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Building a Stereo and Wide-View Hybrid RGB/FIR Imaging System for Autonomous Vehicle

IEEE Sensors Journal 2022年第2期22卷 1638-1651页

作者： Zhang, Yigong Sun, Zezhou Wu, Banghe Yang, Jian Kong, Hui Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China University of Macau Taipa China

In this work, a novel paradigm is proposed to build a hybrid RGB/FIR camera, which outputs pixel-wisely aligned and temporally synchronized RGB and FIR frames simultaneously. Compared with the existing schemes, we innovate in several ways to make it compact, practical, and extendable for real-world applications. First, we propose a new structure layout design of the hybrid camera, and we also design a removable adjuster to detach the hybrid camera from the base platform. For RGB and FIR image registration, we have achieved a pixel-wise spatial registration of the FIR and RGB frames by a two-step alignment, where we propose a ghost-effect removal scheme to remove the ghost effect from the rolling-shutter-based FIR camera. Finally, multiple hybrid cameras are extended to a hybrid camera array, through which we can obtain wide-view spatially aligned FIR, RGB and disparity images simultaneously. The experimental results demonstrate the accuracy of the hybrid camera and the effectiveness of the designed stereo wide-view hybrid camera system. © 2001-2012 IEEE.

关键词： Cameras

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Head pose estimation with improved random regression forests

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2012 International Conference on Service-Oriented Computing, ICSOC 2012

作者： Zhu, Ronghang Sang, Gaoli Cai, Ying You, Jian Zhao, Qijun National Key Laboratory of Fundamental Science on Synthetic Vision Institute of Image and Graphics School of Computer Science China

ISBN: (纸本)9783319029603

Head pose estimation is an important step in many face related applications. In this paper, we propose to use random regression forests to estimate head poses in 2D face images. Given a 2D face image, Gabor filters are first applied to extract raw high-dimensional features. Linear discriminant analysis (LDA) is then used to reduce the feature dimension. Random regression forests are constructed in the low dimensional feature space. Unlike traditional random forests, when generating tree predictors in the forests we weight the features according to the eigenvalues associated with their corresponding LDA axes. The proposed method has been evaluated on a set of 2D face images synthesized from the BU-3DFE database and on the CMU-PIE database. The experimental results demonstrate the effectiveness of the proposed method. © Springer International Publishing 2013.

关键词： Regression analysis

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Multi-Zone Transformer Based on Self-Distillation for Facial Attribute Recognition 17

Multi-Zone Transformer Based on Self-Distillation for Facial...

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17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023

作者： Chen, Si Zhu, Xueyan Wang, Da-Han Zhu, Shunzhi Wu, Yun Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9798350345445

Recently, transformers have shown great promising performance in various computer vision tasks. However, the current transformer based methods ignore the information exchanges between transformer blocks, and they have not been applied in the facial attribute recognition task. In this paper, we propose a multi-zone transformer based on self-distillation for FAR, termed MZTS, to predict the facial attributes. A multi-zone transformer encoder is firstly presented to achieve the interactions of the different transformer encoder blocks, thus avoiding forgetting the effective information between the transformer encoder block groups during the iteration process. Furthermore, we introduce a new self-distillation mechanism based on class tokens, which distills the class tokens obtained from the last transformer encoder block group to the other shallow groups by interacting with the significant information between the different transformer blocks through attention. Extensive experiments on the challenging CelebA and LFWA datasets have demonstrated the excellent performance of the proposed method for FAR. © 2023 IEEE.

关键词： Face recognition

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Deform-CAM: Self-attention Based on Deformable Convolution for Weakly Supervised Semantic Segmentation 17th

Deform-CAM: Self-attention Based on Deformable Convolution f...

