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

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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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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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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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Exoplanet detection in angular and spectral differential imaging: local learning of background correlations for improved detections 6

Exoplanet detection in angular and spectral differential ima...

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conference on Adaptive Optics systems vi

作者： Flasseur, Olivier Denis, Loic Thiebaut, Eric Langlois, Maud Univ Lyon UJM St Etienne CNRS Inst OptGrad SchLab Hubert CurienUMR 5516 F-42023 St Etienne France Univ Lyon1 Univ Lyon ENS Lyon CNRSCtr Rech Astrophys LyonUMR 5574 F-69230 St Genis Laval France

ISBN: (数字)9781510619609

ISBN: (纸本)9781510619609

The search for new exoplanets by direct imaging is a very active research topic in astronomy. The detection is particularly challenging because of the very high contrast between the host star and the companions. They thus remain hidden by a nonstationary background displaying strong spatial correlations. We propose a new algorithm named PACO (for PAtch COvariances) for reduction of differential imaging datasets. Contrary to existing approaches, we model the background correlations using a local Gaussian distribution that locally captures the spatial correlations at the scale of a patch of a few tens of pixels. The decision in favor of the presence or the absence of an exoplanet in then performed by a binary hypothesis test. The method is completely parameter-free and produces both stationary and statistically grounded detection maps so that the false alarm rate, the probability of detection and the contrast can be directly assessed without postprocessing and/or Monte-Carlo simulations. We describe in a forthcoming paper its detailed principle and implementation. In this paper, we recall the principle of the PACO algorithm and we give new illustrations of its bene fits in terms of detection capabilities on datasets from the VLT/SPHERE-IRDIS instrument. We also apply our algorithm on multi-spectral datasets from the VLT/SPHERE-IFS spectrograph. The performance of PACO is compared to state-of-the-art algorithms such as TLOCI and KLIP-PCA.

关键词： direct imaging angular and spectral differential imaging data reduction processing algorithm statistical method

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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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Air, Telescope, and Instrument Temperature Effects on the Gemini Planet imager's image Quality 6

Air, Telescope, and Instrument Temperature Effects on the Ge...

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conference on Adaptive Optics systems vi

作者： Tallis, Melisa Bailey, Vanessa P. Macintosh, Bruce Chilcote, Jeffrey K. Poyneer, Lisa A. Ruffio, Jean-Baptiste Hayward, Thomas L. Savransky, Dmitry Stanford Univ Dept Phys Kavli Inst Particle Astrophys & Cosmol Stanford CA 94305 USA CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA Lawrence Livermore Natl Lab 7000 East Ave Livermore CA 94550 USA Gemini Observ Casilla 603 La Serena Chile Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA

ISBN: (数字)9781510619609

ISBN: (纸本)9781510619609

The Gemini Planet imager (GPI) is a near-infrared instrument that uses Adaptive Optics (AO), a coronagraph, and advanced data processing techniques to achieve very high contrast images of exoplanets. The GPI Exoplanet Survey (GPIES) is a 600 stars campaign aiming at detecting and characterizing young, massive and self-luminous exoplanets at large orbital distances (> 5 au). Science observations are taken simultaneously with environmental data revealing information about the turbulence in the telescope environment as well as limitations of GPI's AO system. Previous work has shown that the timescale of the turbulence, To, is a strong predictor of AO performance, however an analysis of the dome turbulence on AO performance has not been done before. Here, we study correlations between image contrast and residual wavefront error (WFE) with temperature measurements from multiple locations inside and outside the dome. Our analysis revealed GPI's performance is most correlated with the temperature difference between the primary mirror of the telescope and the outside air. We also assess the impact of the current temperature control and ventilation strategy at Gemini South (GS).

关键词： GPI,high contrast imaging adaptive optics local seeing mirror seeing

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ULBP-RF: A Hybrid Approach for Malware image Classification 5

ULBP-RF: A Hybrid Approach for Malware Image Classification

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5th IEEE International conference on Parallel, Distributed and Grid Computing (PDGC)

作者： Gupta, Shakshi Bansal, Priti Kumar, Sumit Netaji Subhas Inst Technol Dept IT New Delhi India KIET Grp Inst Dept IT Ghaziabad India

ISBN: (纸本)9781728106465

The growing dependence on internet for performing critical activities in every domain has raised serious concerns about the security of the computer systems. Maiwares have become a significant threat to computer systems and recently, a massive growth has been observed by experts in the number and sophistication of new malwares. Therefore, the task of malware detection and classification is of utmost importance. However, the task of classifying malwares has become more challenging since the introduction of code obfuscation and metamorphism techniques. These techniques easily alter the malwares' code signatures and make the static techniques of malware detection ineffective. Dynamic analysis is effective but time-consuming. Recently, image processing techniques along with machine learning techniques have been explored by researchers for visualization and classification of malwares. In this paper, we introduce a new approach ULBP-RF that uses Uniform local binary pattern with circular neighborhood strategy to extract features from the malware images dataset. The resulting datasets are classified using Random Forest. To assess the performance of the proposed approach, experiments are performed and a comparative analysis of the performance of various combinations of feature extraction techniques and classification algorithms is done. It has been found that ULBP-RF has the highest classification accuracy.

关键词： Malware Uniform Local Binary Pattern feature extraction Classification

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Real Time Incremental Foveal Texture Mapping for Autonomous Vehicles

Real Time Incremental Foveal Texture Mapping for Autonomous ...

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25th IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Kumar, Ashish McBride, James R. Pandey, Gaurav Indian Inst Technol Dept Elect Engn Kanpur Uttar Pradesh India Ford Motor Co Res & Innovat Ctr Dearborn MI 48121 USA

ISBN: (纸本)9781538680940

We propose an end-to-end real time framework to generate high resolution graphics grade textured 3D map of urban environment. The generated detailed map finds its application in the precise localization and navigation of autonomous vehicles. It can also serve as a virtual test bed for various vision and planning algorithms as well as a background map in the computer games. In this paper, we focus on two important issues: (i) incrementally generating a map with coherent 3D surface, in real time and (ii) preserving the quality of color texture. To handle the above issues, firstly, we perform a pose-refinement procedure which leverages camera image information, Delaunay triangulation and existing scan matching techniques to produce high resolution 3D map from the sparse input LIDAR scan. This 3D map is then texturized and accumulated by using a novel technique of ray-filtering which handles occlusion and inconsistencies in pose-refinement. Further, inspired by human fovea, we introduce foveal-processing which significantly reduces the computation time and also assists ray-filtering to maintain consistency in color texture and coherency in 3D surface of the output map. Moreover, we also introduce texture error (TE) and mean texture mapping error (MTME), which provides quantitative measure of texturing and overall quality of the textured maps.

关键词： Three-dimensional displays Laser radar Cameras Real-time systems image color analysis Global Positioning System

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