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arXiv 2021年

作者： Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shen zhen University Shenzhen518060 China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen518060 China

Motivation: 3D neuron segmentation is a key step for the neuron digital reconstruction, which is essential for exploring brain circuits and understanding brain functions. However, the fine line-shaped nerve fibers of neuron could spread in a large region, which brings great computational cost to the neuron segmentation. Meanwhile, the strong noises and disconnected nerve fibers bring great challenges to the task. Results: In this paper, we propose a 3D wavelet and deep learning based 3D neuron segmentation method. The neuronal image is first partitioned into neuronal cubes to simplify the segmentation task. Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes;the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers. We also produce a Neuronal Cube Dataset (NeuCuDa) using the biggest available annotated neuronal image dataset, BigNeuron, to train 3D WaveUNet. Finally, the nerve fibers segmented in cubes are assembled to generate the complete neuron, which is digitally reconstructed using an available automatic tracing algorithm. The experimental results show that our neuron segmentation method could completely extract the target neuron in noisy neuronal images. The integrated 3D wavelets can efficiently improve the performance of 3D neuron segmentation and reconstruction. © 2021, CC BY.

关键词： Neurons

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RTM3D: Real-Time Monocular 3D Detection from Object Keypoints for Autonomous Driving 16th

RTM3D: Real-Time Monocular 3D Detection from Object Keypoin...

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16th European Conference on computer vision, ECCV 2020

作者： Li, Peixuan Zhao, Huaici Liu, Pengfei Cao, Feidao Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Lab of Image Understanding and Computer Vision ShenyangLiaoning China

ISBN: (纸本)9783030585792

In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without any extra annotations, category-specific 3D shape priors, or depth maps. Our method is the first real-time system (FPS > 24) for monocular image 3D detection while achieves state-of-the-art performance on the KITTI benchmark. © 2020, Springer Nature Switzerland AG.

关键词： Real time systems

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An improved YOLO V3 for small vehicles detection in aerial images

An improved YOLO V3 for small vehicles detection in aerial i...

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作者： Moran Ju Haibo Luo Zhongbo Wang Shenyang Institute of Automation &Institutes for Robotics and Intelligent Manufacturing & Key Laboratory of Opt-Electronic Information Processing Chinese Academy of Sciences University of Chinese Academy of Sciences The Key Laboratory of Image Understanding and Computer Vision

Small vehicle detection in aerial images is a challenge in computer vision because small vehicles occupy less pixels and the environment around the small vehicles is complex. To improve the detection performance for the vehicles in aerial images, we propose an improved YOLO V3. The main contributions of our work include:(1)We redesign the backbone of YOLO V3 to select suitable scales for small vehicle detection in aerial images;(2) To make the improved YOLO V3 much stronger, we redesign the loss function of original YOLO V3 by GIOU loss and Focal loss;(3) To verify the performance of improved YOLO V3, we do the comparative experiments on VEDAI dataset. The experimental results show that the proposed method has obtained better performance than original YOLO V3 for small vehicle detection in aerial image.

关键词： Convolutional neural network Small vehicle detection YOLO V3 Aerial image

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A Novel LOS Rate Estimation Method Based on images for Strap-down Inertial Guidance 3

A Novel LOS Rate Estimation Method Based on Images for Strap...

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2020 3rd International Conference on Advanced Algorithms and Control Engineering, ICAACE 2020

作者： Xu, Zheng Luo, Haibo Hui, Bin Chang, Zheng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Science Shenyang110016 China Key Lab of Image Understanding and Computer Vision Shenyang110016 China

With the development of technology, precision guided weapon is becoming more and more important in modern war. In order to launch our recent guidance system on medium and small guided weapons, we propose a method to obtain the LOS rate by combining information from both camera and gyroscope. To be specific, we firstly calculate the body LOS angle through transforming the image pixel coordinate system into the image physical coordinate system according to camera internal parameters;then subtract the missile motion information contained in the measurement signal of the seeker and finally the LOS rate is deduced. Comparing with traditional gimballed seekers, our strap-down seekers with camera have significantly reduced costs and influences caused by external environments on platform. © Published under licence by IOP Publishing Ltd.

关键词： Cameras

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Millimeter-wave radar image analysis for the traffic sensing 2

Millimeter-wave radar image analysis for the traffic sensing

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2nd Spring International Conference on Defence Technology, ICDT 2020

作者： Wu, H.M. Qi, F. Wang, J.K. Wang, Y. School of Communication Science and Engineering Northeastern University Liaoning Province Shenyang110819 China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

Millimeter-wave(MMW) radar sensing is one of the most promising technologies to provide safe navigation for autonomous vehicles due to its expected high-resolution imaging capability However, driverless cars have higher request for different environment and light conditions. Therefore, millimetre-wave imaging is of paramount importance for complex load scenario. In this paper, we have built models of pavement pits and bulges and analysed their with differences ways of antennas. A comparison of the imaging performance of experimental systems operating at a MMW radar and a Lidar is presented with the analysis of features for initial image interpretation Experimental images of the complex road surface are made by a 94GHz frequency-modulated continuous-wave (FMCW) radar technique with 3mm wavelength. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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Fast and accurate single-image depth estimation on mobile devices, mobile AI 2021 challenge: Report

arXiv

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arXiv 2021年

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Computer Vision Lab ETH Zurich Switzerland Ltd AI Witchlabs Switzerland Tencent GY-Lab China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Nanjing Artificial Intelligence Chip Research Institute of Automation Chinese Academy of Sciences China Black Sesame Technologies Inc. Singapore Singapore Visual Media Lab KAIST Korea Republic of Harbin Institute of Technology China Peng Cheng Laboratory China Multimedia and Computer Vision Laboratory National Cheng Kung University Taiwan Samsung Research UK United Kingdom OPPO Research Institute China ETH Zurich Switzerland

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2021, The Authors. All rights reserved.

