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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chenlu Wei Zhiyu Jiang Yuan Yuan School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi'an Shaanxi P. R. China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Hyperspectral target detection has been widely studied in the field of remote sensing. However, background dictionary building issue and the correlation analysis of target and background dictionary issue have not been well studied. To tackle these issues, a Weighted Hierarchical Sparse Representation for hyperspectral target detection is proposed. The main contributions of this work are listed as follows. 1) Considering the insufficient representation of the traditional background dictionary building by dual concentric window structure, a hierarchical background dictionary is built considering the local and global spectral information simultaneously. 2) To reduce the impureness impact of background dictionary, target scores from target dictionary and background dictionary are weighted considered according to the dictionary quality. Three hyperspectral target detection data sets are utilized to verify the effectiveness of the proposed method. And the experimental results show a better performance when compared with the state-of-the-arts.

关键词： Dictionaries Hyperspectral imaging Object detection Training Buildings Image reconstruction Detectors

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A Novel Unsupervised Change Detection Approach Based On Spectral Transformation For Multispectral Images

A Novel Unsupervised Change Detection Approach Based On Spec...

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IEEE International Conference on Image Processing

作者： Yuelin Zhang Ganchao Liu Yuan Yuan School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi’an Shaanxi P. R. China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Change detection (CD) for multispectral remote sensing images is an important approach to observe the changes of the earth. However, the same object usually has different spectra in multi-temporal images, which is one of the biggest challenges for CD. To overcome this problem, a novel unsupervised CD approach based on spectral transformation and joint spectral-spatial feature learning (STCD) is proposed for multispectral images in this paper. By exploring the relationship between imaging environment and the object spectra, the spectral transformation is used to suppress the phenomenon of “same object with different spectra”. Besides, a detection network with joint spectral-spatial feature learning is designed to extract the spectral-spatial features simultaneously to make the CD algorithm more robust. Both theoretical analyses and experiment results proved that the proposed STCD method is superior to the state-of-the-art unsupervised methods on multispectral images CD.

关键词： Feature extraction Principal component analysis Imaging Remote sensing Robustness Transforms Matrix decomposition

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Pixel-Level Self-Paced learning For Super-Resolution

Pixel-Level Self-Paced Learning For Super-Resolution

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Wei Lin Junyu Gao Qi Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi’an Shaanxi P. R. China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Recently, lots of deep networks are proposed to improve the quality of predicted super-resolution (SR) images, due to its widespread use in several image-based fields. However, with these networks being constructed deeper and deeper, they also cost much longer time for training, which may guide the learners to local optimization. To tackle this problem, this paper designs a training strategy named Pixel-level Self-Paced learning (PSPL) to accelerate the convergence velocity of SISR models. PSPL imitating self-paced learning gives each pixel in the predicted SR image and its corresponding pixel in ground truth an attention weight, to guide the model to a better region in parameter space. Extensive experiments proved that PSPL could speed up the training of SISR models, and prompt several existing models to obtain new better results. Furthermore, the source code is available at .

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Hyperspectral and Multispectral Image Fusion Using Non-Convex Relaxation Low Rank and Total Variation Regularization

Hyperspectral and Multispectral Image Fusion Using Non-Conve...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yue Yuan Qi Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi'an Shaanxi P. R. China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Hyperspectral (HS) and multispectral (MS) image fusion is an important task to construct an HS image with high spatial and spectral resolutions. In this paper, we present a novel HS and MS fusion method using non-convex low rank tensor approximation and total variation regularization. In specific, the Laplace based low-rank model is formed to exploit spatial-spectral correlation and nonlocal similarity of the HS image, and the second-order total variation is used to describe the local smoothness structure in the spatial domain and adjacent bands. Also, an effective optimization algorithm is designed for the proposed model. In the experiments, we demonstrate the superiority of the proposed method compared to several state-of-the-art approaches.

关键词： Spatial resolution Hyperspectral imaging Image fusion Minimization Correlation TV Optimization

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Bio-inspired representation learning for visual attention prediction

arXiv

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arXiv 2021年

作者： Yuan, Yuan Ning, Hailong Lu, Xiaoqiang The Center for OPTical IMagery Analysis and Learning School of the Computer Science Northwestern Polytechnical University Xi’anShaanxi710072 China The Key Laboratory of Spectral Imaging Technology CAS Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi’anShaanxi710119 China The University of Chinese Academy of Sciences Beijing100049 China

Visual Attention Prediction (VAP) is a significant and imperative issue in the field of computer vision. Most of existing VAP methods are based on deep learning. However, they do not fully take advantage of the low-level contrast features while generating the visual attention map. In this paper, a novel VAP method is proposed to generate visual attention map via bio-inspired representation learning. The bio-inspired representation learning combines both low-level contrast and high-level semantic features simultaneously, which are developed by the fact that human eye is sensitive to the patches with high contrast and objects with high semantics. The proposed method is composed of three main steps: 1) feature extraction, 2) bio-inspired representation learning and 3) visual attention map generation. Firstly, the high-level semantic feature is extracted from the refined VGG16, while the low-level contrast feature is extracted by the proposed contrast feature extraction block in a deep network. Secondly, during bio-inspired representation learning, both the extracted low-level contrast and high-level semantic features are combined by the designed densely connected block, which is proposed to concatenate various features scale by scale. Finally, the weighted-fusion layer is exploited to generate the ultimate visual attention map based on the obtained representations after bio-inspired representation learning. Extensive experiments are performed to demonstrate the effectiveness of the proposed method. © 2021, CC BY.

