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Improved Kernel Correlation Filter Tracking with Gaussian scale space

Improved Kernel Correlation Filter Tracking with Gaussian sc...

International Symposium on Optoelectronic Technology and Application 2016(OTA 2016)——Conf.12 Infrared Technology and Application

作者： Shukun Tan Yunpeng Liu Yicui Li Shenyang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Science The Key Lab of Image Understanding and Computer Vision

Visual tracking is one of the most challenging tasks in the field of computer vision and is related to a wide range of applications like surveillance and robotics. Tracking-by-detection methods are wi

ISBN: (纸本)9781510607729

关键词： visual tracking kernel correlation filter Gaussian scale space bilinear interpolation Mean Absolute Difference visual tracking bilinear interpolation images absolute difference imagery (Psychotherapy) Gauss Scale formation scale parameter Targets image Seeds jigs(templates) scales Scale

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Fical: Focal inter-class angular loss for image classification 34

Fical: Focal inter-class angular loss for image classificati...

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34th IEEE International Conference on Visual Communications and image Processing, VCIP 2019

作者： Wei, Xinran Chang, Dongliang Xie, Jiyang Zheng, Yixiao Gong, Chen Zhang, Chuang Ma, Zhanyu Beijing University of Posts and Telecommunications Pattern Recognition and Intelligent Systems Lab Beijing China Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education PCA Lab China Jiangsu Key Lab of Image and Video Understanding for Social Security China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781728137230

Convolutional Neural Networks (CNNs) have been successfully applied in various image analysis tasks and gradually become one of the most powerful machine learning approaches. In order to improve the capability of the model generalization and performance in image classification, a new trend is to learn more discriminative features via CNNs. The main contribution of this paper is to increase the angles between the categories to extract discriminative features and enlarge the inter-class variance. To this end, we propose a loss function named focal inter-class angular loss (FICAL) which introduces the confusion rate-weighted cosine distance as the similarity measurement between categories. This measurement is dynamically evaluated during each iteration to adapt the model. Compared with other loss functions, experimental results demonstrate that the proposed FICAL achieved best performance among the referred loss functions on two image classificaton datasets. © 2019 IEEE.

关键词： image classification

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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2023 IEEE/CVF Conference on computer vision and Pattern Recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 IEEE.

关键词： Multilayer neural networks

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94 GHz Three-Dimensional Imaging Radar for Environmental Detection 8

94 GHz Three-Dimensional Imaging Radar for Environmental Det...

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8th Asia-Pacific Conference on Antennas and Propagation, APCAP 2019

作者： Wu, HongMing Qi, Feng Wang, Jinkuan Northeastern University College of Information Science and Engineering Shenyang110819 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

ISBN: (纸本)9781665400541

At present, With the development of technology, the high resolution of millimeter wave (terahertz) can be applied to environmental sensing imaging. In this paper, Using the frequency-modulated continuous-wave (FMCW) radar technique with a 1GHz bandwidth, we show experimentally that a high-resolution real time imaging radar operating at 93.5-94.5GHz is capable of detecting different car models at standoff ranges of 12 m. We also achieved 3D high-resolution imaging of the building. at 140 meters. © 2019 IEEE.

关键词： Millimeter waves

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Prototype-based classifier learning for long-tailed visual recognition

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Science China(Information Sciences) 2022年第6期65卷 62-76页

作者： Xiu-Shen WEI Shu-Lin XU Hao CHEN Liang XIAO Yuxin PENG School of Computer Science and Engineering Nanjing University of Science and Technology State Key Laboratory of Integrated Services Networks Xidian University 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 Wangxuan Institute of Computer Technology Peking University

In this paper, we tackle the long-tailed visual recognition problem from the categorical prototype perspective by proposing a prototype-based classifier learning(PCL) method. Specifically, thanks to the generalization ability and robustness, categorical prototypes reveal their advantages of representing the category semantics. Coupled with their class-balance characteristic, categorical prototypes also show potential for handling data imbalance. In our PCL, we propose to generate the categorical classifiers based on the prototypes by performing a learnable mapping function. To further alleviate the impact of imbalance on classifier generation, two kinds of classifier calibration approaches are designed from both prototype-level and example-level aspects. Extensive experiments on five benchmark datasets, including the large-scale iNaturalist, Places-LT,and imageNet-LT, justify that the proposed PCL can outperform state-of-the-arts. Furthermore, validation experiments can demonstrate the effectiveness of tailored designs in PCL for long-tailed problems.