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17th Chinese Conference on image and Graphics Technologies and Applications, IGTA 2022

作者： Huang, Feihong Wang, Da-Han Ye, Hai-Li Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Xiamen China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen China

ISBN: (纸本)9789811950957

Weakly-supervised semantic segmentation (WSSS) receives increasing attentions from the community in recent years as it leverages the weakly annotated data to solve the problem of lacking of fully annotated data. Among them, the WSSS method based on image-level annotation is the most direct and effective while the image-level annotation is easy to obtain. Most advanced methods use class activation maps (CAM) as initial pseudo-labels, however, they only identify local regions of the target, while ignoring the context information among local regions. To solve this problem, this paper proposes a deformable convolution based self-attention module (DSAM), which introduces a pixel relationship matrix, to learn the contextual information of the image. A regularization loss is introduced to narrow the distance between the DSAM and the CAM. Compared to the base CAM method, our method can identify more target features and robustly improve the performance of WSSS without training the classifier multiple times. Our proposed method achieves the mIoU of 65.5% and 66.8% on the Pascal VOC 2012 val and test sets, respectively, demonstrating the feasibility of the method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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GCAT: graph calibration attention transformer for robust object tracking

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Neural Computing and Applications 2024年第23期36卷 14151-14172页

作者： Chen, Si Hu, Xinxin Wang, Da-Han Yan, Yan Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen361005 China

Recent Siamese trackers have taken advantage of transformers to achieve impressive advancements. However, existing transformer trackers ignore considering the positional and structural information between tokens, and traditional template update strategies easily introduce noises to the dynamic templates during tracking. In order to alleviate this issue, this paper develops a novel end-to-end graph calibration attention transformer network (GCAT) to enhance tracking robustness and accuracy. A graph calibration attention mechanism is first designed to calibrate and aggregate template information, for effectively updating dynamic templates during the tracking process. Specifically, each token is considered as a node in the graph, and then, we calculate the weight relationships between each node and their adjacent nodes. Thus, this mechanism can aggregate the global context information of the template and search nodes and activate feature channels based on weights and biases to obtain more discriminative feature information. Moreover, we leverage a multi-level dropout mechanism to perform the data dropout, the layer dropout, and the feature dropout on the data, network, and attention levels, respectively, to avoid overfitting of local-specific information and improve the generalization ability. Extensive experiments show the proposed method achieves superior performance on seven challenging benchmark datasets, i.e., OTB100, OTB2013, UAV123, LaSOT, GOT10K, VOT2020, and TrackingNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Aggregates

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A parallel artificial immune model for optimization

A parallel artificial immune model for optimization

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2009 International Conference on Computational Intelligence and Security, CIS 2009

作者： Qi, Yutao Liu, Fang Jiao, Licheng School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Xidian University Xi'an China Inst. of Intelligent Information Processing and Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China

ISBN: (纸本)9780769539317

This paper presents a parallel artificial immune model termed as tower master-slave model (TMSM) for solving optimising problems. Based on TMSM, the parallel immune memory clonal selection algorithm (PIMCSA) is also proposed. TMSM is a two level coarse-grained parallel artificial immune model with distributed immune response and distributed immune memory. In PIMCSA, vaccines are extracted and migrated between populations rather than individual migration as has been done in parallel genetic algorithms. It is a good balance between population diversity and the convergent speed. Experimental results on the numerical optimization and TSP problems show that PIMCSA achieves good performance in terms of both solution quality and computation time. © 2009 IEEE.

关键词： Genetic algorithms

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Exploring Joint Information of Multi-Scales for Vehicle Re-Identification 22

Exploring Joint Information of Multi-Scales for Vehicle Re-I...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Zhou, Yongjie Wang, Dahan School of Computer and Information Engineering Xiamen University of Technology Xiamen Chinese Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Vehicle re-identification (re-ID) is an essential component of intelligent video surveillance, which attempts to solve the problem of retrieving specific vehicle instances. The technical challenge is mainly the requirement that the algorithm be robust under different viewing angles, resolutions, occlusions, and lighting conditions, yet different conditions can arise where the appearance of the exact vehicle varies dramatically. In contrast, the appearance of two different vehicles is exceptionally similar. To overcome this enormous challenge: we adopt a novel multi-scales attention network architecture, which will feature representation for feature maps at different scales to enhance the recognition of intricate parts, and introduce an inter-coordinate attention module to build interdependencies between component positions, making it possible to build a more robust vehicle model feature representation, and finally this paper also proposes a camera-based vehicle re localization method that enables the model to calculate the vehicle occurrence probability using the potential information among road network cameras as a post-processing step to re-correct the confidence level of the re-identification ranking. Experimental results show that the method outperforms the reference method in all indexes even without post-processing. At the same time, the camera confidence correction can be used in overlay with the reordering clustering correction method, which can improve the results by four percentage points and MAP up to 81.4%. © 2022 ACM.

