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Chee Seng Chan
Researcher at University of Malaya
Publications - 178
Citations - 6010
Chee Seng Chan is an academic researcher from University of Malaya. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 29, co-authored 167 publications receiving 3991 citations. Previous affiliations of Chee Seng Chan include University of Portsmouth & MIMOS.
Papers
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Journal ArticleDOI
Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
TL;DR: In this article, the authors provide a comprehensive survey of state-of-the-art remote sensing deep learning research for remote sensing applications, focusing on theories, tools, and challenges for the remote sensing community.
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A robust arbitrary text detection system for natural scene images
TL;DR: A robust system based on the concepts of Mutual Direction Symmetry (MDS), Mutual Magnitude Symmetric (MMS) and Gradient Vector Symmeter (GVS) properties to identify text pixel candidates regardless of any orientations including curves from natural scene images is presented.
Proceedings ArticleDOI
Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition
Chee-Kheng Chng,Chee Seng Chan +1 more
TL;DR: The Total-Text dataset as discussed by the authors is a large scale scene text dataset with a large number of curve-oriented text orientations, including horizontal and multioriented text, and features curved text.
Journal ArticleDOI
How deep learning extracts and learns leaf features for plant classification
TL;DR: This paper learns useful leaf features directly from the raw representations of input data using Convolutional Neural Networks (CNN), and gains intuition of the chosen features based on a Deconvolutional Network (DN) approach, and gains insights into the design of new hybrid feature extraction models which are able to further improve the discriminative power of plant classification systems.
Proceedings ArticleDOI
Deep-plant: Plant identification with convolutional neural networks
TL;DR: In this article, convolutional neural networks (CNN) were used to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England.