學術出版;;Publications

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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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MVSNet plus plus : Learning Depth-Based Attention Pyramid Features for Multi-View Stereo
(2020-01-01)
Chen, Po-Heng
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Yang, Hsiao-Chien
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Chen, Kuan-Wen
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Chen, Yong-Sheng
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資訊工程學系
The goal of Multi-View Stereo (MVS) is to reconstruct 3D point-cloud model from multiple views. On the basis of the considerable progress of deep learning, an increasing amount of research has moved from traditional MVS methods to learning-based ones. However, two issues remain unsolved in the existing state-of-the-art methods: (1) only high-level information is considered for depth estimation. This may reduce the localization accuracy of 3D points as the learned model lacks spatial information; and (2) most of the methods require additional post-processing or network refinement to generate a smooth 3D model. This significantly increases the number of model parameters or the computational complexity. To this end, we propose MVSNet++, an end-to-end trainable network for dense depth estimation. Such an estimated depth map can further be applied to 3D model reconstruction. Different from previous methods, in the proposed method, we first adopt feature pyramid structures for both feature extraction and cost volume regularization. This can lead to accurate 3D point localization by fusing multi-level information. To generate smooth depth map, we then carefully integrate instance normalization into MVSNet++ without increasing model parameters and computational burden. Furthermore, we additionally design three loss functions and integrate Curriculum Learning framework into the training process, which can lead to an accurate reconstruction of 3D model. MVSNet++ is evaluated on DTU and Tanks & Temples benchmarks with comprehensive ablation studies. Experimental results demonstrate that our proposed method performs favorably against previous state-of-the-art methods, showing the accuracy and effectiveness of the proposed MVSNet++.
FADE: Feature Aggregation for Depth Estimation With Multi-View Stereo
(2020-01-01)
Yang, Hsiao-Chien
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Chen, Po-Heng
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Chen, Kuan-Wen
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Lee, Chen-Yi
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Chen, Yong-Sheng
Both structural and contextual information is essential and widely used in image analysis. However, current multi-view stereo (MVS) approaches usually use a single common pre-trained model as pixel descriptor to extract features, which mix structural and contextual information together and thus increase the difficulty of matching correspondence. In this paper, we propose FADE (feature aggregation for depth estimation), which treats spatial and context information separately and focuses on aggregating features for efficient learning of the MVS problem. Spatial information includes image details such as edges and corners, whereas context information comprises object features such as shapes and traits. To aggregate these multi-level features, we use an attention mechanism to select important features for matching. We then build a plane sweep volume by using a homography backward warping method to generate match candidates. Furthermore, we propose a novel cost volume regularization network aims to minimize the noise in the matching candidates. Finally, we take advantage of 3D stacked hourglass and regression to produces high-quality depth maps. With these well-aggregated features, FADE can efficiently perform dense depth reconstruction, achieving state-of-the-art performance in terms of accuracy and requiring the least amount of model parameters.
Unravelling the Spatio-Temporal Neurodynamics of Rhythm Encoding-reproduction Networks by a Novel fMRI Autoencoder
(2019-01-01)
Kao, Chia-Hsiang
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Yang, Ching-Ju
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Cheng, Li-Kai
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Yu, Hsin-Yen
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Chen, Yong-Sheng
Visualization of how the external stimuli are processed dynamically in the brain would help understanding the neural mechanisms of functional segregation and integration. The present study proposed a novel temporal autoencoder to estimate the neurodynamics of functional networks involved in rhythm encoding and reproduction. A fully-connected two-layer autoencoder was proposed to estimate the temporal dynamics in functional magnetic resonance image recordings. By minimizing the reconstruction error between the predicted next time sample and the corresponding ground truth next time sample, the system was trained to extract spatial patterns of functional network dynamics without any supervision effort. The results showed that the proposed model was able to extract the spatial patterns of task-related functional dynamics as well as the interactions between them. Our findings suggest that artificial neural networks would provide a useful tool to resolve temporal dynamics of neural processing in the human brain.
