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Multiple functional connectivity networks fusion for schizophrenia diagnosis

Medical & biological engineering & computing 2020年第8期58卷 1779-1790页

作者： Hongliang Zou Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China. hongliangzou@***. PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China.

Accurate diagnosis of schizophrenia is of great importance to patients and clinicians. Recent studies have found that different frequency bands contain complementary information for diagnosis and prognosis. However, conventional multiple frequency functional connectivity (FC) networks using Pearson's correlation coefficient (PCC) are usually based on pairwise correlations among different brain regions on single frequency band, while ignoring the interactions between regions in different frequency bands, the relationship among different networks, and the nonlinear properties of blood-oxygen-level-dependent (BOLD) signal. To take into account these relationships, we propose in this study a multiple networks fusion method for schizophrenia diagnosis. Specifically, we first construct FC networks within the same and across frequency from the resting-state functional magnetic resonance imaging (rs-fMRI) time series by using extended maximal information coefficient (eMIC) based on four frequency bands: slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz), and slow-2 (0.198-0.25 Hz). Then, these networks are combined nonlinearly through network fusion, which generates a unified network for each subject. Features extracted from the unified network are used for final classification. Experimental results demonstrated that the interaction between distinct brain regions across different frequency bands can significantly improve the classification performance, comparing with conventional FC analysis based on specific or entire low-frequency band. The promising results suggest that our proposed framework would be a useful tool in computer-aided diagnosis of schizophrenia. Graphical abstract The flowchart of proposed classification framework.

关键词： Across-frequency Functional connectivity Schizophrenia diagnosis eMIC rs-fMRI

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A single frame and multi-frame joint network for 360-degree panorama video super-resolution

arXiv

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arXiv 2020年

作者： Liu, Hongying Ruan, Zhubo Fang, Chaowei Zhao, Peng Shang, Fanhua Liu, Yuanyuan Wang, Lijun Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi’an China Information School North China University of Technology Beijing China

Spherical videos, also known as 360◦ (panorama) videos, can be viewed with various virtual reality devices such as computers and head-mounted displays. They attract large amount of interest since awesome immersion can be experienced when watching spherical videos. However, capturing, storing and transmitting high-resolution spherical videos are extremely expensive. In this paper, we propose a novel single frame and multi-frame joint network (SMFN) for recovering high-resolution spherical videos from low-resolution inputs. To take advantage of pixel-level inter-frame consistency, deformable convolutions are used to eliminate the motion difference between feature maps of the target frame and its neighboring frames. A mixed attention mechanism is devised to enhance the feature representation capability. The dual learning strategy is exerted to constrain the space of solution so that a better solution can be found. A novel loss function based on the weighted mean square error is proposed to emphasize on the super-resolution of the equatorial regions. This is the first attempt to settle the super-resolution of spherical videos, and we collect a novel dataset from the Internet, MiG Panorama Video, which includes 204 videos. Experimental results on 4 representative video clips demonstrate the efficacy of the proposed method. The dataset and code are available at https://***/lovepiano/SMFN_For_360VSR. Copyright © 2020, The Authors. All rights reserved.

关键词： Helmet mounted displays

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Fast and light-weight network for single frame structured illumination microscopy super-resolution

arXiv

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arXiv 2021年

作者： Cheng, Xi Li, Jun Dai, Qiang Fu, Zhenyong Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Technology Soochow University Suzhou China

Structured illumination microscopy (SIM) is an important super-resolution based microscopy technique that breaks the diffraction limit and enhances optical microscopy systems. With the development of biology and medical engineering, there is a high demand for real-time and robust SIM imaging under extreme low light and short exposure environments. Existing SIM techniques typically require multiple structured illumination frames to produce a high-resolution image. In this paper, we propose a single-frame structured illumination microscopy (SF-SIM) based on deep learning. Our SF-SIM only needs one shot of a structured illumination frame and generates similar results compared with the traditional SIM systems that typically require 15 shots. In our SF-SIM, we propose a noise estimator which can effectively suppress the noise in the image and enable our method to work under the low light and short exposure environment, without the need for stacking multiple frames for non-local denoising. We also design a bandpass attention module that makes our deep network more sensitive to the change of frequency and enhances the imaging quality. Our proposed SF-SIM is almost 14 times faster than traditional SIM methods when achieving similar results. Therefore, our method is significantly valuable for the development of microbiology and medicine. © 2021, CC BY-NC-SA.

