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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

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Laboratory Approbation of the Algorithm for Isolating People's Faces on a Thermal Infrared image in the Case of Their Quasi-Stationary Arrangement in a Room 8

Laboratory Approbation of the Algorithm for Isolating People...

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8th Annual International conference of the Biologically-Inspired-Cognitive Architectures-Society on Biologically Inspired Cognitive Architectures (BICA)

作者： Alyushin, M., V Alyushin, A. M. Kolobashkina, L., V Natl Res Nucl Univ MEPhI Moscow Engn Phys Inst Moscow Russia

Increasing the effectiveness of training and training sessions is possible through the implementation of so-called biological feedback. Such feedback allows the teacher, or the instructor, to continuously monitor the current psycho-emotional and functional state of the students. As a result, it becomes possible to adapt the style, pace, training mode and the volume of the material outlined, depending on the current receptivity and fatigue level of the listeners. The main element of systems that implement biological feedback in practice are remote non-contact technologies. Such technologies allow in a fully automatic mode to register the main most informative human bio-parameters. Among them, in the first place are the parameters characterizing the current state of the cardiovascular system of man, his breathing system, as well as his peripheral nervous system. The bulk of information is obtained by processing in real time the thermal infrared image of a person's face. Unfortunately, existing algorithms for distinguishing a person's face have a sufficiently high computational complexity and insufficient reliability. A typical example in this regard can be a family of algorithms based on the Viola-Jones approach. The approach proposed in the work is based on taking into account additional information about the most likely location of a person's face on a thermal image. This approach is most appropriate to use in cases of quasi-stationary location of people in the room. A typical example is the location of students at the tables in the classroom. For such cases it is possible to determine the areas of the most probable location of the trainees' faces, as well as the possible boundaries of their movement. Laboratory tests of the developed program on the basis of the proposed algorithm have confirmed its high productivity, as well as efficiency in identifying students faces in the classroom. (C) 2018 The Authors. Published by Elsevier Ltd. This is an open access article

关键词： Thermal image of the face Face Selection Algorithm Quasi-Stationary Arrangement of People

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Near-field SAR imaging with dynamic metasurface antennas using an adapted range migration algorithm 3

Near-field SAR imaging with dynamic metasurface antennas usi...

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conference on Computational Imaging iii

作者： Diebold, Aaron V. Pulida-Mancera, Laura Sleasman, Timothy Boyarsk, Michael Imani, Mohammadreza F. Smith, David R. Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

ISBN: (纸本)9781510618503

Synthetic aperture radar (SAR) is a well-established approach for retrieving images with high resolution. However, common hardware used for SAR systems is usually complex and costly, and can suffer from lengthy signal acquisition. In near-field imaging, such as through-wall-sensing and security screening, simpler and faster hardware can be found in the form of dynamic metasurface antennas (DMAs). These antennas consist of a waveguide-fed array of tunable metamaterial elements whose overall radiation patterns can be altered by DC signals. By sweeping through a set of tuning states, near-field imaging can be accomplished by multiplexing scene information into a collection of measurements, which are post-processed to retrieve scene information. While DMAs simplify hardware, the post-processing can become cumbersome, especially when DMAs are moving in a fashion similar to SAR. In this presentation, we address this problem by modifying the range migration algorithm (RMA) to be compatible with DMAs. To accommodate complex patterns generated by DMAs in the RMA, a pre-processing step is introduced to transform the measurements into an equivalent set corresponding to an effective multistatic configuration, for which specific forms of the algorithm have been derived. As we are operating in the near field of the antennas, some approximations made in the classical formulation of RMA may not be valid. In this paper, we examine the effect of one such approximation: the discarding of amplitude terms in the signal-target Fourier relationship. We demonstrate the adaptation of the RMA to near field imaging using a DMA as central hardware of a SAR system, and discuss the effects of this approximation on the resulting image quality.

关键词： Computational imaging Metamaterials image reconstruction techniques

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The Analysis of image Enhancement for Target Detection 9th

The Analysis of Image Enhancement for Target Detection

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9th International conference on Swarm Intelligence (ICSI)

作者： Zhang, Rui Jia, Yongjun Shi, Lihui Pan, Hang Chen, Jinlong Chen, Xianjun Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Guangxi Peoples R China Beijing Inst Special Electrometr Technol Beijing 100190 Peoples R China Guilin Univ Elect Technol Key Lab Intelligent Proc Comp Image & Graph Guilin 541004 Guangxi Peoples R China Haikou Coll Econ Informat Engn Sch Haikou 571127 Hainan Peoples R China

ISBN: (纸本)9783319938189;9783319938172

In the process of automatic detection and recognition based on image, the quality of the detected images affects the target detection and recognition results. To solve the problem of low contrast and high signal-to-noise ratio of the target image in the target detection process, this paper introduces two types of image detail enhancement algorithms which are widely used in recent years, including brightness contrast image enhancement algorithm and HSV color space based enhancement algorithm, and its impact on the target detection. Experiments show that the image detail enhancement can improve the overall and local contrast of the image, highlight the details of the image, and the enhanced image can effectively improve the number and accuracy of the target detection.