关键词： computer operating systems

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Improving Generalized Zero-Shot Learning by Semantic Discriminator

arXiv

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arXiv 2020年

作者： Li, Xinpeng Ye, Mao Zhou, Lihua Zhang, Dan Zhu, Ce Liu, Yiguang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Vision and Image Processing Laboratory School of Computer Science Sichuan University Chengdu610065 China

It is a recognized fact that the classification accuracy of unseen classes in the setting of Generalized Zero-Shot Learning (GZSL) is much lower than that of traditional Zero-Shot Leaning (ZSL). One of the reasons is that an instance is always misclassified to the wrong domain. Here we refer to the seen and unseen classes as two domains respectively. We propose a new approach to distinguish whether the instances come from the seen or unseen classes. First the visual feature of instance is projected into the semantic space. Then the absolute norm difference between the projected semantic vector and the class semantic embedding vector, and the minimum distance between the projected semantic vectors and the semantic embedding vectors of the seen classes are used as discrimination basis. This approach is termed as SD (Semantic Discriminator) because domain judgement of instance is performed in the semantic space. Our approach can be combined with any existing ZSL method and fully supervision classification model to form a new GZSL method. Furthermore, our approach is very simple and doesn’t need any fixed parameters. A large number of experiments show that the accuracy of our approach is 8.5% to 21.9% higher than the current best method. Copyright © 2020, The Authors. All rights reserved.

关键词： Discriminators

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RTM3D: Real-time monocular 3D detection from object keypoints for autonomous driving

arXiv

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arXiv 2020年

作者： Li, Peixuan Zhao, Huaici Liu, Pengfei Cao, Feidao Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Key Lab of Image Understanding and Computer Vision Liaoning Province

In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. Most successful 3D detectors take the projection constraint from the 3D bounding box to the 2D box as an important component. Four edges of a 2D box provide only four constraints and the performance deteriorates dramatically with the small error of the 2D detector. Different from these approaches, our method predicts the nine perspective keypoints of a 3D bounding box in image space, and then utilize the geometric relationship of 3D and 2D perspectives to recover the dimension, location, and orientation in 3D space. In this method, the properties of the object can be predicted stably even when the estimation of keypoints is very noisy, which enables us to obtain fast detection speed with a small architecture. Training our method only uses the 3D properties of the object without the need for external networks or supervision data. Our method is the first real-time system for monocular image 3D detection while achieves state-ofthe-art performance on the KITTI benchmark. Code will be released at https://***/Banconxuan/RTM3D. Copyright © 2020, The Authors. All rights reserved.

关键词： Real time systems

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Random-valued impulse denoising using a fast l 1-minimization-based image inpainting technique

Random-valued impulse denoising using a fast l 1-minimizatio...

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International Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2016

作者： Kalita, Mayuri Deka, Bhabesh Computer Vision and Image Processing Laboratory Department of Electronics and Communication Engineering Tezpur University TezpurAssam784028 India

ISBN: (纸本)9789811047640

In this chapter, an image inpainting approach based on l 1-norm regularization is presented for the estimation of pixels corrupted by the random-valued impulse noise. It is a two-stage reconstruction scheme. First, a reasonably accurate random-valued impulse detection scheme is applied to detect the corrupted pixels. Next, the corrupted pixels are treated as missing pixels and replaced by using an image inpainting technique. The inpainting method is based on the fast iterative shrinkage thresholding algorithm (FISTA). The proposed method is fast and experimental results show that it is robust to non-Gaussian and nonadditive degradations like the random-valued impulse noise. It also outperforms similar random-valued impulse denoising schemes in terms of computational complexity while preserving the image quality. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Pixels

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Weighted wavelet tree sparsity regularization for compressed sensing magnetic resonance image reconstruction

Weighted wavelet tree sparsity regularization for compressed...

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International Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2016

作者： Deka, Bhabesh Datta, Sumit Computer Vision and Image Processing Laboratory Department of Electronics and Communication Engineering Tezpur University TezpurAssam784028 India

ISBN: (纸本)9789811047640

Compressed sensing in magnetic resonance imaging (CS-MRI) improves the MRI scan time by acquiring only a few k-space samples and then reconstructs the image using a nonlinear procedure from the highly undersampled measurements. Besides the standard wavelet sparsity, MR images are also found to exhibit tree sparsity across various scales of the wavelet decomposition which are generally modeled as overlapping group sparsity. In this chapter, we propose a novel iteratively weighted wavelet tree sparsity based CS-MRI reconstruction technique to estimate MR images from highly undersampled Fourier measurements. Simulations on various real MR images show that the proposed technique offers significant improvements compared to the state-of-the-art either in terms of visual quality or k-space measurements with the same reconstruction time. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Magnetic resonance imaging

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