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3D hand pose and shape estimation from monocular RGB via efficient 2D cues

Computational Visual Media 2024年第1期10卷 79-96页

作者： Fenghao Zhang Lin Zhao Shengling Li Wanjuan Su Liman Liu Wenbing Tao Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis&Treatment School of Biomedical EngineeringSouth Central Minzu UniversityWuhan430074China National Key Laboratory of Science and Technology of Multi-spectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan430074China

Estimating 3D hand shape from a single-view RGB image is important for many ***,the diversity of hand shapes and postures,depth ambiguity,and occlusion may result in pose errors and noisy hand *** full use of 2D cues such as 2D pose can effectively improve the quality of 3D human hand shape *** this paper,we use 2D joint heatmaps to obtain spatial details for robust pose *** also introduce a depth-independent 2D mesh to avoid depth ambiguity in mesh regression for efficient hand-image *** method has four cascaded stages:2D cue extraction,pose feature encoding,initial reconstruction,and reconstruction ***,we first encode the image to determine semantic features during 2D cue extraction;this is also used to predict hand joints and for ***,during the pose feature encoding stage,we use a hand joints encoder to learn spatial information from the joint ***,a coarse 3D hand mesh and 2D mesh are obtained in the initial reconstruction step;a mesh squeeze-and-excitation block is used to fuse different hand features to enhance perception of 3D hand ***,a global mesh refinement stage learns non-local relations between vertices of the hand mesh from the predicted 2D mesh,to predict an offset hand mesh to fine-tune the reconstruction *** and qualitative results on the FreiHAND benchmark dataset demonstrate that our approach achieves state-of-the-art performance.

关键词： hand 3D reconstruction deep learning image features 3D mesh

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Distribution Mining and Noise Correction for Facial Expression Recognition 5

Distribution Mining and Noise Correction for Facial Expressi...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Huang, Hailan Tan, Yihua Huazhong University of Science and Technology National Key Laboratory of Science & Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9798350373820

Due to the high annotation cost and inter-class ambiguity, many facial expression datasets only offer noisy one-hot labels. This creates two challenges for facial expression recognition (FER) as the one-hot labels only represent limited single semantics, and the noisy labels will mislead the model. Our study proposes a novel method to perform both label Distribution Mining and Noise Correction (DMNC) on the FER dataset. That is, while eliminating the disturbance of noisy samples, we start from the original one-hot labels and gradually refine them to distributions of expressions close to the real world. Specifically, DMNC uses the network forgetting property to select noisy samples. Then based on clean samples, an expression feature space is learned by contrastive learning. Finally, the labels of clean and noisy samples are refined or relabelled in a divide-and-conquer manner using the learned feature space. Experiments show that our method exhibits leading performance on both real-world FER benchmarks and synthetic noisy datasets. © 2024 IEEE.

关键词： Semantics

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Anti-interference object detection algorithm based on spatio-Temporal behavior perception 12

Anti-interference object detection algorithm based on spatio...

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SPIE 12th International Symposium on multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Chen, Guo Han, Peng Mei, Jianwei Zuo, Zhengrong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan Hubei China National Key Laboratory of Science and Technology on Multi-Spectral Information Processing China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

In a military scenario, targets release decoy projectiles to achieve cover, then escape, evading the attack range of the guidance system. Therefore, recognizing target behaviors such as releasing decoys, escaping, and maneuvering, and locating the spatial position and behavior of the target can provide richer information for the precise targeting of the guidance system. Based on the behaviors of both the target and decoy, a behavioraware anti-interference algorithm is proposed. The core idea of this method is to link event recognition with spatio-Temporal action localization tasks. It extracts spatio-Temporal features of consecutive frames through a 3D backbone network and utilizes a 2D backbone network with a feature pyramid to extract spatial features at different levels of key frames for behavior detection at different scales. Additionally, a context extraction network is introduced in the key frames to achieve global context awareness, improving the accuracy of event recognition. Taking into account the correlation between spatial information, temporal information, and contextual information, this paper introduces channel attention mechanisms to fuse various feature components. Simultaneously, a non-local attention module is incorporated into the 3D backbone network to enhance the understanding of global content and the connection between different frames. Experimental results demonstrate that the proposed algorithm outperforms other algorithms, achieving a frame mean Average Precision (mAP) of 92.69% on our anti-interference dataset,enabling better resistance to interference. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Object detection

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Radio-Frequency Interference Source Detection Based on Enhanced YOLOv8 for Synthetic Aperture Interferometric Radiometers

Radio-Frequency Interference Source Detection Based on Enhan...

