J
Jie Yang
Researcher at Shanghai Jiao Tong University
Publications - 680
Citations - 12772
Jie Yang is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Image segmentation & Feature extraction. The author has an hindex of 46, co-authored 629 publications receiving 10558 citations. Previous affiliations of Jie Yang include East China University of Science and Technology & Chinese Ministry of Education.
Papers
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Journal ArticleDOI
Semiempirical Quantum Chemical Method and Artificial Neural Networks Applied for λmax Computation of Some Azo Dyes
TL;DR: The maximum absorption wavelengths of 31 azo dyes have been calculated by two comprehensive methods using the semiempirical quantum chemical method, PM3, and the weight decay based artificial neural network (WD-ANN) or the early stopping based artificial Neural network (ES-ANN).
Proceedings ArticleDOI
Car Plate Detection Using Cascaded Tree-Style Learner Based on Hybrid Object Features
TL;DR: This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features to reduce the false alarm and the false dismissal while retaining a high detection ratio.
Journal ArticleDOI
A spiking neural network model for obstacle avoidance in simulated prosthetic vision
TL;DR: It is argued that spiking neural networks (SNN) are effective techniques for object recognition and for the first time a SNN model for obstacle recognition is introduced to assist blind people wearing prosthetic vision devices by modelling and classifying spatio-temporal video data.
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Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion
TL;DR: A simple yet effective approach using Retinex theory and Taylor series expansion for nighttime image dehazing, referred to as ‘RDT’ is proposed, which demonstrates the superior performance of the proposed RDT method over the state-of-the-art methods.
Journal ArticleDOI
Mutual information based multi-modal remote sensing image registration using adaptive feature weight
TL;DR: This letter strives to present a novel mutual information scheme for image registration in remote sensing scenario based on feature map technique, and incorporates the LOG and Guided Filter methods into image registration for the first time to construct a new feature map based on differences and similarities strategy.