关键词： Vehicles

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CDE-Net: A semi-supervised nucleus instance segmentation method based on center deviation estimation 22

CDE-Net: A semi-supervised nucleus instance segmentation met...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Yan, Yi Wang, Da-Han Ye, Haili Zhu, Shunzhi Li, Jianmin School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Cell nucleus segmentation is an important step in the diagnosis of tumor malignancy. In the case of out-of-focus, the cell nucleus boundary tends to be blurred, which increases the difficulty of cell segmentation and marking. Therefore, in the case of marking only some core instances, it is very important to realize the segmentation of core instances. In this paper, we propose a semi-supervised nucleus instance segmentation method based on anchor bias prediction that is achieved by our proposed center deviation estimation network (CDE-Net). By introducing the CDE branch to predict the offset degree of the anchor frame, and weighting the confidence of the anchor frame, more accurate prediction results can be obtained. The model adopts the semi-supervised training method to progressively learn the CDE-Net model, which alleviates the problem of imbalance between positive and negative samples caused by only marking part of the nuclear samples. The experimental results show that CDE-Net is superior to Mask R-CNN in many evaluation indicators, and achieves SOTA on the partially labelled MoNuSeg data set. © 2022 Association for Computing Machinery. All rights reserved.

关键词： Diagnosis

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Brain Tumor image Segmentation Based on Grouped Convolution 6

Brain Tumor Image Segmentation Based on Grouped Convolution

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2022 6th International Conference on Machine vision and Information Technology, CMVIT 2022

作者： Wu, Mingliang Ye, Hai-Li Wu, Yun Li, Jianmin School of Computer and Information Engineering Xiamen University of Technology Xiamen361000 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361000 China

The automatic segmentation of MRI multi-modal images of brain tumors is one of the important research contents of disease detection and analysis. Due to the heterogeneity of tumors, it is difficult to achieve efficient and accurate automatic segmentation of brain tumors. Traditional segmentation methods based on machine learning cannot handle complex scenes such as complex edges and overlapping categories. In clinical assisted diagnosis, it is of great significance to apply deep learning to two-dimensional natural image segmentation and three-dimensional medical image segmentation. In this paper, we propose a three-dimensional network model to achieve precise segmentation of brain tumors. The model adopts an encoder-decoder structure and replaces ordinary convolution with grouped convolution to reduce network parameters and improve network performance. The model improves the problem of information exchange between different groups through channel mixing. Experiments conducted on the BraTS (Brain Tumor Segmentation) 2018 challenge dataset prove that our network greatly reduces the computational cost while ensuring segmentation accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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Dynamic Updating of Partial Feature Maps for image-based Real-Time Recognition of Online Handwritten Chinese Text 22

Dynamic Updating of Partial Feature Maps for Image-based Rea...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Wu, Yuefeng Zhong, Ying Wang, Da-Han Chi, Xueke School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Online handwritten Chinese text recognition (OHCTR) has made certain progress in recent years. Most methods currently used are image-based text recognition which has achieved some success. However, it should be noticed that, it is difficult to smoothly recognize online handwritten texts in real-time, especially for long text lines. For real-time recognition of OHCT, the feature extraction of ever-extending text lines will be time-consuming and make the system un-humanization. Moreover, the feature extraction of the non-written part will be performed repeatedly, which is not suitable and has not been addressed by previous works. In this paper, we propose a method to dynamically update partial feature maps of the newly written part for real-time OHCTR. The basic idea is to update the feature maps according to the newly written stroke, which can avoid repeated convolutional computations and thus improve the recognition speed. The proposed method can handle the case of delayed strokes, and can greatly improve the speed of real-time recognition without any loss of accuracy. Experiments on the CASIA-OLHWDB and the ICDAR-2013 competition database demonstrate that our real-time recognition speed are multiplier increased compared with traditional text line recognition methods. © 2022 Association for Computing Machinery. All rights reserved.

关键词： Convolution

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