关键词： Forecasting

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Automatic ultrasound scanning system based on robotic arm

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science China(Information sciences) 2019年第5期62卷 35-37页

作者： Qinghua HUANG Jiulong LAN Xuelong LI School of Mechanical Engineering and the Center for Optical Imagery Analysis and LearningNorthwestern Polytechnical University School of Electronic and Information Engineering South China University of Technology School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University

Dear editor,As an indispensable diagnosis-aid technologies, ultrasound (US) examination gains more and more attention in recent years [1]. US could real-time display the 2D images of the region of interest in a way of... 详细信息

关键词： school USA Automatic ultrasound scanning system based on robotic arm

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Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers

Predicting Cognitive Declines Using Longitudinally Enriched ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Lyujian Lu Hua Wang Saad Elbeleidy Feiping Nie Department of Computer Science Colorado School of Mines Golden Colorado U.S.A. School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi'an P. R. China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

With rapid progress in high-throughput genotyping and neuroimaging, researches of complex brain disorders, such as Alzheimer's Disease (AD), have gained significant attention in recent years. Many prediction models have been studied to relate neuroimaging measures to cognitive status over the progressions when these disease develops. Missing data is one of the biggest challenge in accurate cognitive score prediction of subjects in longitudinal neuroimaging studies. To tackle this problem, in this paper we propose a novel formulation to learn an enriched representation for imaging biomarkers that can simultaneously capture both the information conveyed by baseline neuroimaging records and that by progressive variations of varied counts of available follow-up records over time. While the numbers of the brain scans of the participants vary, the learned biomarker representation for every participant is a fixed-length vector, which enable us to use traditional learning models to study AD developments. Our new objective is formulated to maximize the ratio of the summations of a number of ℓ 1 -norm distances for improved robustness, which, though, is difficult to efficiently solve in general. Thus we derive a new efficient iterative solution algorithm and rigorously prove its convergence. We have performed extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. A performance gain has been achieved to predict four different cognitive scores, when we compare the original baseline representations against the learned representations with enrichments. These promising empirical results have demonstrated improved performances of our new method that validate its effectiveness.

关键词： Neuroimaging Biomarkers Dementia Brain modeling Biomedical imaging

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Listen to the Image

Listen to the Image

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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Di Hu Dong Wang Xuelong Li Feiping Nie Qi Wang School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University

ISBN: (纸本)9781728132945

Visual-to-auditory sensory substitution devices can assist the blind in sensing the visual environment by translating the visual information into a sound pattern. To improve the translation quality, the task performances of the blind are usually employed to evaluate different encoding schemes. In contrast to the toilsome human-based assessment, we argue that machine model can be also developed for evaluation, and more efficient. To this end, we firstly propose two distinct cross-modal perception model w.r.t. the late-blind and congenitally-blind cases, which aim to generate concrete visual contents based on the translated sound. To validate the functionality of proposed models, two novel optimization strategies w.r.t. the primary encoding scheme are presented. Further, we conduct sets of human-based experiments to evaluate and compare them with the conducted machine-based assessments in the cross-modal generation task. Their highly consistent results w.r.t. different encoding schemes indicate that using machine model to accelerate optimization evaluation and reduce experimental cost is feasible to some extent, which could dramatically promote the upgrading of encoding scheme then help the blind to improve their visual perception ability.

关键词： Coding systems machine model task performance Image images reduced mass Blind Listening Cross-modal sound pattern

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learning from Synthetic Data for Crowd Counting in the Wild

Learning from Synthetic Data for Crowd Counting in the Wild

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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Qi Wang Junyu Gao Wei Lin Yuan Yuan School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University

ISBN: (纸本)9781728132945

Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security). It is a difficult task in the wild: changeable environment, large-range number of people cause the current methods can not work well. In addition, due to the scarce data, many methods suffer from over-fitting to a different extent. To remedy the above two problems, firstly, we develop a data collector and labeler, which can generate the synthetic crowd scenes and simultaneously annotate them without any manpower. Based on it, we build a large-scale, diverse synthetic dataset. Secondly, we propose two schemes that exploit the synthetic data to boost the performance of crowd counting in the wild: 1) pretrain a crowd counter on the synthetic data, then finetune it using the real data, which significantly prompts the model's performance on real data;2) propose a crowd counting method via domain adaptation, which can free humans from heavy data annotations. Extensive experiments show that the first method achieves the state-of-the-art performance on four real datasets, and the second outperforms our baselines. The dataset and source code are available at .

关键词： synthetic data learning video monitoring Persons observational data Manpower Labor Force Dataset Source code Data Annotation

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Deep Multimodal Clustering for Unsupervised Audiovisual learning

Deep Multimodal Clustering for Unsupervised Audiovisual Lear...

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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Di Hu Feiping Nie Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University

ISBN: (纸本)9781728132945

The seen birds twitter, the running cars accompany with noise, etc. These naturally audiovisual correspondences provide the possibilities to explore and understand the outside world. However, the mixed multiple objects and sounds make it intractable to perform efficient matching in the unconstrained environment. To settle this problem, we propose to adequately excavate audio and visual components and perform elaborate correspondence learning among them. Concretely, a novel unsupervised audiovisual learning model is proposed, named as Deep Multimodal Clustering (DMC), that synchronously performs sets of clustering with multimodal vectors of convolutional maps in different shared spaces for capturing multiple audiovisual correspondences. And such integrated multimodal clustering network can be effectively trained with max-margin loss in the end-to-end fashion. Amounts of experiments in feature evaluation and audiovisual tasks are performed. The results demonstrate that DMC can learn effective unimodal representation, with which the classifier can even outperform human performance. Further, DMC shows noticeable performance in sound localization, multisource detection, and audiovisual understanding.

关键词： Audiovisuals end-to-end HUMAN PERFORMANCE Bulk molding compounds Cluster analysis Audio SOUND Classifiers learning Probability

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