Search Results for author: Jinchang Ren

Found 8 papers, 1 papers with code

Nondestructive Quality Control in Powder Metallurgy using Hyperspectral Imaging

no code implementations26 Jul 2022 Yijun Yan, Jinchang Ren, He Sun

Measuring the purity in the metal powder is critical for preserving the quality of additive manufacturing products.

Nondestructive Testing of Composite Fibre Materials with Hyperspectral Imaging : Evaluative Studies in the EU H2020 FibreEUse Project

no code implementations4 Nov 2021 Yijun Yan, Jinchang Ren, Huan Zhao, James F. C. Windmill, Winifred Ijomah, Jesper de Wit, Justus von Freeden

Through capturing spectral data from a wide frequency range along with the spatial information, hyperspectral imaging (HSI) can detect minor differences in terms of temperature, moisture and chemical composition.

SR-GAN: Semantic Rectifying Generative Adversarial Network for Zero-shot Learning

no code implementations15 Apr 2019 Zihan Ye, Fan Lyu, Linyan Li, Qiming Fu, Jinchang Ren, Fuyuan Hu

First, we pre-train a Semantic Rectifying Network (SRN) to rectify semantic space with a semantic loss and a rectifying loss.

Zero-Shot Learning

Does Normalization Methods Play a Role for Hyperspectral Image Classification?

no code implementations9 Oct 2017 Faxian Cao, Zhijing Yang, Jinchang Ren, Mengying Jiang, Wing-Kuen Ling

For Hyperspectral image (HSI) datasets, each class have their salient feature and classifiers classify HSI datasets according to the class's saliency features, however, there will be different salient features when use different normalization method.

Classification General Classification +1

Sparse Representation Based Augmented Multinomial Logistic Extreme Learning Machine with Weighted Composite Features for Spectral Spatial Hyperspectral Image Classification

no code implementations12 Sep 2017 Faxian Cao, Zhijing Yang, Jinchang Ren, Wing-Kuen Ling

To tackle these two problems, in this paper, we propose a new framework for ELM based spectral-spatial classification of HSI, where probabilistic modelling with sparse representation and weighted composite features (WCF) are employed respectively to derive the op-timized output weights and extract spatial features.

Classification General Classification +1

Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

no code implementations8 Sep 2017 Faxian Cao, Zhijing Yang, Jinchang Ren, Wing-Kuen Ling

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values.

General Classification Hyperspectral Image Classification

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