Search

Your search keyword '"LIN, JZAU-SHENG"' showing total 108 results
Start Over Author "LIN, JZAU-SHENG"
108 results on '"LIN, JZAU-SHENG"'

3. Extracting Blood Vessel in Sclera-Conjunctiva Image Based on Fuzzy C-means and Modified Cone Responses

Author

Lin, Jzau-Sheng, Huang, Yu-Yang, Liao, Yu-Yi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Ali, Moonis, editor, Bosse, Tibor, editor, Hindriks, Koen V., editor, Hoogendoorn, Mark, editor, Jonker, Catholijn M., editor, and Treur, Jan, editor

Published
2013
Full Text
View/download PDF

20. The application of competitive Hopfield neural network to medical image segmentation

Author

Cheng, Kuo-Sheng, Lin, Jzau-Sheng, and Mao, Chi-Wu

Subjects
Neural networks -- Usage, Diagnostic imaging -- Research, Business, Electronics, Electronics and electrical industries, Health care industry
Abstract

In this paper, a parallel and unsupervised approach using the competitive Hopfield neural network (CHNN) is proposed for medical image segmentation. It is a kind of Hopfield network which incorporates the winner-takes-all (WTA) learning mechanism. The image segmentation is conceptually formulated as a problem of pixel clustering based upon the global information of the gray level distribution. Thus, the energy function for minimization is defined as the mean of the squared distance measures of the gray levels within each class. The proposed network avoids the onerous procedure of determining values for the weighting factors in the energy function. In addition, its training scheme enables the network to learn rapidly and effectively. For an image of n gray levels and c interesting objects, the proposed CHNN would consist of n by c neurons and be independent of the image size. In both simulation studies and practical medical image segmentation, the CHNN method shows promising results in comparison with two well-known methods: the hard and the fuzzy c-means (FCM) methods.

Published
1996

21. A Motor-Imagery BCI System Based on Deep Learning Networks and Its Applications

Author

Lin, Jzau-Sheng

Subjects
Medical / Neuroscience
Abstract

Motor imagery brain-computer interface (BCI) by using of deep-learning models is proposed in this paper. In which, we used the electroencephalogram (EEG) signals of motor imagery (MI-EEG) to identify different imagery activities. The brain dynamics of motor imagery are usually measured by EEG as non-stationary time series of low signal-to-noise ratio. However, a variety of methods have been previously developed to classify MI-EEG signals getting not satisfactory results owing to lack of characteristics in time-frequency features. In this paper, discrete wavelet transform (DWT) was applied to transform MIEEG signals and extract their effective coefficients as the time-frequency features. Then two deep learning (DL) models named Long-short term memory (LSTM) and gated recurrent neural networks (GRNN) are used to classify MI-EEG data. LSTM is designed to fight against vanishing gradients. GRNN makes each recurrent unit to capture dependencies of different time scales adaptively. Similar scheme of the LSTM unit, GRNN has gating units that modulate the flow of information inside the unit, but without having a separate memory cells. Experimental results show that GRNN and LSTM yield higher classification accuracies compared to the existing approaches that is helpful for the further research and application of relative RNN in processing of MI-EEG.

Published
2018

Catalog

Books, media, physical & digital resources
See catalog results