Prof. Chih-Yang Lin
Yuan Ze University, Taoyuan District, Taoyuan, Taiwan
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Representative Publications
View All Publications7 March 2023 in The International Journal of Advanced Manufacturing Technology
Journal Article
FAFityanul Akhyar
Fityanul AkhyarYLYing Liu
Ying LiuCHChao-Yung Hsu
Chao-Yung HsuTSTimothy K. Shih
Timothy K. ShihChih-Yang LinJournal Article
NHNoorkholis Luthfil Hakim
Noorkholis Luthfil HakimTSTimothy K. Shih
Timothy K. ShihSASandeli Priyanwada Kasthuri Arachchi
Sandeli Priyanwada Kasthuri ArachchiWAWisnu Aditya
Wisnu AdityaYCYi-Cheng Chen
Yi-Cheng ChenChih-Yang LinWith the recent growth of Smart TV technology, the demand for unique and beneficial applications motivates the study of a unique gesture-based system for a smart TV-like environment. Combining movie recommendation, social media platform, call a friend application, weather updates, chatting app, and tourism platform into a single system regulated by natural-like gesture controller is proposed to allow the ease of use and natural interaction. Gesture recognition problem solving was designed through 24 gestures of 13 static and 11 dynamic gestures that suit to the environment. Dataset of a sequence of RGB and depth images were collected, preprocessed, and trained in the proposed deep learning architecture. Combination of three-dimensional Convolutional Neural Network (3DCNN) followed by Long Short-Term Memory (LSTM) model was used to extract the spatio-temporal features. At the end of the classification, Finite State Machine (FSM) communicates the model to control the class decision results based on application context. The result suggested the combination data of depth and RGB to hold 97.8% of accuracy rate on eight selected gestures, while the FSM has improved the recognition rate from 89% to 91% in a real-time performance.
15 February 2017 in IEEE Transactions on Industrial Electronics
Journal Article
Chia-Hung YehChih-Yang Lin
Kahlil MuchtarHLHsiang-Erh Lai
Hsiang-Erh Lai
MSMing-Ting Sun
Ming-Ting Sun
Moving object extraction is the core of event detection in video surveillance. Although many related methods have been proposed to extract moving objects, even advanced applications still encounter cavity problems, which are false detection and deficiencies resulting from cavities inside the body or fragmented foreground objects. In this paper, an entirely new structure for extracting moving objects is proposed. This scheme is based on the concepts of hysteresis thresholding and motion compensation, which constitute spatial and temporal compensations, respectively. Experimental results show that the proposed scheme can outperform modern moving object detection methods in terms of precision, recall, F-measure, and other measurements.
25 September 2020 in IEEE Transactions on Image Processing
Journal Article
Chih-Yang LinZTZhuang Tao
Zhuang Tao
AXAi-Sheng Xu
Ai-Sheng XuLKLi-Wei Kang
Li-Wei KangFAFityanul Akhyar
Fityanul AkhyarVarious weather conditions, such as rain, haze, or snow, can degrade visual quality in images/videos, which may significantly degrade the performance of related applications. In this paper, a novel framework based on sequential dual attention deep network is proposed for removing rain streaks (deraining) in a single image, called by SSDRNet (Sequential dual attention-based Single image DeRaining deep Network). Since the inherent correlation among rain steaks within an image should be stronger than that between the rain streaks and the background (non-rain) pixels, a two-stage learning strategy is implemented to better capture the distribution of rain streaks within a rainy image. The two-stage deep neural network primarily involves three blocks: residual dense blocks (RDBs), sequential dual attention blocks (SDABs), and multi-scale feature aggregation modules (MAMs), which are all delicately and specifically designed for rain removal. The two-stage strategy successfully learns very fine details of the rain steaks of the image and then clearly removes them. Extensive experimental results have shown that the proposed deep framework achieves the best performance on qualitative and quantitative metrics compared with state-of-the-art methods. The corresponding code and the trained model of the proposed SSDRNet have been available online at https://github.com/fityanul/SDAN-for-Rain-Removal .
28 March 2023 in Journal of Intelligent Manufacturing
Journal Article
9 June 2023 in IEEE Access
Journal Article
YPYori Pusparani
Yori Pusparani
Chih-Yang Lin
Yih-Kuen JanFLFu-Yu Lin
Fu-Yu Lin
BLBen-Yi Liau
Ben-Yi Liau
Chi-Wen Lung +5
Alzheimer’s disease (AD) is a major public health priority. Hippocampus is one of the most affected areas of the brain and is easily accessible as a biomarker using MRI images in machine learning for diagnosing AD. In machine learning, using entire MRI image slices showed lower accuracy for AD classification. We present the select slices method by landmarks on the hippocampus region in MRI images. This study aims to see which views of MRI images have higher accuracy for AD classification. Then, to get the value of three views and categories, we used multiclass classification with the publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset using Resnet50 and LeNet. The models were used in a total dataset of 4,500 MRI slices in three views and categories. Our study demonstrated that the selecting slices performed better than using entire slices in MRI images for AD classification. Our method improves the accuracy of machine learning, and the coronal view showed higher accuracy. This method played a significant role in improving the accuracy of machine learning performance. The results for the coronal view were similar to the medical experts usually used to diagnose AD. We also found that LeNet models became the potential model for AD classification.
About Me
Chih-Yang Lin is a professor in the Department of Mechanical Engineering at National Central University, Taoyuan, Taiwan. He has been a professor at Yuan-Ze University, Taiwan, where he was also the Dean of the International Academy and Chief of Global Affairs. He is an IET Fellow, having authored and co-authored over 200 papers in international conferences and journals, and has received Best Paper Awards from Pacific-Rim Conference on Multimedia (PCM) in 2008, Best Paper Awards and Excellent Paper Award from IPPR Conference on Computer Vision, Graphics and Image Processing Conference in 2009, 2013, and 2019, Best Paper Award from 6th International Visual Informatics Conference 2019 (IVIC'19), and Best Paper Award from 2nd International Conference on Broadband Communications, Wireless Sensors and Powering in 2020. He has served as a program chair, session chair, publication chair, publicity chair, and workshop organizer in many international conferences, including AHFE, ICCE, ACCV, IEEE Multimedia Big Data, ACM IH&MMSec, APSIPA, and CVGIP. He is also a regular Reviewer of the IEEE Transactions on Image Processing, the IEEE Transactions on Circuits and Systems for Video Technology, the IEEE Transactions on Multimedia, IEEE Access, and many other prestigious Elsevier journals. His research fields include computer vision, machine learning, deep learning, image processing, big data analysis, and the design of surveillance systems.