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Context prior for scene segmentation

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arXiv 2020年

作者： Yu, Changqian Wang, Jingbo Gao, Changxin Yu, Gang Shen, Chunhua Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology University of Adelaide Australia Chinese University of Hong Kong Tencent

Recent works have widely explored the contextual dependencies to achieve more accurate segmentation results. However, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. In this work, we directly supervise the feature aggregation to distinguish the intra-class and inter-class context clearly. Specifically, we develop a Context Prior with the supervision of the Affinity Loss. Given an input image and corresponding ground truth, Affinity Loss constructs an ideal affinity map to supervise the learning of Context Prior. The learned Context Prior extracts the pixels belonging to the same category, while the reversed prior focuses on the pixels of different classes. Embedded into a conventional deep CNN, the proposed Context Prior Layer can selectively capture the intra-class and inter-class contextual dependencies, leading to robust feature representation. To validate the effectiveness, we design an effective Context Prior Network (CPNet). Extensive quantitative and qualitative evaluations demonstrate that the proposed model performs favorably against state-of-the-art semantic segmentation approaches. More specifically, our algorithm achieves 46.3% mIoU on ADE20K, 53.9% mIoU on PASCAL-Context, and 81.3% mIoU on Cityscapes. Code is available at https://***/ContextPrior. Copyright © 2020, The Authors. All rights reserved.

关键词： Pixels

GTNet: Generative transfer network for zero-shot object detection

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arXiv 2020年

作者： Zhao, Shizhen Gao, Changxin Shao, Yuanjie Li, Lerenhan Yu, Changqian Ji, Zhong Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electrical and Information Engineering Tianjin University

We propose a Generative Transfer Network (GTNet) for zero-shot object detection (ZSD). GTNet consists of an Object Detection Module and a Knowledge Transfer Module. The Object Detection Module can learn large-scale seen domain knowledge. The Knowledge Transfer Module leverages a feature synthesizer to generate unseen class features, which are applied to train a new classification layer for the Object Detection Module. In order to synthesize features for each unseen class with both the intra-class variance and the IoU variance, we design an IoU-Aware Generative Adversarial Network (IoUGAN) as the feature synthesizer, which can be easily integrated into GTNet. Specifically, IoUGAN consists of three unit models: Class Feature Generating Unit (CFU), Foreground Feature Generating Unit (FFU), and Background Feature Generating Unit (BFU). CFU generates unseen features with the intra-class variance conditioned on the class semantic embeddings. FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively. We evaluate our method on three public datasets and the results demonstrate that our method performs favorably against the state-of-the-art ZSD approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

Relevant Region Prediction for Crowd Counting

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arXiv 2020年

作者： Chen, Xinya Bin, Yanrui Gao, Changxin Sang, Nong Tang, Hao Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Department of Information Engineering and Computer Science University of Trento

Crowd counting is a concerned and challenging task in computer vision. Existing density map based methods excessively focus on the individuals’ localization which harms the crowd counting performance in highly congested scenes. In addition, the dependency between the regions of different density is also ignored. In this paper, we propose Relevant Region Prediction (RRP) for crowd counting, which consists of the Count Map and the Region Relation-Aware Module (RRAM). Each pixel in the count map represents the number of heads falling into the corresponding local area in the input image, which discards the detailed spatial information and forces the network pay more attention to counting rather than localizing individuals. Based on the Graph Convolutional Network (GCN), Region Relation-Aware Module is proposed to capture and exploit the important region dependency. The module builds a fully connected directed graph between the regions of different density where each node (region) is represented by weighted global pooled feature, and GCN is learned to map this region graph to a set of relation-aware regions representations. Experimental results on three datasets show that our method obviously outperforms other existing state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

Do not disturb me: Person re-identification under the interference of other pedestrians

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arXiv 2020年

作者： Zhao, Shizhen Gao, Changxin Zhang, Jun Cheng, Hao Han, Chuchu Jiang, Xinyang Guo, Xiaowei Zheng, Wei-Shi Sang, Nong Sun, Xing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Tencent Youtu Lab Sun Yat-sen University China

In the conventional person Re-ID setting, it is widely assumed that cropped person images are for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods. Our project is available at https://***/X-BrainLab/PI-ReID. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

Calibration of the internal and external parameters of wheeled robot mobile chasses and inertial measurement units based on nonlinear optimization

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arXiv 2020年

作者： Peng, Gang Lu, Zezao Tan, Zejie He, Dingxin Li, Xinde Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan School of Automation South East University Nanjing

Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit (IMU) to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an IMU arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal reference calibration methods available for IMUs, external reference calibration methods between the IMU and the chassis of a mobile robot are lacking. This study addresses this issue by proposing a novel chassis-IMU internal and external parameter calibration algorithm based on nonlinear optimization, which is designed for robots equipped with cameras, IMUs, and wheel speed odometers, and functions under the premise of accurate calibrations for the internal parameters of the IMU and the internal and external parameters of the camera. All of the internal and external reference calibrations are conducted using the robot's existing equipment without the need for additional calibration aids. The feasibility of the method is verified by its application to a Mecanum wheel omnidirectional mobile platform as an example, as well as suitable for other type chassis of mobile robots. The proposed calibration method is thereby demonstrated to guarantee the accuracy of robot pose estimation. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

High Confidence Tracking with Offline Historical Learning and Online Correlation Filter Updating

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High Confidence Tracking with Offline Historical Learning an...

