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arXiv 2020年

作者： Yang, Jiaqi Huang, Zhiqiang Quan, Siwen Zhang, Qian Zhang, Yanning Cao, Zhiguo National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University Xi’an710129 China School of Electronic and Control Engineering Chang’an University Xi’an710064 China School of Resources and Environment Hubei University Wuhan430062 China National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration. Estimating six-degree-of-freedom (6-DoF) pose from feature correspondences remains a popular approach to 3D rigid registration, where random sample consensus (RANSAC) is a de-facto choice to this problem. However, existing metrics for RANSAC hypotheses are either time-consuming or sensitive to common nuisances, parameter variations, and different application scenarios, resulting in performance deterioration in overall registration accuracy and speed. We alleviate this problem by first analyzing the contributions of inliers and outliers, and then proposing several efficient and robust metrics with different designing motivations for RANSAC hypotheses. Comparative experiments on four standard datasets with different nuisances and application scenarios verify that the proposed metrics can significantly improve the registration performance and are more robust than several state-of-the-art competitors, making them good gifts to practical applications. This work also draws an interesting conclusion, i.e., not all inliers are equal while all outliers should be equal, which may shed new light on this research problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Deterioration

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A performance evaluation of correspondence grouping methods for 3D rigid data matching

arXiv

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arXiv 2019年

作者： Yang, Jiaqi Xian, Ke Wang, Peng Zhang, Yanning School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710129 China National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision. While a number of correspondence selection methods have been proposed in recent years, their advantages and shortcomings remain unclear regarding different applications and perturbations. To fill this gap, this paper gives a comprehensive evaluation of nine state-of-the-art 3D correspondence grouping methods. A good correspondence grouping algorithm is expected to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall as well as facilitating accurate transformation estimation. Toward this rule, we deploy experiments on three benchmarks with different application contexts including shape retrieval, 3D object recognition, and point cloud registration together with various perturbations such as noise, point density variation, clutter, occlusion, partial overlap, different scales of initial correspondences, and different combinations of keypoint detectors and descriptors. The rich variety of application scenarios and nuisances result in different spatial distributions and inlier ratios of initial feature correspondences, thus enabling a thorough evaluation. Based on the outcomes, we give a summary of the traits, merits, and demerits of evaluated approaches and indicate some potential future research directions. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

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Learning Robust Facial Landmark Detection via Hierarchical Structured Ensemble

Learning Robust Facial Landmark Detection via Hierarchical S...

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International Conference on Computer Vision (ICCV)

作者： Xu Zou Sheng Zhong Luxin Yan Xiangyun Zhao Jiahuan Zhou Ying Wu College of Life Science and Technology Huazhong University of Science & Technology (HUST) Wuhan China National Key Laboratory of Science & Technology on Multi-Spectral Information Processing HUST Wuhan China School of Aritificial Intelligence and Automation HUST Wuhan China Department of Electrical and Computer Engineering Northwestern University IL USA

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Heatmap regression-based models have significantly advanced the progress of facial landmark detection. However, the lack of structural constraints always generates inaccurate heatmaps resulting in poor landmark detection performance. While hierarchical structure modeling methods have been proposed to tackle this issue, they all heavily rely on manually designed tree structures. The designed hierarchical structure is likely to be completely corrupted due to the missing or inaccurate prediction of landmarks. To the best of our knowledge, in the context of deep learning, no work before has investigated how to automatically model proper structures for facial landmarks, by discovering their inherent relations. In this paper, we propose a novel Hierarchical Structured Landmark Ensemble (HSLE) model for learning robust facial landmark detection, by using it as the structural constraints. Different from existing approaches of manually designing structures, our proposed HSLE model is constructed automatically via discovering the most robust patterns so HSLE has the ability to robustly depict both local and holistic landmark structures simultaneously. Our proposed HSLE can be readily plugged into any existing facial landmark detection baselines for further performance improvement. Extensive experimental results demonstrate our approach significantly outperforms the baseline by a large margin to achieve a state-of-the-art performance.

