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2019 1

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Dense scale-invariant feature transform (dense SIFT) 1

Engineering vehicles 1

Feature learning 1

Gaussian net (GNET) 1

Histogram of oriented gradient (HOG) 1

Object detection 1

Part models 1

Retinal vessel segmentation 1

Saliency 1

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A saliency and Gaussian net model for retinal vessel segmentation Research Articles

Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, Shao-hua ZHENG, Lun YU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1075-1086 doi: 10.1631/FITEE.1700404

Abstract: A novel deep learning structure called the Gaussian net (GNET) model combined with a saliency model isA saliency image is used as the input of the GNET model replacing the original image.

Keywords： Retinal vessel segmentation     Saliency model     Gaussian net (GNET)     Feature learning    

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Detection of engineering vehicles in high-resolution monitoring images

Xun Liu, Yin Zhang, San-yuan Zhang, Ying Wang, Zhong-yan Liang, Xiu-zi Ye,star.liuxun@gmail.com,yinzh@zju.edu.cn,syzhang@zju.edu.cn,maggiewang0427@gmail.com

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 5,   Pages 346-357 doi: 10.1631/FITEE.1500026

Abstract: This paper presents a novel formulation for detecting objects with articulated rigid bodies from high-resolution monitoring images, particularly . There are many pixels in high-resolution monitoring images, and most of them represent the background. Our method first detects object patches from monitoring images using a coarse detection process. In this phase, we build a descriptor based on histograms of oriented gradient, which contain color frequency information. Then we use a linear support vector machine to rapidly detect many image patches that may contain object parts, with a low false negative rate and a high false positive rate. In the second phase, we apply a refinement classification to determine the patches that actually contain objects. In this stage, we increase the size of the image patches so that they include the complete object using models of the object parts. Then an accelerated and improved salient mask is used to improve the performance of the dense scale-invariant feature transform descriptor. The detection process returns the absolute position of positive objects in the original images. We have applied our methods to three datasets to demonstrate their effectiveness.

Keywords： detection     Histogram of oriented gradient (HOG)     Dense scale-invariant feature transform (dense SIFT)     Saliency    

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Title Author Date Type Operation

A saliency and Gaussian net model for retinal vessel segmentation

Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, Shao-hua ZHENG, Lun YU

Journal Article

Detection of engineering vehicles in high-resolution monitoring images

Xun Liu, Yin Zhang, San-yuan Zhang, Ying Wang, Zhong-yan Liang, Xiu-zi Ye,star.liuxun@gmail.com,yinzh@zju.edu.cn,syzhang@zju.edu.cn,maggiewang0427@gmail.com

Journal Article