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2023 Cross Strait Radio Science and Wireless technology Conference, CSRSWTC 2023

作者： Lu, Shimeng Yang, Mingjin Wang, Huanyu Li, Fanxiang Zhu, Dong School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Science and Technology on Multi-Spectral Information Processing Wuhan China

ISBN: (纸本)9798350358971

The presence of radio-frequency interference (RFI) contaminates data collected by synthetic aperture interferometric radiometers (SAIRs). The performances of current RFI detection methods are limited due to insufficient exploration of RFI characteristics. This paper proposes a new RFI source detection method based on a convolutional neural network (CNN) that exploits side-lobe characteristics of the array factor of SAIR systems. Based on the common characteristic observed in RFI images, namely the presence of hexagonal tailings extended from a local peak of the RFI source, this method incorporates the hexagonal tailing feature into the detection scope, thereby expanding the capability to identify RFI. By including the hexagonal tailing feature, the method enhances the comprehensiveness and accuracy of RFI detection. This inclusion leads to a more comprehensive and accurate detection range for RFI sources. Results using real SMOS satellite data demonstrate that the proposed method has improved detection performance compared with traditional approaches that are commonly used in RFI detection tasks for SAIRs. © 2023 IEEE.

关键词： Deep learning Radio frequency interference (RFI) RFI detection

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A moving infrared small target detection method based on optical flow-guided neural networks 4

A moving infrared small target detection method based on opt...

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4th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2023

作者： Zhu, Wenming Tan, Yihua School of Artificial Intelligence and Automation National Key Laboratory of Science & Technology on Multi-spectral Information Processing Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798350326444

In the practical application scenarios of infrared small dim target detection (IRSTD), the background is complex and changeable, which leads to poor detection performance. The traditional methods based on prior knowledge and the existing deep learning methods can only adapt to a single fixed scene, and cannot be popularized in complex and changeable scenes. Therefore, there is still a lack of a robust infrared small target detection method. In this paper, we propose an optical flow-guided neural network for moving infrared small target detection. Firstly, we design an optical flow capture module (OFCM), which can enhance the characteristics of weak targets, and capture the motion information in the image by calculating the optical flow value of the image. Secondly, we design a pyramid feature interactive network (PFI-Net) to perform feature interaction fusion of optical flow and infrared images. PFI-Net can analyse the motion information expressed by the optical flow and the contour information expressed by the infrared image on multiple scales to extract features and perform target detection and segmentation. Experimental results on public datasets show that our method can effectively detect moving targets, and has better performance than other methods. © 2023 IEEE.

关键词： Image enhancement

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VAS: An Unsupervised Visible Area Sensing Module for Lane Classification 3

VAS: An Unsupervised Visible Area Sensing Module for Lane Cl...

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3rd International Conference on Big Data, information and Computer Network, BDICN 2024

作者： Hong, Xing Zhao, Ruixiang Lv, Wu Ma, Jie National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China CSSC Navigation Technology Co. Ltd Beijing China

ISBN: (纸本)9798350363821

Road lane segmentation and classification play a crucial role in autonomous vehicles. Lane segmentation helps localize the position of the lane boundaries and the category information provides assistance for making decisions such as lane changing. With the development of deep learning, lane segmentation becomes faster and stronger, while classification has not received enough attention. Existing methods separate them into two networks, and can not handle complex scenes such as occlusion well. In this paper, we propose a novel network architecture combining the two tasks. To improve the robustness of classification to the scene, we also design a unsupervised visible area sensing module inspired by the simple lane geometry structure. The model was evaluated on several datasets with lane category information and get competitive results. © 2024 IEEE.

关键词： Deep learning

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Robust diffractive neural networks for resilient all-optical intelligent imaging detection 12

Robust diffractive neural networks for resilient all-optical...

