A lightweight semantic segmentation network for urban traffic scene understanding. [PDF]

PLoS One
Zhang H, Zhao Z, Chu X, Liu Y.
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Multi-Scale Feature for Recognition

2009 International Conference on Electronic Computer Technology, 2009
For combining global and local features effectively, a multi-scale description and feature extraction algorithm is proposed. The original image is decomposed into two levels by wavelet analysis, and the two reconstructed approximate images are divided into several regions.
Songze Lei, Chongyang Hao, Min Qi
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Feature Enhancement for Multi-scale Object Detection

Neural Processing Letters, 2020
Recently, deep learning has brought great progress in object detection. However, we believe that traditional hand-crafted features may still contain valuable human knowledge complementary to features learned from raw data. Besides, almost all top-performing object detection methods extract features by using backbones originally designed for image ...
Huicheng Zheng, Jiajie Chen, Lvran Chen, Ye Li, Zhiwei Yan   +4 more
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Monocular Depth Estimation With Multi-Scale Feature Fusion

IEEE Signal Processing Letters, 2021
Depth estimation from a single image is a crucial but challenging task for reconstructing 3D structures and inferring scene geometry. However, most existing methods fail to extract more detailed information and estimate the distant small-scale objects well.
Xianfa Xu, Zhe Chen 0005, Fuliang Yin
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Multi-scale feature selection in stereo

Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 2003
In binocular stereo matching, points in left and right images are matched according to features that characterize each point and identify pairs of points. When one tries to use multiple features, a difficult problem is which feature, or combination of features, to use.
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Multi-scale Feature Spaces for Shape Processing and Analysis

2010 Shape Modeling International Conference, 2010
In digital geometry processing and shape modeling, the Laplace-Beltrami and the heat diffusion operator, together with the corresponding Laplacian eigenmaps, harmonic and geometry-aware functions, have been used in several applications, which range from surface parameterization, deformation, and compression to segmentation, clustering, and comparison ...
GPatane', B Falcidieno
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Multi scale feature point tracking

2014 22nd Iranian Conference on Electrical Engineering (ICEE), 2014
Feature point tracking is one of the most important subjects in machine vision due to its abundant applications. The advantage of this sort of tracking compared with other tracking methods is in extracting exact position information from tracking object's components.
Reza Serajeh, Amir Ebrahimi Ghahnavieh, Karim Faez   +2 more
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Multi-scale feature network for few-shot learning

Multimedia Tools and Applications, 2020
Few-shot learning aims to learn a classifier that has good generalization performance in new classes, where each class only a small number of labeled examples are available. The existing few-shot classification methods use the single-scale image do not learn effective feature representation.
Mengya Han, Ronggui Wang, Juan Yang 0001, Lixia Xue, Min Hu   +4 more
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