关键词： image enhancement Brightness contrast HSV color space Target detection

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An Improved Method Based on Deep Reinforcement Learning for Target Searching

An Improved Method Based on Deep Reinforcement Learning for ...

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International conference on Robotics and Automation Engineering (ICRAE)

作者： Xiao Long Wei Xiang Lin Huang Tao Lu Ge Ge Song School of Computer Science Communication University of China Beijing China Simulation Research Center China Aerospace System Simulation Technology Co. Ltd (Beijing) Beijing China

ISBN: (数字)9781728147406

ISBN: (纸本)9781728147413

Unmanned Aerial Vehicle (UAV), due to their high mobility and the ability to cover areas of different heights and locations at relatively low cost, are increasingly used for disaster monitoring and detecting. However, developing and testing UAVs in real world is an expensive task, especially in the domain of search and rescue, most of the previous systems are developed on the basis of greedy or potential-based heuristics without neural network. On the basis of the recent development of deep neural network architecture and deep reinforcement learning (DRL), in this research we improved the probability of success rate of searching target in an unstructured environment by combining image processing algorithms and reinforcement learning methods (RL). This paper aims at the deficiency of target tracking in unstructured environment, trying to propose an algorithm of stationary target positioning of UAV based on computer vision system. Firstly, a new input source is formed by acquiring depth information image of current environment and combining segmentation image. Secondly, the DQN algorithm is used to regulate the reinforcement learning model, and the specific flight response can be independently selected by the UAV through training. This paper utilizes open-source Microsoft UAV simulator AirSim as training and test environment based with Keras a machine learning framework. The main approach investigated in this research is modifying the network of Deep Q-Network, which designs the moving target tracking experiment of UAV in simulation scene. The experimental results demonstrate that this method has better tracking effect.

关键词： Reinforcement learning image segmentation Neural networks Unmanned aerial vehicles Task analysis Meteorology Markov processes

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25th Saint Petersburg International conference on Integrated Navigation systems, ICINS 2018 - Proceedings

25th Saint Petersburg International Conference on Integrated...

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25th Saint Petersburg International conference on Integrated Navigation systems, ICINS 2018

ISBN: (纸本)9785919950578

The proceedings contain 105 papers. The topics discussed include: a novel autonomous navigation technique using pictures in support of a circumlunar mission: development testing aboard the iss;inertial sensor data based motion estimation aided by image processing and differential barometry;specific features of methods and algorithms for planning unmanned vehicles' routes in dynamically changing road scene;precise positioning using the modified ambiguity function approach with combination of GPS and Galileo observations;on integration of a strapdown inertial navigation system with modern magnetic sensors;vehicle dynamic model-based integrated navigation system for land vehicles;and using high-order signal variance moments to evaluate noise characteristics of measuring channels.

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Synchronized 2D SLAM and 3D Mapping Based on Three Wheels Omni-directional Mobile Robot

Synchronized 2D SLAM and 3D Mapping Based on Three Wheels Om...

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IEEE International conference on Cyber Technology in Automation, Control, and Intelligent systems

作者： Sibo Huang Chong Li Zhaoquan Cai Guijie Zhu Li Yao Zhun Fan Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou China Huizhou University Huizhou China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

In this paper, we design a three wheels Omni-directional mobile robot (TOMR) and propose a method of simultaneous construction of 2D and 3D maps based on the mobile robot. To be more specific, we use information from a laser and a kinect to build 2D grid maps and 3D environment, respectively. The particle filter algorithm is used to achieve the pose of the robot, together with the OctoMap which is generated from a 3D point cloud map, to construct the 2D and 3D maps. An asymmetric environment is employed to test our proposed method and some state-of-the-art methods like RGB-D SLAM and ORB-SLAM. The experimental results show that the proposed method is efficient for synchronized 2D and 3D mapping and has better performance than other compared algorithms.

关键词： Three-dimensional displays Mobile robots Two dimensional displays Robot kinematics Wheels Simultaneous localization and mapping

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Identifying Asthma genetic signature patterns by mining Gene Expression BIG Datasets using image Filtering algorithms

Identifying Asthma genetic signature patterns by mining Gene...

