J
Jinchang Ren
Researcher at Robert Gordon University
Publications - 312
Citations - 8628
Jinchang Ren is an academic researcher from Robert Gordon University. The author has contributed to research in topics: Hyperspectral imaging & Feature extraction. The author has an hindex of 38, co-authored 274 publications receiving 6400 citations. Previous affiliations of Jinchang Ren include Hong Kong Polytechnic University & Northwestern Polytechnical University.
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Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning
TL;DR: A novel and effective geospatial object detection framework is proposed by combining the weakly supervised learning (WSL) and high-level feature learning by jointly integrating saliency, intraclass compactness, and interclass separability in a Bayesian framework.
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Background Prior-Based Salient Object Detection via Deep Reconstruction Residual
TL;DR: A novel framework for saliency detection is proposed by first modeling the background and then separating salient objects from the background by developing stacked denoising autoencoders with deep learning architectures to model the background.
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Classification of Hyperspectral Images by Exploiting Spectral–Spatial Information of Superpixel via Multiple Kernels
TL;DR: Experimental results on three widely used real HSIs indicate that the proposed SC-MK approach outperforms several well-known classification methods.
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Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging
Jaime Zabalza,Jinchang Ren,Jiangbin Zheng,Huimin Zhao,Chunmei Qing,Zhijing Yang,Peijun Du,Stephen Marshall +7 more
TL;DR: Segmented SAE (S-SAE) is proposed by confronting the original features into smaller data segments, which are separately processed by different smaller SAEs, which has resulted in reduced complexity but improved efficacy of data abstraction and accuracy of data classification.
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Medical image analysis with artificial neural networks.
TL;DR: A focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing is provided to increase awareness of how neural networks can be applied to these areas.