no code implementations • 20 Apr 2024 • Junpu Wang, Guili Xu, Chunlei Li, Guangshuai Gao, Yuehua Cheng
Unsupervised anomaly detection using only normal samples is of great significance for quality inspection in industrial manufacturing.
no code implementations • 9 Apr 2024 • ChenGuang Liu, Guangshuai Gao, Ziyue Huang, Zhenghui Hu, Qingjie Liu, Yunhong Wang
2) Small object size leads to insufficient information for effective detection.
no code implementations • 24 Dec 2020 • Ran Qin, Qingjie Liu, Guangshuai Gao, Di Huang, Yunhong Wang
Objects in aerial images usually have arbitrary orientations and are densely located over the ground, making them extremely challenge to be detected.
1 code implementation • 7 Dec 2020 • Guangshuai Gao, Qingjie Liu, Zhenghui Hu, Lu Li, Qi Wen, Yunhong Wang
Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention.
1 code implementation • 28 Aug 2020 • Guangshuai Gao, Qingjie Liu, Yunhong Wang
Object counting, whose aim is to estimate the number of objects from a given image, is an important and challenging computation task.
no code implementations • 20 Aug 2020 • Guangshuai Gao, Wenting Zhao, Qingjie Liu, Yunhong Wang
Co-saliency detection aims to detect common salient objects from a group of relevant images.
3 code implementations • 28 Mar 2020 • Guangshuai Gao, Junyu. Gao, Qingjie Liu, Qi. Wang, Yunhong Wang
Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields.
no code implementations • 14 Feb 2020 • Guangshuai Gao, Qingjie Liu, Yunhong Wang
Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied.
no code implementations • 18 Feb 2017 • Chunlei Li, Guangshuai Gao, Zhoufeng Liu, Di Huang, Sheng Liu, Miao Yu
In order to accurately detect defects in patterned fabric images, a novel detection algorithm based on Gabor-HOG (GHOG) and low-rank decomposition is proposed in this paper.