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IEEE International Workshop on Imaging systems and Techniques (IST)

作者： Mahmood Yaseen Hachim Bassam Mahboub Qutayba Hamid Rifat Hamoudi Sharjah Institute for Medical Research College of Medicine University of Sharjah United Arab Emirates Rashid Hospital Dubai United Arab Emirates Division of Surgery and Interventional Science University College London United Kingdom

ISBN: (数字)9781728138688

ISBN: (纸本)9781728138695

Asthma is a treatable but incurable chronic inflammatory disease affecting more than 14% of the UAE population. Asthma is still a clinical dilemma as there is no proper clinical definition of asthma, unknown definitive underlying mechanisms, no objective prognostic tool nor bedside noninvasive diagnostic test to predict complication or exacerbation. Big Data in the form of publicly available transcriptomics can be a valuable source to decipher complex diseases like asthma. Such an approach is hindered by technical variations between different studies that may mask the real biological variations and meaningful, robust findings. A large number of datasets of gene expression microarray images need a powerful tool to properly translate the image intensities into truly differential expressed genes between conditioned examined from the noise. Here we used a novel bioinformatic method based on the coefficient of variance to filter nonvariant probes with stringent image analysis processing between asthmatic and healthy to increase the power of identifying accurate signals hidden within the heterogeneous nature of asthma. Our analysis identified important signaling pathways members, namely NFKB and TGFB pathways, to be differentially expressed between severe asthma and healthy controls. Those vital pathways represent potential targets for future asthma treatment and can serve as reliable biomarkers for asthma severity. Proper image analysis for the publicly available microarray transcriptomics data increased its usefulness to decipher asthma and identify genuine differentially expressed genes that can be validated across different datasets.

关键词： Respiratory system Diseases Probes Gene expression Pipelines Bioinformatics

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3d synthetic aperture radar image 4

3d synthetic aperture radar image

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4th International conference on Information Technology and Nanotechnology - Session: image processing and Earth Remote Sensing, IPERS-ITNT 2018

作者： Leukhin, A.N. Rozentsov, A.A. Bezrodnyy, V.I. Voronin, A.A. Karasev, D.Yu. Kokovihina, N.A. Mari State University Lenin square 1 Yoskar-Ola424000 Russia Volga State University of Technology Lenin square 3 Yoskar-Ola424000 Russia

Synthetic aperture radar (SAR) is a coherent active microwave imaging method. In remote sensing it is used for mapping the scattering properties of the Earth's surface in the respective wavelength domain. The algorithms for the formation of 3D radar images in multiposition interferometric systems for remote sensing of the Earth are considered. Examples of reconstruction of the relief map for systems with one and two transmit antenna are presented. © 2018 CEUR-WS. All rights reserved.

关键词： Remote sensing

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Convolutional Neural Networks: Estimating Relations in the Ising Model on Overfiting 17

Convolutional Neural Networks: Estimating Relations in the I...

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17th IEEE International conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Gavrilov, Andrei Jordache, Alex Vasdani, Maya Deng, Jack Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781538633601

A CNN (Convolutional Neural Network) is one of actively researched and broadly applied deep machine learning methods. A CNN is composed of a feed-forward neural network that takes in images as inputs, and outputs a probability value associated to a class that best describes the image. As well, it is constructed of multiple layers, which include convolutional, max-pooling and fully connected layers. However, the training set has a large influence on the accuracy of a network, and hence it is paramount to create a network architecture that prevents overfitting (when a trained model cannot differentiate newly input data from its test data) and underfitting (the inability of a model to find relationships among inputs). This paper addresses the above deficiencies by comparing the statistics of CNN image recognition algorithms to the Ising model. The Ising model consists of magnetic dipole moments that can be in one of two states: +1 or -1. Using a two-dimensional square-lattice array once a training set of such data is complete, we determine the impact that network parameters, specifically learning rate and regularization rate, have on the adaptability of convolutional neural networks for image classification. Our results not only contribute to a better theoretical understanding of a CNN, but also provide concrete guidance on preventing model overfitting and underfitting when a CNN is applied for image recognition.

关键词： Machine Learning Convolutional Neural Network image processing Ising Model Data Overfitting Learning Rate Regularization Rate Cognitive systems

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Robust Feature Descriptors for Object Segmentation Using Active Shape Models 19th

Robust Feature Descriptors for Object Segmentation Using Act...

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19th International conference on Advanced Concepts for Intelligent Vision systems (ACIVS)

作者： Medley, Daniela Santiago, Carlos Nascimento, Jacinto C. Inst Super Tecn Inst Syst & Robot Lisbon Portugal

ISBN: (纸本)9783030014490;9783030014483

Object segmentation is still an active topic that is highly visited in image processing and computer vision communities. This task is challenging due not only to difficult image conditions (e.g., poor resolution or contrast), but also to objects whose appearance vary significantly. This paper visits the Active Shape Model (ASM) that has become a widely used deformable model for object segmentation in images. Since the success of this model depends on its ability to locate the object, many detectors have been proposed. Here, we propose a new methodology in which the ASM search takes the form of local rectangular regions sampled around each landmark point. These regions are then correlated to variable or fixed texture templates learned over a training set. We compare the performance of the proposed approach against other detectors based on: (i) the classical ASM edge detection;(ii) the Histogram of Oriented Gradients (HOG);and (iii) the Scale-Invariant Feature Transform (SIFT). The evaluation is performed in two different applications: facial fitting and segmentation of the left ventricle (LV) in cardiac magnetic resonance (CMR) images, showing that the proposed method leads to a significant increase in accuracy and outperforms the other approaches.

关键词： Active shape models image segmentation Texture regions Histogram of oriented gradients Scale-invariant feature transform

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