关键词： long-tailed distribution categorical prototype classifier generation classifier calibration class imbalance

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image saliency detection via multi-feature and manifold-space ranking 2021

Image saliency detection via multi-feature and manifold-spac...

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3rd Asia Pacific Information Technology Conference, APIT 2021

作者： Li, Xiaoli Zhao, Huaici Liu, Yunpeng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China The Key Lab of Image Understanding and Computer Vision Shenyang110016 China Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9781450388108

In this paper, we propose an image saliency detection method by using multi-feature and manifold-space ranking. Basically, the proposed method extracts the color-histogram feature to obtain the fine information of the image, and the color-mean feature to obtain the coarse information respectively. To further improve the detection accuracy of the feature correlation between different image units, a manifold-space ranking method is used to calculate saliency values of image units to construct a saliency map on each feature-space. Specifically, we fuse the two saliency maps to obtain the final saliency map. Extensive experiments demonstrate that the proposed method not only outperforms the other methods, but also improves the accuracy and robustness of the saliency detection. © 2021 ACM.

关键词： Color

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Multiple functional connectivity networks fusion for schizophrenia diagnosis

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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 novel clinical artificial intelligence model for disease detection via retinal imaging

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The Innovation 2024年第2期5卷 11-12页

作者： Yidian Fu Liang Ma Sheng Wan Shengfang Ge Zhi Yang Department of Ophthalmology Ninth People’s HospitalShanghai JiaoTong University School of MedicineShanghai 200011China Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology Shanghai 200011China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of EducationNanjing 210000China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing 210000China

Artificial intelligence(AI)is an emerging field in which computerized systems are used to carry out complex tasks in place of *** AI algorithms have been developed for disease diagnosis and prediction and treatment recommendation across various clinical data types,e.g.,chest X-rays,electrocardiograms,and other radiological images.1 In ophthalmology,particularly,great progress has been made in AI systems over the past *** fundus photography(CFP)and optical coherence tomography(OCT),which are readily available in routine clinical practice,are both mainstream and useful retinal imaging modalities in *** September 2023,the 2023 Lasker-Debakey Clinical Medical Research Award was awarded to three scientists for their work on OCT for accurate retinal disease detection.

关键词： clinical routine diagnosis

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Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition

arXiv

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arXiv 2022年

作者： Truong, Thanh-Dat Duong, Chi Nhan Le, Ngan Savvides, Marios Luu, Khoa The Computer Vision and Image Understanding Lab University of Arkansas United States The Department of Computer Science and Software Engineering Concordia University Canada Carnegie Mellon University United States

In this work, we investigate the problem of face reconstruction given a facial feature representation extracted from a blackbox face recognition engine. Indeed, it is a very challenging problem in practice due to the limitations of abstracted information from the engine. We, therefore, introduce a new method named Attention-based Bijective Generative Adversarial Networks in a Distillation framework (DAB-GAN) to synthesize the faces of a subject given his/her extracted face recognition features. Given any unconstrained unseen facial features of a subject, the DAB-GAN can reconstruct his/her facial images in high definition. The DAB-GAN method includes a novel attention-based generative structure with the newly defined Bijective Metrics Learning approach. The framework starts by introducing a bijective metric so that the distance measurement and metric learning process can be directly adopted in the image domain for an image reconstruction task. The information from the blackbox face recognition engine will be optimally exploited using the global distillation process. Then an attention-based generator is presented for a highly robust generator to synthesize realistic faces with ID preservation. We have evaluated our method on the challenging face recognition databases, i.e., CelebA, LFW, CFP-FP, CP-LFW, AgeDB, CA-LFW, and consistently achieved state-of-the-art results. The advancement of DAB-GAN is also proven in both image realism and ID preservation properties. Copyright © 2022, The Authors. All rights reserved.

关键词： Face recognition

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A bi-level formulation for label noise learning with spectral cluster discovery 29

A bi-level formulation for label noise learning with spectra...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Luo, Yijing Han, Bo Gong, Chen PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Lab of Image Video Understanding for Social Security China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9780999241165

Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed "Spectral Cluster Discovery" (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we use the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Iterative methods

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