Deep Neural Network with Attention Mechanism for Classification of Motor Imagery EEG
(2019-01-01)
Huang, Yen-Cheng
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Chang, Jia-Ren
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Chen, Li-Fen
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Chen, Yong-Sheng
;
交大名義發表
This paper presents a deep neural network architecture for the classification of motor imagery electroencephalographic recordings. This classification task usually encounters difficulties such as data with poor signal-to-noise ratio, contamination from muscle activity, body movements, and external interferences, and both intra-subject and intersubject variability. Through the spatiotemporal features automatically learned from training data, deep neural networks continue to demonstrate their good performance, versatility, and adaptation capability. In this work, we developed a novel neural network model which can extract signal features from multiple electrodes in a manner similar to that of conventional signal processing methods, such as common spatial patterns and common temporal patterns. The proposed neural network model comprises an attention mechanism, which calculates the importance of each electrode, and a spatial convolution layer. Compared to the results obtained using a variety of state-of-the-art deep learning techniques, the proposed scheme represents a considerable advancement in classification accuracy when applied to the BCI competition IV dataset 2a. By training with data of all subjects, the proposed universal neural network model outperforms state-of-the-art methods in terms of classification accuracy.
Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer's disease
(2019-01-01)
Lin, Shih-Yen
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Lin, Chen-Pei
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Hsieh, Tsung-Jen
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Lin, Chung-Fen
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Chen, Sih-Huei
Alzheimer's disease (AD), an irreversible neurodegenerative disease, is the most common type of dementia in elderly people. This present study incorporated multiple structural and functional connectivity metrics into a graph theoretical analysis framework and investigated alterations in brain network topology in patients with mild cognitive impairment (MCI) and AD. By using this multiparametric analysis, we expected different connectivity metrics may reflect additional or complementary information regarding the topological changes in brain networks in MCI or AD. In our study, a total of 73 subjects participated in this study and underwent the magnetic resonance imaging scans. For the structural network, we compared commonly used connectivity metrics, including fractional anisotropy and normalized streamline count, with multiple diffusivity-based metrics. We compared Pearson correlation and covariance by investigating their sensitivities to functional network topology. Significant disruption of structural network topology in MCI and AD was found predominantly in regions within the limbic system, prefrontal and occipital regions, in addition to widespread alterations of local efficiency. At a global scale, our results showed that the disruption of the structural network was consistent across different edge definitions and global network metrics from the MCI to AD stages. Significant changes in connectivity and tract-specific diffusivity were also found in several limbic connections. Our findings suggest that tract-specific metrics (e.g., fractional anisotropy and diffusivity) provide more sensitive and interpretable measurements than does metrics based on streamline count. Besides, the use of inversed radial diffusivity provided additional information for understanding alterations in network topology caused by AD progression and its possible origins. Use of this proposed multiparametric network analysis framework may facilitate early MCI diagnosis and AD prevention.
Investigation of functional brain network reconfiguration during vocal emotional processing using graph-theoretical analysis
(2019-05-01)
Lin, Shih-Yen
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Lee, Chi-Chun
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Chen, Yong-Sheng
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Kuo, Li-Wei
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資訊工程學系
Vocal expression is essential for conveying the emotion during social interaction. Although vocal emotion has been explored in previous studies, little is known about how perception of different vocal emotional expressions modulates the functional brain network topology. In this study, we aimed to investigate the functional brain networks under different attributes of vocal emotion by graph-theoretical network analysis. Functional magnetic resonance imaging (fMRI) experiments were performed on 36 healthy participants. We utilized the Power-264 functional brain atlas to calculate the interregional functional connectivity (FC) from fMRI data under resting state and vocal stimuli at different arousal and valence levels. The orthogonal minimal spanning trees method was used for topological filtering. The paired-sample t-test with Bonferroni correction across all regions and arousal-valence levels were used for statistical comparisons. Our results show that brain network exhibits significantly altered network attributes at FC, nodal and global levels, especially under high-arousal or negative-valence vocal emotional stimuli. The alterations within/between well-known large-scale functional networks were also investigated. Through the present study, we have gained more insights into how comprehending emotional speech modulates brain networks. These findings may shed light on how the human brain processes emotional speech and how it distinguishes different emotional conditions.