关键词： Robustness Heating systems Detectors Face Shape Mathematical model

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multi-view stereo with asymmetric checkerboard propagation and multi-hypothesis joint view selection

arXiv

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arXiv 2018年

作者： Xu, Qingshan Tao, Wenbing National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Automation Huazhong University of Science & Technology

In computer vision domain, how to fast and accurately perform multiview stereo (MVS) is still a challenging problem. In this paper we present a fast yet accurate method for 3D dense reconstruction, called AMHMVS, built on the PatchMatch based stereo algorithm. Different from the regular symmetric propagation scheme, our approach adopts an asymmetric checkerboard propagation strategy, which can adaptively make effective hypotheses expand further according to the confidence of current neighbor hypotheses. In order to aggregate visual information from multiple images better, we propose the multi-hypothesis joint view selection for each pixel, which leverages a cost matrix based on the multiple propagated hypotheses to robustly infer an appropriate aggregation subset parallel. Combined with the above two steps, our approach not only has the capacity of massively parallel computation, but also obtains high accuracy and completeness. Experiments on extensive datasets show that our method achieves more accurate and robust results, and runs faster than the competing methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Stereo image processing

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IoU-uniform R-CNN: breaking through the limitations of rpn

arXiv

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arXiv 2019年

作者： Zhu, Li Xie, Zihao Liu, Liman Tao, Bo Tao, Wenbing National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artifcial Intelligence and Automation Huazhong University of Science and Technology Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology WuhanHubei430074 China School of Biomedical Engineering South-Central University for Nationalities Wuhan430074 China

Region Proposal Network (RPN) is the cornerstone of two-stage object detectors, it generates a sparse set of object proposals and alleviates the extrem foreground-background class imbalance problem during training. However, we find that the potential of the detector has not been fully exploited due to the IoU distribution imbalance and inadequate quantity of the training samples generated by RPN. With the increasing intersection over union (IoU), the exponentially smaller numbers of positive samples would lead to the distribution skewed towards lower IoUs, which hinders the optimization of detector at high IoU levels. In this paper, to break through the limitations of RPN, we propose IoU-Uniform R-CNN, a simple but effective method that directly generates training samples with uniform IoU distribution for the regression branch as well as the IoU prediction branch. Besides, we improve the performance of IoU prediction branch by eliminating the feature offsets of RoIs at inference, which helps the NMS procedure by preserving accurately localized bounding box. Extensive experiments on the PASCAL VOC and MS COCO dataset show the effectiveness of our method, as well as its compatibility and adaptivity to many object detection architectures. The code is made publicly available at https://***/zl1994/IoU-Uniform-R-CNN. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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Towards real-time eyeblink detection in the wild: Dataset, theory and practices

arXiv

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arXiv 2019年

作者： Hu, Guilei Xiao, Yang Cao, Zhiguo Meng, Lubin Fang, Zhiwen Zhou, Joey Tianyi Yuan, Junsong National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University China School of Energy and Mechanicalelectronic Engineering Hunan University of Humanities Science and Technology China Institute of High Performance Computing A∗STAR Singapore Computer Science and Engineering Department of University at Buffalo State University of New York United States

Effective and real-time eyeblink detection is of widerange applications, such as deception detection, drive fatigue detection, face anti-spoofing. Despite previous efforts, most of existing focus on addressing the eyeblink detection problem under constrained indoor conditions with relative consistent subject and environment setup. Nevertheless, towards practical applications, eyeblink detection in the wild is highly preferred, and of greater challenges. In this paper, we shed the light to this research topic. A labelled eyeblink in the wild dataset (i.e., HUST-LEBW) of 673 eyeblink video samples (i.e., 381 positives, and 292 negatives) is first established. These samples are captured from the unconstrained movies, with the dramatic variation on face attribute, head pose, illumination condition, imaging configuration, etc. Then, we formulate eyeblink detection task as a binary spatial-temporal pattern recognition problem. After locating and tracking human eyes using SeetaFace engine and KCF (Kernelized Correlation Filters) tracker respectively, a modified LSTM model able to capture the multi-scale temporal information is proposed to verify eyeblink. A feature extraction approach that reveals the appearance and motion characteristics simultaneously is also proposed. The experiments on HUSTLEBW reveal the superiority and efficiency of our approach. The comparisons with the existing state-of-the-art methods validate the advantages of our manner for eyeblink detection in the wild. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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Notice of Removal: Inter-frame Correlation Based on Moving Vehicle Target Detection in Infrared Image Sequences

Notice of Removal: Inter-frame Correlation Based on Moving V...