作者： Shou-dong HAN Hong-wei WANG Xin-xin XIA Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Research Institute of Huazhong University of Science and Technology in Shenzhen

Target tracking is one of the most challenging tasks in computer vision. In this paper, the high confidence tracking(HCT) algorithm is proposed by combining the offline historical learning network with online correlation filter updating model. First, the weighted historical targets are introduced into the offline learning network, which solves the problem of target loss caused by inaccurate tracking of the previous frame. Second, the target's confidence detection mechanism is proposed, and added to the correlation filter tracking algorithm, so that the model drift is avoided. Finally, we form a new high confidence tracking algorithm with offline learning. Compared with the state-of-the-art tracking algorithm, our algorithm performs outstandingly on benchmark OTB13 and OTB15, while ensuring real-time performance.

关键词： High confidence Target tracking Offline learning Online updating

Modelling brain based on canonical ensemble with functional MRI: A thermodynamic exploration on neural system

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arXiv 2021年

作者： Li, Wei Zhou, Chenxi Yang, Bin Fan, Wenliang Chen, Xi School of Artificial Intelligence and Automation Huazhong University of Science and Technology WuhanHubei430074 China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China WuhanHubei430074 China Department of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology WuhanHubei430074 China

Objective. Modelling is an important way to study the working mechanism of brain. While the characterization and understanding of brain are still inadequate. This study tried to build a model of brain from the perspective of thermodynamics at system level, which brought a new thinking to brain modelling. Approach. Regarding brain regions as systems, voxels as particles, and intensity of signals as energy of particles, the thermodynamic model of brain was built based on canonical ensemble theory. Two pairs of activated regions and two pairs of inactivated brain regions were selected for comparison in this study, and the analysis on thermodynamic properties based on the model proposed were performed. In addition, the thermodynamic properties were also extracted as input features for the detection of Alzheimer's disease. Main results. The experiment results verified the assumption that the brain also follows the thermodynamic laws. It demonstrated the feasibility and rationality of brain thermodynamic modelling method proposed, indicating that thermodynamic parameters could be applied to describe the state of neural system. Meanwhile, the brain thermodynamic model achieved much better accuracy in detection of Alzheimer's disease, suggesting the potential application of thermodynamic model in auxiliary diagnosis. Significance. (1) Instead of applying some thermodynamic parameters to analyze neural system, a brain model at system level was proposed from perspective of thermodynamics for the first time in this study. (2) The study discovered that the neural system also follows the laws of thermodynamics, which leads to increased internal energy, increased free energy and decreased entropy when system is activated. (3) The detection of neural disease was demonstrated to be benefit from thermodynamic model, implying the immense potential of thermodynamics in auxiliary *** Codes 80-10, 82-10, 92B99 Copyright © 2021, The Authors. All rights reserved.

关键词： Brain

Probabilistic Robust Parity Relation based Fault Detection Using Biased Minimax Probability Machine ⁎

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IFAC-PapersOnLine 2020年第2期53卷 646-651页

作者： Yujia Ma Yiming Wan Maiying Zhong Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China College of Electrical Engineering and Automation Shangdong University of Science and Technology Qingdao 266590 China

This paper proposes a probabilistic robust parity relation based approach to fault detection of stochastic linear systems. Instead of assuming exact knowledge of disturbance distribution, the uncertainty of distribution information is taken into account by considering an ambiguity set of disturbance distributions. The biased minimax probability machine scheme is exploited to formulate an integrated design of the parity vector/matrix and the detection threshold. It maximizes the worst-case fault detection rate (FDR) with respect to selected reference faults, while ensuring a predefined worst-case false alarm rate. Firstly, a scalar residual design is derived in an analytical form. The analysis of its FDR in the presence of an arbitrary fault shows its limitation due to using a single reference fault. This issue is further addressed by proposing a vector residual design with a systematic method to select multiple reference faults. The efficacy of the proposed approach is illustrated by a simulation example.

关键词： Fault detection parity relation biased minimax probability machine false alarm rate fault detection rate

Bioimage-based protein subcellular location prediction： a comprehensive review

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Frontiers of Computer Science 2018年第1期12卷 26-39页

作者： Ying-Ying XU Li-Xiu YAO Hong-Bin SHEN Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasingly popular in automatically analyzing the protein subcellular location pat- terns. Compared with the widely used protein 1D amino acid sequence data, the images of protein distribution are more intuitive and interpretable, making the images a better choice at many applications for revealing the dynamic char- acteristics of proteins, such as detecting protein translocation and quantification of proteins. In this paper, we systemati- cally reviewed the recent progresses in the field of automated image-based protein subcellular location prediction, and clas- sified them into four categories including growing of bioim- age databases, description of subcellular location distribution patterns, classification methods, and applications of the pre- diction systems. Besides, we also discussed some potential directions in this field.

关键词： bioimage informatics protein subcellular loca-tion prediction global and local features multi-location pro-tein recognition