Asymmetric Engagement of Amygdala and Its Gamma Connectivity in Early Emotional Face Processing
(2015-01-28)
Liu, Tai-Ying
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Chen, Yong-Sheng
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Hsieh, Jen-Chuen
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Chen, Li-Fen
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資訊工程學系
The amygdala has been regarded as a key substrate for emotion processing. However, the engagement of the left and right amygdala during the early perceptual processing of different emotional faces remains unclear. We investigated the temporal profiles of oscillatory gamma activity in the amygdala and effective connectivity of the amygdala with the thalamus and cortical areas during implicit emotion-perceptual tasks using event-related magnetoencephalography (MEG). We found that within 100 ms after stimulus onset the right amygdala habituated to emotional faces rapidly (with duration around 20-30 ms), whereas activity in the left amygdala (with duration around 50-60 ms) sustained longer than that in the right. Our data suggest that the right amygdala could be linked to autonomic arousal generated by facial emotions and the left amygdala might be involved in decoding or evaluating expressive faces in the early perceptual emotion processing. The results of effective connectivity provide evidence that only negative emotional processing engages both cortical and subcortical pathways connected to the right amygdala, representing its evolutional significance (survival). These findings demonstrate the asymmetric engagement of bilateral amygdala in emotional face processing as well as the capability of MEG for assessing tha-lamo-cortico-limbic circuitry.
Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis
(2017-12-01)
Low, Intan
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Kuo, Po-Chih
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Liu, Yu-Hsiang
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Tsai, Cheng-Lin
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Chao, Hsiang-Tai
How chronic pain affects brain functions remains unclear. As a potential indicator, brain complexity estimated by entropy-based methods may be helpful for revealing the underlying neurophysiological mechanism of chronic pain. In this study, complexity features with multiple time scales and spectral features were extracted from resting-state magnetoencephalographic signals of 156 female participants with/without primary dysmenorrhea (PDM) during pain-free state. Revealed by multiscale sample entropy (MSE), PDM patients (PDMs) exhibited loss of brain complexity in regions associated with sensory, affective, and evaluative components of pain, including sensorimotor, limbic, and salience networks. Significant correlations between MSE values and psychological states (depression and anxiety) were found in PDMs, which may indicate specific nonlinear disturbances in limbic and default mode network circuits after long-term menstrual pain. These findings suggest that MSE is an important measure of brain complexity and is potentially applicable to future diagnosis of chronic pain.
Encoding of menstrual pain experience with theta oscillations in women with primary dysmenorrhea
(2017-11-22)
Lee, Pin-Shiuan
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Low, Intan
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Chen, Yong-Sheng
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Tu, Cheng-Hao
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Chao, Hsiang-Tai
Theta oscillation (4-7 Hz) is well documented for its association with neural processes of memory. Pronounced increase of theta activity is commonly observed in patients with chronic neurogenic pain. However, its association with encoding of pain experience in patients with chronic pain is still unclear. The goal of the present study is to investigate the theta encoding of sensory and emotional information of long-term menstrual pain in women with primary dysmenorrhea (PDM). Forty-six young women with PDM and 46 age-matched control subjects underwent resting-state magnetoencephalography study during menstrual and periovulatory phases. Our results revealed increased theta activity in brain regions of pain processing in women with PDM, including the right parahippocampal gyrus, right posterior insula, and left anterior/middle cingulate gyrus during the menstrual phase and the left anterior insula and the left middle/inferior temporal gyrus during the periovulatory phase. The correlations between theta activity and the psychological measures pertaining to pain experience (depression, state anxiety, and pain rating index) implicate the role of theta oscillations in emotional and sensory processing of pain. The present study provides evidence for the role of theta oscillations in encoding the immediate and sustained effects of pain experience in young women with PDM.
A novel point cloud registration using 2D image features
(2017-01-07)
Lin, Chien-Chou
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Tai, Yen-Chou
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Lee, Jhong-Jin
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Chen, Yong-Sheng
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資訊工程學系
Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.

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