关键词： Optical resolving power

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A Deep Generalized Correlation Network for Bitemporal image Change Detection

A Deep Generalized Correlation Network for Bitemporal Image ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Rongfang Wang Weidong Wang Jia-Wei Chen Licheng Jiao Hongxia Hao Key Laboratory of Intelligent Perception Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Xi'an China School of Computer Science and Technology Xidian University Xi'an China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Recently, many convolution neural networks have been successfully employed in bitemporal SAR image change detection. However, most methods are developed based on the traditional framework that exploits the changed region from a difference image (DI) that is usually subject to the speckle. To essentially solve this issue, in this paper, we propose a deep canonic correlation network for bitemporal SAR image. In the proposed network, bitemporal SAR images and its corresponding DI are taken as the inputs and then three deep neural networks are designed to employ their features, respectively. Then the changed regions are obtained by the exploited features. Finally, we compare the proposed method with other deep learning methods and perform the comparison on four sets of bitemporal SAR images. The experimental results show that our proposed method outperforms other methods.

关键词： Radar polarimetry Neural networks Synthetic aperture radar Training Feature extraction Correlation Remote sensing

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Scene Attention Mechanism for Remote Sensing image Caption Generation

Scene Attention Mechanism for Remote Sensing Image Caption G...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Shiqi Wu Xiangrong Zhang Xin Wang Chen Li Licheng Jiao Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Remote sensing images play an important role in various applications. To make it easier for humans to understand remote sensing images, the task of remote sensing image captioning attracts more and more researchers' attention. Inspired from the way human receives visual information, attention mechanism has been widely used in remote sensing image understanding. To catch more scene information and improve the stability of the generated sentences, a new attention mechanism called scene attention is proposed. Except for the current attention via the current hidden state of the long shortterm memory network (LSTM), our proposed method simultaneously explores the global visual information from the mean feature of all convolutional features. The effectiveness of the proposed method is evaluated on UCM-captions, Sydney-captions and RSICD datasets. The results of our experiment show that comparing with some other captioning methods, our method is more stable and obtains a better performance.

关键词： Remote sensing Feature extraction Task analysis Visualization Data mining Logic gates Decoding

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Non-aligned Supervision for Real image Dehazing

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Fan, Junkai Li, Xiang Qian, Jianjun Li, Jun Yang, Jian Nanjing University of Science and Technology PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China Nankai University PCA Lab VCIP School of Computer Science Tianjin China

Removing haze from real-world images is challenging due to unpredictable weather conditions, resulting in the misalignment of hazy and clear image pairs. In this paper, we propose an innovative dehazing framework that operates under non-aligned supervision. This framework is grounded in the atmospheric scattering model, and consists of three interconnected networks: dehazing, airlight, and transmission networks. In particular, we explore a non-alignment scenario that a clear reference image, unaligned with the input hazy image, is utilized to supervise the dehazing network. To implement this, we present a multi-scale reference loss that compares the feature representations between the referred image and the dehazed output. Our scenario makes it easier to collect hazy/clear image pairs in real-world environments, even under conditions of misalignment and shift views. To showcase the effectiveness of our scenario, we have collected a new hazy dataset including 415 image pairs captured by mobile Phone in both rural and urban areas, called "Phone-Hazy". Furthermore, we introduce a self-attention network based on mean and variance for modeling real infinite airlight, using the dark channel prior as positional guidance. Experimental results demonstrate the superior performance of our framework over existing state-of-the-art techniques in the real-world image dehazing task. © 1991-2012 IEEE.

关键词： Smartphones

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An efficient solution to the relative pose estimation with a common direction

An efficient solution to the relative pose estimation with a...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yaqing Ding Jian Yang Hui Kong Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

In this paper, we propose an efficient solution to the calibrated camera motion estimation with a common direction. This case is relevant to smart phones, tablets, and other camera-IMU (Inertial measurement unit) systems, which have accelerometers to measure the gravity direction. We can align one of the axes of the camera with this common direction so that the relative rotation between the views reduces to only 1DOF (degree of freedom). This allows us to use only three point correspondences for relative pose estimation. Unlike previous work, we derive new constraints on the simplified essential matrix using an elimination strategy based on Gröbner basis. In this case, computing the coefficients of these constraints require less computation and we only need to solve a polynomial eigenvalue problem. We show detailed analyses and comparisons against the existing 3-point algorithms, with satisfactory results obtained.