引用

International Computers, Signals and Systems Conference (ICOMSSC)

作者： Jin Lin Yihua Tan Jinwen Tian National Key Laboratory of Science & Technology on Multi-Spectral Information Processing School of Automation Huazhong University of Science & Technology Wuhan China

Removed.

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SCALABLE multi-CONSISTENCY FEATURE MATCHING WITH NON-COOPERATIVE GAMES

SCALABLE MULTI-CONSISTENCY FEATURE MATCHING WITH NON-COOPERA...

引用

IEEE International Conference on Image processing

作者： Chen Zhao Jiaqi Yang Yang Xiao Zhiguo Cao National Key Laboratory of Science and Technology on Multi-spectral Information Processing Huazhong University of Science and Technology P. R. China

Correspondence selection aiming at seeking correct relationships between two images is a fundamental and critical task in computer vision. This paper attempts to select consistent correspondences in the context of dynamic scenarios where multiple matching consistencies are normally incorporated. To this end, we present a grid-based game-theoretic matching (Grid-GTM) method which is divided into three processes, i.e., grid matching, local games and enrichment. Specifically, grid matching translates the multi-consistency problem into several independent single-consistency problems to decrease difficulties of selection and boost the efficiency. Local games extended under the guidance of a novel payoff function guarantee that mismatches are effectively removed. Enrichment is added to recover correct matches neglected by local games. Crucially, our approach achieves the state-of-the-art performance compared with seven algorithms in comprehensive evaluations. In addition, we construct a dataset that involves multiple consistencies under three different scenes in this paper.

关键词： Games Robustness Task analysis Computer vision Feature extraction Position measurement Interference

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Rotation invariant point cloud analysis: Where local geometry meets global topology

arXiv

引用

arXiv 2019年

作者： Zhao, Chen Yang, Jiaqi Xiong, Xin Zhu, Angfan Cao, Zhiguo Li, Xin National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown26506 United States

Point cloud analysis is a fundamental task in 3D computer vision. Most previous works have conducted experiments on synthetic datasets with well-aligned data;while real-world point clouds are often not pre-aligned. How to achieve rotation invariance remains an open problem in point cloud analysis. To meet this challenge, we propose an approach toward achieving rotation-invariant (RI) representations by combining local geometry with global topology. In our local-global-representation (LGR)-Net, we have designed a two-branch network where one stream encodes local geometric RI features and the other encodes global topology-preserving RI features. Motivated by the observation that local geometry and global topology have different yet complementary RI responses in varying regions, two-branch RI features are fused by an innovative multi-layer perceptron (MLP) based attention module. To the best of our knowledge, this work is the first principled approach toward adaptively combining global and local information under the context of RI point cloud analysis. Extensive experiments have demonstrated that our LGR-Net achieves the state-of-the-art performance on various rotation-augmented versions of ModelNet40, ShapeNet, ScanObjectNN, and S3DIS. Copyright © 2019, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Inter-frame correlation based on moving vehicle target detection in infrared image sequences

Inter-frame correlation based on moving vehicle target detec...

引用

2017 International Conference on Computer technology, Electronics and Communication, ICCTEC 2017

作者： Lin, Jin Tan, Yihua Tian, Jinwen National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781538657843

Moving vehicle targets detection in infrared image sequences is playing a more and more important role in infrared search and track systems. This paper presents a novel method based on inter-frame correlation to detect moving vehicle target in infrared image sequences reliably. Firstly, for a single frame, the image respectively is sharpened and enhanced after image denoising, and then generating the preprocessed image. Secondly, the vehicle targets in infrared image sequences are detected by a saliency based target detection algorithm. For consecutive frames, features of motion between real vehicle targets and false ones are different, then the inter-frame correlation is operated to suppress the false alarm, making the vehicle targets detection more accurate. The experiments on the image sequences demonstrate that the proposed method has ensure detection effectiveness and robustness for vehicle detection in infrared image sequences under complex backgrounds, and it also improve the reliability. © 2017 IEEE.

关键词： Image denoising

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