Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Qingge Ji"'
Publikováno v:
IET Image Processing, Vol 18, Iss 1, Pp 233-246 (2024)
Abstract Current skeleton‐based action recognition methods usually assume the input skeleton is complete and noise‐free. However, it is inevitable that the captured skeletons are incomplete due to occlusions or noisy due to changes in the environ
Externí odkaz:
https://doaj.org/article/00b9628a0619472cb07d01a0acb202f8
Publikováno v:
IET Image Processing, Vol 15, Iss 10, Pp 2192-2201 (2021)
Abstract Large‐scale variations may cause a serious problem in crowd counting. In recent years, most methods for this problem use convolutional neural networks with a fixed scale for encoding and decoding image features. The scale of the convolutio
Externí odkaz:
https://doaj.org/article/12583f60f7564ffd808d83f6aed6a31b
Autor:
Qingge Ji, Chaofeng Li, Xianshu Fu, Jinyan Liao, Xuezhen Hong, Xiaoping Yu, Zihong Ye, Mingzhou Zhang, Yulou Qiu
Publikováno v:
Molecules, Vol 28, Iss 6, p 2803 (2023)
This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 Ophiopogon japonicus samples
Externí odkaz:
https://doaj.org/article/8add417c949844f4895c5dea0bc4b400
Autor:
Chaofeng Li, Qingge Ji, Xianshu Fu, Xiaoping Yu, Zihong Ye, Mingzhou Zhang, Chuanxin Sun, Yulou Qiu
Publikováno v:
Molecules, Vol 27, Iss 13, p 3968 (2022)
Rice cultivation is one of the most significant human-created sources of methane gas. How to accurately measure the methane concentration produced by rice cultivation has become a major problem. The price of the automatic gas sampler used as a nation
Externí odkaz:
https://doaj.org/article/6bb6245f0bdc413bb26ff78a0a181956
Autor:
Xianshu Fu, Xuezhen Hong, Jinyan Liao, Qingge Ji, Chaofeng Li, Mingzhou Zhang, Zihong Ye, Xiaoping Yu
Publikováno v:
Foods, Vol 10, Iss 12, p 2986 (2021)
Of the salmon sold in China’s consumer market, 92% was labelled as Norwegian salmon, but was in fact was mainly imported from Chile. The aim of this study was to establish an effective method for discriminating the geographic origin of imported sal
Externí odkaz:
https://doaj.org/article/94611e791e534ff5b7fabe9daa2e81e3
Autor:
Xianshu Fu, Erjing Chen, Biao Ma, Ying Xu, Peiying Hao, Mingzhou Zhang, Zihong Ye, Xiaoping Yu, Chaofeng Li, Qingge Ji
Publikováno v:
Foods, Vol 10, Iss 2, p 413 (2021)
Heavy metals in food packaging materials have been indicated to release into the environment at slow rates. Heavy metal contamination, especially that of cadmium (Cd), is widely acknowledged as a global environment threat that leads to continuous gro
Externí odkaz:
https://doaj.org/article/66d1a11abfe84b4ba9fd66b416f16c9b
Publikováno v:
Algorithms, Vol 13, Iss 7, p 169 (2020)
Modeling spatiotemporal representations is one of the most essential yet challenging issues in video action recognition. Existing methods lack the capacity to accurately model either the correlations between spatial and temporal features or the globa
Externí odkaz:
https://doaj.org/article/86f1e0f1164c4a7984b5b06b7aef645f
Publikováno v:
Algorithms, Vol 13, Iss 3, p 60 (2020)
Optical coherence tomography (OCT) is an optical high-resolution imaging technique for ophthalmic diagnosis. In this paper, we take advantages of multi-scale input, multi-scale side output and dual attention mechanism and present an enhanced nested U
Externí odkaz:
https://doaj.org/article/bd16390bc5f24a919b6e1db19dab9e38
Hierarchical-Matching-Based Online and Real-Time Multi-Object Tracking with Deep Appearance Features
Publikováno v:
Algorithms, Vol 13, Iss 4, p 80 (2020)
Based on tracking-by-detection, we propose a hierarchical-matching-based online and real-time multi-object tracking approach with deep appearance features, which can effectively reduce the false positives (FP) in tracking. For the purpose of increasi
Externí odkaz:
https://doaj.org/article/423d69cfedc04186828beacb17452767
Publikováno v:
Algorithms, Vol 12, Iss 3, p 51 (2019)
Finetuning pre-trained deep neural networks (DNN) delicately designed for large-scale natural images may not be suitable for medical images due to the intrinsic difference between the datasets. We propose a strategy to modify DNNs, which improves the
Externí odkaz:
https://doaj.org/article/04e0e5e5088c4191bd0be01d058fc589