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Sung Wook Baik
Researcher at Sejong University
Publications - 234
Citations - 8776
Sung Wook Baik is an academic researcher from Sejong University. The author has contributed to research in topics: Convolutional neural network & Computer science. The author has an hindex of 38, co-authored 202 publications receiving 5183 citations. Previous affiliations of Sung Wook Baik include George Mason University & Universiti Teknologi Malaysia.
Papers
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Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
TL;DR: A novel action recognition method by processing the video data using convolutional neural network (CNN) and deep bidirectional LSTM (DB-LSTM) network that is capable of learning long term sequences and can process lengthy videos by analyzing features for a certain time interval.
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Multi-grade brain tumor classification using deep CNN with extensive data augmentation
TL;DR: A novel convolutional neural network (CNN) based multi-grade brain tumor classification system that is experimentally evaluated on both augmented and original data and results show its convincing performance compared to existing methods.
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Convolutional Neural Networks Based Fire Detection in Surveillance Videos
TL;DR: Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in CCTV surveillance systems compared to state-of-the-art methods.
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Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications
TL;DR: This paper proposes an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire, which uses smaller convolutional kernels and contains no dense, fully connected layers.
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Early fire detection using convolutional neural networks during surveillance for effective disaster management
TL;DR: An early fire detection framework using fine-tuned convolutional neural networks for CCTV surveillance cameras, which can detect fire in varying indoor and outdoor environments is proposed and an adaptive prioritization mechanism for cameras in the surveillance system is proposed to ensure the autonomous response.