关键词： Cameras Eigenvalues and eigenfunctions Gravity Pose estimation Transmission line matrix methods Symmetric matrices Motion estimation

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Texture and Wavelet based PCANet(TW-PCANet) for Infants' Facial Expression Recognition

Texture and Wavelet based PCANet(TW-PCANet) for Infants' Fac...

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作者： Fang Shen Jing Liu Peng Wu Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University

The babyhood is a very important stage in the human growth *** learning the facial expression characteristics of infants is of great significance to nursing care for ***' faces are significantly different from adults' faces,like eyebrows,eyes,nose,cheeks,skin texture,***,the facial expression recognition models trained on adults' facial expression database cannot be directly applied to infants9 facial expression *** are dozens of facial expression databases for adults' facial expression analysis,but there is no database composed by various infants' facial *** we first collect candid images of infants' facial expression,which includes six obvious expressions of infants,namely crying,curious,happy,sleeping,wronged,*** the texture and wavelet based PCANet(TW-PCANet) is proposed to fully extract the features of infants' facial *** proposed method first handles original input color images by local binary patterns(LBP) and Gabor wavelet,then the processed images are put into the PCANet to get the final *** experimental results show that the proposed method can achieve higher recognition rate obtained by PCANet,LBP and Gabor wavelet.

关键词： Infants' facial expression recognition PCANet Local binary patterns Gabor wavelet

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A robust low-pass filtering graph diffusion clustering framework for hyperspectral images

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Knowledge-Based Systems 2025年 324卷

作者： Aitao Yang Min Li Yao Ding Yaoming Cai Jie Feng Yujie He Yuanchao Su The Xi’an Research Institute of High Technology Xi’an 710025 China The School of Information Engineering Zhongnan University of Economics and Law Wuhan 430073 China The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi’an 710071 China The Department of Remote Sensing College of Geomatics Xi’an University of Science and Technology Xi’an 710054 China

Hyperspectral image (HSI) clustering faces significant challenges due to spectral heterogeneity and complex spatial relationships. Although graph clustering methods enhance spatial information utilization through flexible topological modeling, their isotropic propagation mechanisms risk feature over-smoothing, and multi-layer parameter stacking leads to prohibitive computational costs. To address these limitations, we propose a Robust Low-pass Filtered Graph Diffusion Clustering framework (LFGD) for HSI featuring anisotropic dynamic diffusion and parameter-sharing architecture. Specifically, we design a Cross-Attention Graph Diffusion module (CAGD) that employs a multi-view cross-attention mechanism to integrate node features with high-order structural information into latent representations, while the graph diffusion module enables anisotropic feature propagation with dynamic topology optimization. To extract discriminative spectral features, the Discriminative Spectral Feature Extraction module (DSFE) applies a low-pass graph Laplacian filter to suppress noise interference, followed by a spectral dissimilarity matrix to amplify discriminative characteristics. We further introduce a graph self-supervised loss function to learn more robust feature representations. To our knowledge, this is the first work on hyperspectral image clustering using the graph diffusion network. Extensive experiments strongly demonstrate the effectiveness and robustness of LFGD on three public datasets. The code will be released at https://***/ahappyyang/LFGD .

关键词：

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ADVERSARIAL HASH-CODE LEARNING FOR REMOTE SENSING image RETRIEVAL

ADVERSARIAL HASH-CODE LEARNING FOR REMOTE SENSING IMAGE RETR...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Chao Liu Jingjing Ma Xu Tang Xiangrong Zhang Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Joint International Research Laboratory of Intelligent Perception and Computation

Hashing, a useful solution for approximate nearest neighbor (ANN) search, is popular for large-scale image retrieval. In this paper, we presents a deep supervised hashing model for remote sensing image retrieval (RSIR) in the framework of generative adversarial networks (GAN), named GAN-assist Hashing (GAAH). First, to learn the compact and useful hash codes from the images, we define a novel loss function for the generator. The loss function mainly consists of classification, similarity, and bits entropy terms. The classification term makes the hash code is discriminative, the similarity term constrains the binary code is similarity preserving, and the bits entropy term assures the learned code is low-error in the quantization. Second, we construct the unique "true" matrix with the uniform distribution as the input of discriminator to limit the leaned hash codes are bit balanced. The final hash code is learned by a minimax optimization. The positive experimental results on a ground-truth remote sensing image archive validate the usefulness of our GAAH model. Compare with the popular deep hashing methods, our GAAH achieves improved performance.

关键词： Quantization (signal) Training image retrieval Generative adversarial networks Remote sensing Generators Binary codes

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