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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

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AGV Navigation Based on AprilTags2 Auxiliary Positioning

AGV Navigation Based on AprilTags2 Auxiliary Positioning

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Yunyun Wang Shaojin Liu Lianming Wang Jipeng Huang Institute of computational intelligence Northeast Normal University Changchun Jilin China School of Electrical and Computer Engineering Purdue University West Lafayette USA School of Ocean Science and Technology Hainan Tropical Ocean University Hainan Sanya China

Aiming at the problems of poor flexibility, complicated path maintenance and poor positioning performance in the current guidance technology of AGV, this paper designs and implements a new navigation system of automated guided vehicle (AGV) based on AprilTags2 auxiliary positioning. The system uses the Robot Operating System (ROS) as a platform to develop AGV navigation functions. The navigation system comprises two parts: the hardware and software layers. Firstly, hardware selection is performed after considering the actual requirements, performance, cost, and other factors in the hardware layer. Simultaneously, the AGV chassis and single-steering wheel walking mechanism are built to provide a stable and flexible operating platform for the software layer. Secondly, the software layer design includes two parts, namely the ROS navigation planning end and AprilTags2 detection. The ROS planning navigation end performs the design of four functional modules (i.e., AGV map construction, autonomous positioning, path planning, and path tracking), while the AprilTags2 detection part obtains the visual positioning pose of AGV by setting the AprilTags2 on each site and using the Kinect1 camera to detect. A more accurate AGV pose can be obtained by merging the former pose with the kinematic estimated pose. Finally, the two groups of positioning errors of the system before and after adding the AprilTags2 auxiliary positioning are tested and compared. The experimental results show that the navigation accuracy of the proposed system with AprilTags2 auxiliary positioning is significantly improved.

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Robust Infrared Pedestrian Detection via GMR and Logistic Regression Based ROIs Extraction 11

Robust Infrared Pedestrian Detection via GMR and Logistic Re...

引用

11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Wang, Jiangtao Chen, Yan Wang, Weiwei Li, Huaijiang Huaibei Normal Univ Sch Phys & Elect Informat Huaibei Peoples R China

ISBN: (纸本)9781538676042

Real-time detection of persons via infrared camera is a potential task for advanced driver assistance systems (ADAS). In this paper, we develop a simple and effective pedestrian detection strategy. To robustly locate the region of interest (ROI), we first design a gradient based descriptor which called gradient magnitude ratio (GMR) to depict the pedestrian target with very low computing cost. Then a logistic regression (LR) classifier is trained to identify the ROIs. After this coarse detection step, about one thousand ROIs is obtained in the experiments. Finally, the detected candidates are feed into a traditional HOG-SVM based classifying framework to gain the fine detection results. To furtherly enhance the real-time performance of the proposed system, PCA based dimension reduction is performed on the original HOG feature. Experiment results show that the proposed method could detect pedestrians effectively with realtime speed under challenging circumstances such as dynamic background, low contrast and various scales.

关键词： pedestrian detection infrared image logistic regression gradient magnitude ratio

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A Novel Method of Synthetic CT Generation from MR images based on Convolutional Neural Networks 11

A Novel Method of Synthetic CT Generation from MR Images bas...

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11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Li, Guang Bai, Lu Zhu, Chuanwei Wu, Enhe Ma, Ruibing Neusoft Med Syst Co Ltd Precis Image Tech R&D Dept Shenyang Liaoning Peoples R China

ISBN: (纸本)9781538676042

The synthesis of medical images from one modality to another is an intensity transformation between two images acquired from different medical devices, such as Magnetic Resonance (MR) image synthesis to Computed Tomography (CT) image, or MR T1 weighted (T1W) image to T2 weighted (T2W) or proton density weighted (PDW) image. MR based synthetic CT is very useful for some clinical cases, such as PET attenuation correction for PET/MR, MR/CT registration etc. In this paper, we propose a novel method based on fully convolutional networks (FCN) to generate synthetic CT image from MR image. We adopt a u-net like structure FCN model for image regression from MR image to CT image. To achieve better result, there are some key steps proposed. Firstly, the preprocessing step, normalize MR and CT image intensity using mean value of brain tissue. Secondly, Propose the tissue focused loss function for convolutional neural networks (CNN) regression. Thirdly, we adopt three orthogonal planner convolution instead 3D convolution to avoid huge computation however keep 3D structure information. Fifteen brain MR and CT data pairs are analyzed. Experiment results show that our method can accurately synthesize CT images in various scenarios, even for the images with large rotation or lesion, and it is also extended to get accurate results between different MR sequence images synthesis.

关键词： image synthesis convolutional neural networks MR CT fully convolutional networks

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Research on Real-Time Graphics Drawings Technology in Virtual Scene 11

Research on Real-Time Graphics Drawings Technology in Virtua...