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SPIE 12th International Symposium on multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Shi, Jiashuo Zhang, Xinyu National Key Laboratory of Science & Technology on Multi-spectral Information Processing Huazhong University of Science & Technology Wuhan430074 China School of Artificial Intelligence & Automation Huazhong University of Science & Technology Wuhan430074 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Conventional imaging detection relies on electrical modulation, but it grapples with limitations in speed and adaptability within complex environments. Intelligent imaging detection, capable of manipulating micro-nano-lightfields generated by imaging objective lenses, is essential. The all-optical deep diffractive neural network (D2NN) offers a potential solution, enabling swift spatial information transformation and recognition. However, existing D2NN versions are vulnerable to optical disturbances due to their reliance on computer-based backpropagation. This paper introduces a robust diffractive neural network (SRNN) mathematical model designed to mitigate the impact of structural errors and lightwave frequency shifting without necessitating hardware modifications. The SRNN model incorporates Gaussian noise at various levels during training to emulate the error distribution of the phase mask, augmenting the network's resilience to hardware errors. Weight-Noise-Injection training assists the network in locating an error-insensitive region, thereby enhancing its robustness. © 2024 SPIE.

关键词： Optical data processing

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Semantic Segmentation Algorithm of Remote Sensing Images Based on Improved Panoptic

Semantic Segmentation Algorithm of Remote Sensing Images Bas...

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2022 International Conference on Automation, Robotics and Computer Engineering, ICARCE 2022

作者： Zheng, Tong Liu, Liu Chen, Qimeng Chen, Zhong Yu, Longji Zhao, Zeyu Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multi-Spectral Information Processing Wuhan China

ISBN: (纸本)9781665475488

Semantic segmentation of images can play an important role in land use, building extraction, road extraction and vehicle detection using remote sensing images. In the scenario of applying Panoptic FPN algorithm for remote sensing images, we found that the decoder of this algorithm is not robust in feature extraction and cannot do weighted enhancement of key spaces and channels, and its encoder loses a lot of high-dimensional semantic features when simply fusing semantic information at all levels by simple addition. To address these two problems, we propose the Se-Resnext encoder based on the attention mechanism and the Concat-based feature fusion mechanism, respectively, and verify the effectiveness of the methods through experiments. The accuracy of semantic segmentation is improved in both suichang dataset and posdam dataset. © 2022 IEEE.

关键词： Semantics

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PCA-W: An Simulation Algorithm for Waterlogging in Ground Point Cloud Based on Principal Component Analysis 3

PCA-W: An Simulation Algorithm for Waterlogging in Ground Po...

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3rd International Conference on Big Data, information and Computer Network, BDICN 2024

作者： Duan, Yucong Zhang, Yifan Xu, Ziwen Ma, Jie National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan China China Ship Development and Design Center Wuhan Hubei China

ISBN: (纸本)9798350363821

In the context of autonomous driving, ground point clouds play an important role in system decision-making. However, in the case of adverse weather, ground point clouds may suffer from a certain degree of missing data, which will have a negative impact on some downstream tasks of autonomous driving. With the development of autonomous driving algorithms, deep learning has achieved better results in this field. However, due to the lack of point cloud data sets under adverse weather conditions, the network cannot be effectively trained. This paper proposes a simulation algorithm for ground point clouds with water accumulation, which simulates the phenomenon of missing data in the ground point clouds due to road water accumulation in adverse weather. As a data augmentation method, it can expand the data set for downstream tasks. We evaluated the method on the STF dataset and proved its effectiveness. © 2024 IEEE.

关键词： Decision making

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De-DETR: A More Accurate Real-Time Defect Detection Method 3

De-DETR: A More Accurate Real-Time Defect Detection Method

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3rd International Conference on Big Data, information and Computer Network, BDICN 2024

作者： Yue, Zhongmu Xu, Hao Zhang, Yifan Xu, Ziwen Ma, Jie School of Artificial Intelligence and Automation Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multi-spectral Information Processing Wuhan Hubei China China Ship Development and Design Center Wuhan Hubei China

ISBN: (纸本)9798350363821

In industrial production, defect detection technology plays a very important role in improving product yield and increasing enterprise efficiency. At present, defect detection technology based on deep learning mainly focuses on convolutional neural networks. With the gradual application of the Transformer architecture represented by the DETR[1] model in the image field, there is a new direction for the update of defect detection technology. This paper proposes a defect detection method based on DETR. This method focuses on improving the encoding method of defect information, further improving the information fusion efficiency of deep features and shallow features, while maximizing the retention of deep semantic information, effectively improving the overall performance of the model. Experiments show that this method is superior to excellent real-Time detection models such as YOLO and Rt-DETR[2] in terms of defect detection effects on the NEU-DET dataset. © 2024 IEEE.

关键词： information fusion

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