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Theanalysis and test ofthethermal shock resistance of the Multi-spectralCVD ZnS dome

Theanalysis and test ofthethermal shock resistance of the Mu...

International Symposium on Optoelectronic Technology and Application 2016(OTA 2016)——Conf.12 Infrared Technology and Application

作者： ZHANG Daijun LUO Haibo ZHOU Peipei HOU Xinglin Shenyang Institute of Automation Chinese Academy of Science University of Chinese Academy of Science Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Key Laboratory of Image Understanding and Computer Vision

Optical guidance system start infrared guided mode by throwing away the fairing in the terminal guidance phase. After throwing away the fairing, the infrared dome will be suddenly exposed to the aerod

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An Anti-interfering Reconstruction Algorithm Based on Compressed Sensing

An Anti-interfering Reconstruction Algorithm Based on Compre...

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2013 3rd International Conference on Electric and Electronics(EEIC 2013)

作者： Mei Du Huaici Zhao Chunyang Zhao Bo Li Shenyang Institute of automation Chinese Academy of Science University of Chinese Academy of Sciences Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Key Laboratory of Image Understanding and Computer Vision

When images compressed by traditional transformation-based compression algorithms are transmitted over wireless channels, if the gaussian random interference causes the loss of the crucial transformation coefficients, the contents of the reconstructed images will be lost obviously and this will reduce the accuracy of the subsequent detection and recognition results greatly. In order to solve this problem, this paper proposed an antiinterfering image reconstruction algorithm based on compressed sensing. This algorithm first confirmed the new compressed sensing signals and the new reconstruction matrix based on the locations of the compressed sensing signal components corresponding to the gaussian-interfered bit stream, and then reconstructed the original images employing the iterative threshold algorithm. The simulation results demonstrated that the new algorithm reconstructed exact images at low bit error rates, and reconstructed inexact images whose qualities were slightly lowered without loss of local contents at high bit error rates. As a result, our algorithm is able to overcome the deficiencies of compression algorithms based on diverse transformations and the iterative threshold algorithm, thus proposes a feasible solution scheme for the anti-interfering problem that arises in wireless image transmission.

关键词： anti-interference compressed sensing image reconstruction Gaussian random interference

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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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Multiobjective social evolutionary algorithm based on multi-agent

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Ruan Jian Xue Bao/Journal of Software 2009年第7期20卷 1703-1713页

作者： Pan, Xiao-Ying Liu, Fang Jiao, Li-Cheng Key Lab. of Intelligent Perception and Image Understanding of the Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Computer Science and Engineering Xidian University Xi'an 710071 China

In this paper, a multi-agent social evolutionary algorithm is proposed for multiobjective optimization problems. It completes the search process by the agent evolution. MOMASEA (multi-agent social evolutionary algorithm for multiobjective) defines the trust degree to denote the historical information of agents, and the neighborhood of agent is confirmed by it. According to the characteristic of multiobjective problems, three evolutionary operators are designed to complete the whole evolutionary process. The experimental results show that MOMASEA has a good convergence to the Pareto set. Furthermore, the analysis of the mode for instructs local environment verified that importing acquaintance net model can speed up the convergence effectively. © by Institute of Software, the Chinese Academy of Sciences.

关键词： Multiobjective optimization

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Adversarial learning of structure-aware fully convolutional networks for landmark localization

arXiv

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arXiv 2017年

作者： Chen, Yu Shen, Chunhua Chen, Hao Wei, Xiu-Shen Liu, Lingqiao Yang, Jian Key Lab of Intelligent Perception and Systems High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology School of Computer Science University of Adelaide Australian Centre for Robotic Vision Department of Computer Science and Technology Nanjing University

Landmark/pose estimation in single monocular images has received much effort in computer vision due to its important applications. It remains a challenging task when input images come with severe occlusions caused by, e.g., adverse camera views. Under such circumstances, biologically implausible pose predictions may be produced. In contrast, human vision is able to predict poses by exploiting geometric constraints of landmark point inter-connectivity. To address the problem, by incorporating priors about the structure of pose components, we propose a novel structure-aware fully convolutional network to implicitly take such priors into account during training of the deep network. Explicit learning of such constraints is typically challenging. Instead, inspired by how human identifies implausible poses, we design discriminators to distinguish the real poses from the fake ones (such as biologically implausible ones). If the pose generator G generates results that the discriminator fails to distinguish from real ones, the network successfully learns the priors. Training of the network follows the strategy of conditional Generative Adversarial Networks (GANs). The effectiveness of the proposed network is evaluated on three pose-related tasks: 2D human pose estimation, 2D facial landmark estimation and 3D human pose estimation. The proposed approach significantly outperforms several state-of-the-art methods and almost always generates plausible pose predictions, demonstrating the usefulness of implicit learning of structures using GANs. Copyright © 2017, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A legible method for fluid cavity detection in ultrasound image

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2013 International Conference on Energy Research and Power Engineering, ERPE 2013

作者： Guo, De Quan Yang, Hong Yu Zhang, Zhi Jie Liu, Dong C. Key Lab. of Fundamental Synthetic Vision Graphics and Image Sci. for National Defense Sichuan University Chengdu 610065 China Neijiang Vocational and Technical College Neijiang Sichuan 641100 China College of Computer science Sichuan University Chengdu 610065 China

ISBN: (纸本)9783037857588

We present a novel method to modulate influence of diffuse scattering in fluid cavity and reflection at boundary of pulse-echo ultrasound image. A simplified formation of ultrasound model is used to delineate it, and which is merged with modified dehaze model from principle of ultrasonic imaging, a legible fluid cavity in vascular or amniotic fluid of ultrasound image is obtained. Local and global characteristics of the image are utilized to adjust diffuse in different levels. Experimental results indicate that the proposed method is capable of producing ultrasound images that have better image quality and greater fidelity to the true tissue reflectivity. © (2013) Trans Tech Publications, Switzerland.

关键词： Ultrasonic imaging

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Defining quantum neural networks via quantum time evolution

arXiv

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arXiv 2019年

作者： Dendukuri, Aditya Keeling, Blake Fereidouni, Arash Burbridge, Joshua Luu, Khoa Churchill, Hugh Computer Vision and Image Understanding Lab Computer Science and Computer Engineering Department Department of Physics University of Arkansas FayettevilleAR72701 United States Institute for Nanoscience and Engineering University of Arkansas FayettevilleAR72701 United States

This work presents a novel fundamental algorithm for for defining and training Neural Networks in Quantum Information based on time evolution and the Hamiltonian. Classical Neural Network algorithms (ANN) are computationally expensive. For example, in image classification, representing an image pixel by pixel using classical information requires an enormous amount of computational memory resources. Hence, exploring methods to represent images in a different paradigm of information is important. Quantum Neural Networks (QNNs) have been explored for over 20 years. The current forefront work based on Variational Quantum Circuits is specifically defined for the Continuous Variable (CV) Model of quantum computers. In this work, a model is proposed which is defined at a more fundamental level and hence can be inherited by any variants of quantum computing models. This work also presents a quantum backpropagation algorithm to train our QNN model and validate this algorithm on the MNIST dataset on a quantum computer simulation. Copyright © 2019, The Authors. All rights reserved.

关键词： Qubits

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Completely self-supervised crowd counting via distribution matching

arXiv

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arXiv 2020年

作者： Sam, Deepak Babu Agarwalla, Abhinav Joseph, Jimmy Sindagi, Vishwanath A. Babu, R. Venkatesh Patel, Vishal M. Video Analytics Lab Department of Computational and Data Sciences Indian Institute of Science Bangalore India Vision & Image Understanding Lab Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States

Dense crowd counting is a challenging task that demands millions of head annotations for training models. Though existing self-supervised approaches could learn good representations, they require some labeled data to map these features to the end task of density estimation. We mitigate this issue with the proposed paradigm of complete self-supervision, which does not need even a single labeled image. The only input required to train, apart from a large set of unlabeled crowd images, is the approximate upper limit of the crowd count for the given dataset. Our method dwells on the idea that natural crowds follow a power law distribution, which could be leveraged to yield error signals for backpropagation. A density regressor is first pretrained with self-supervision and then the distribution of predictions is matched to the prior by optimizing Sinkhorn distance between the two. Experiments show that this results in effective learning of crowd features and delivers significant counting performance. Furthermore, we establish the superiority of our method in less data setting as well. The code and models for our approach is available at https://***/val-iisc/css-ccnn. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Fast Candidate Region Extraction for SAR Ship Target

Fast Candidate Region Extraction for SAR Ship Target

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Panpan Zhang Haibo Luo Zheng Xu Miao He Shenyang Institute of Automation Shenyang China Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Shenyang China The Key Lab of Image Understanding and Computer Vision Shenyang China

ISBN: (纸本)9781665465373

At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes. To be able to use the deep learning model for training and testing, the image needs to be cropped to the appropriate size. In a high-resolution SAR ship image, the ship target usually takes only a small part of the whole image. As a result, only part of the cropped image contains the target, and most of the rest are background regions. This will cause a lot of computational redundancy in the model inference stage. To solve this problem, a fast candidate region extraction algorithm is proposed in this paper for ship target extraction. The algorithm consists of three parts: firstly, extract the saliency map of the SAR ship image, secondly, perform median filtering on the segmented image, and thirdly, extract candidate regions. The superiority of the algorithm was demonstrated by experiments on the AIR-SARShip-1.0 dataset.

关键词： Deep learning Training Redundancy Radar polarimetry Inference algorithms Indexes Marine vehicles

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Performance Bound of Position Estimation in image Matching

Performance Bound of Position Estimation in Image Matching

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2011 International Conference on Advanced Materials in Microwaves and Optics（AMMO 2011）

作者： Bao-shu XU Ze-lin SHI Shenyang Institute of Automation Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Sciences Key Laboratory of Image Understanding and Computer Vision Liaoning Province

Position estimation in image matching is a fundamental step in computer vision and image processing. To deal with the problem of performance prediction, we formulate it under statistical parameter estimation aspect. The lower bound of position estimation variance is obtained based on Cramer-Rao lower bound(CRLB) theory. This paper analyses the impact of noise to 1-D signal matching, derives the lower bound of variance, and then extends it to 2-D image matching. Furthermore, we derive numerical expression that can be computed from observed data. Finally, we use Monte Carlo simulation method to verify the derived analytical expressions. Experimental results show that the derived CRLB is tight to simulation estimated variance. The CRLB can characterize the performance bound of position estimation in image matching.

关键词： Performance prediction image matching Cramer-Rao Lower Bound Fisher information

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