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11th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2018

作者： Liu, Ping-Ping Wu, Yu-Chen Xi' An Technological University School of Computer Science and Engineering Xi' an China

ISBN: (纸本)9781538676042

The drawing technology of real-time graphics in virtual scenes has been continuously upgraded. The purpose of this study is to study the rapid generation algorithm of virtual campus models. Based on the analysis of the current existing Z-buffer blanking algorithm using two depth cache variables, virtual reality is used. Technology (VRML), 3D panoramic technology and computer network technology to build a virtual campus, using OpenGL programming, 3D max modeling technology, design and implementation of a virtual campus panorama roaming model to generate a Z buffer Buffering algorithm using a deep cache variable to solve To observe three-dimensional objects in a certain point of view, you can see the dotted surface of the surface of the object, and the other parts may be occluded by these parts;this algorithm can effectively blank the complex scene target virtual scene. The model was optimized and the realistic campus panorama environment was simulated using computer technology to realize a friendly and interactive virtual campus roaming system. © 2018 IEEE.

关键词： 3D modeling

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A Deep Residual Neural Network Based Framework for Epileptogenesis Detection in a Rodent Model with Single-Channel EEG Recordings

A Deep Residual Neural Network Based Framework for Epileptog...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Diyuan Lu Sebastian Bauer Valentin Neubert Lara Sophie Costard Felix Rosenow Jochen Triesch Frankfurt Institute for Advanced Studies Frankfurt am Main Germany Neurology and Epilepsy Center Frankfurt Rhine-Main University Hospital Goethe-University Frankfurt am Main Germany Universitätsmedizin Rostock Oscar-Langendorff-Institut für Physiologie Rostock Germany Tissue Engineering Research Group Royal College of Surgeons Ireland Dublin Ireland

Epilepsy is one of the most common neurological disorders affecting patients across all ages. During the progression of the disease, termed epileptogenesis (EPG), patients may not yet show any clinical manifestation. The EPG phase can range from weeks to years and patients with epilepsy are usually diagnosed by the occurrence of a spontaneous seizure followed by electroencephalography (EEG) monitoring in the hospital. However, the more seizures they have, the less effective the treatment will be. Detecting the development of epilepsy before the first spontaneous s eizure may a llow for e arlier intervention and better treatment outcome. Here we propose a framework based on deep residual neural networks to identify the EPG phase based on EEG recordings in a rodent model where the epilepsy is induced by perforant pathway stimulation (PPS). A deep convolutional neural network is trained to distinguish EEG data recorded before (baseline period, BL) and after (epileptogenesis period, EPG) the EPG is triggered. The proposed model takes the Fast Fourier Transform (FFT) of the preprocessed five-second long EEG segments as input. During testing, we apply a prediction aggregation across multiple consecutive segments to accumulate information over a longer time period. When classifying a continuous stretch of one hour of data, our model achieves 83 % sensitivity and 83 % specificity. Further analysis suggests interpretable features in the FFT transformed data that contribute to the distinction of the two phases.

关键词： Electroencephalography Epilepsy Brain modeling Rodents Rats Residual neural networks Biological system modeling

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An Improved Otsu Multi-threshold image Segmentation Algorithm Based on Pigeon-Inspired Optimization 11

An Improved Otsu Multi-threshold Image Segmentation Algorith...

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11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Liu, Wei Shi, Heng Pan, Shang Huang, Yongkun Wang, Yingbin Hubei Univ Technol Sch Comp Sci Wuhan Hubei Peoples R China Hubei Univ Technol Sch Civil Engn Architecture & Environm Wuhan Hubei Peoples R China

ISBN: (纸本)9781538676042

Threshold segmentation is a simple and effective method in the field of image segmentation which has the widest application domain. And the improvement of efficiency and precision of the threshold segmentation has received extensive attention and research. Inspired with the bio-inspired intelligent optimization, this paper proposes an Otsu multi-threshold segmentation based on pigeon-inspired optimization. The basic idea of this method is: the Otsu multi-threshold segmentation method is used to design the objective function, and the interclass variance function is used as the fitness function. The iterative optimization process is performed by the pigeon-inspired optimization. In this process, the fitness function is used as a criterion for the solution and corresponds to the coordinate of pigeon in the pigeon-inspired optimization. The best segmentation threshold group is obtained when the pigeon finds the global best position. This method converts the problem of finding the optimal solution into the solving problem of multi-dimensional variables and effectively optimizes the solution process. For the purpose of verifying the feasibility and segmentation accuracy of this method, the multiple segmentation parameters of several classical images of this method are compared with parameters of other classic algorithms such as particle swarm optimization and fireworks algorithm. The experiments show that the improved Otsu segmentation method based on pigeon-inspired optimization can effectively improve the speed of threshold solution, and the double operators ensures the accuracy of the segmentation. The method has the advantages of superior convergence and convenience of implementation. Simultaneously, the segmentation effect is ideal with this modus.

关键词： component image segmentation Otsu Multi-threshold segmentation Pigeon-Inspired Optimization

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Development of New Calibration Standard for Noncontact Tonometers 11

Development of New Calibration Standard for Noncontact Tonom...

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11th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2018

作者： Pavlasek, Peter Chytil, Miroslav Rybar, Jan Palencar, Jakub Duris, Stanislav Dep.of Thermometry Slovak Institute of Metrology Bratislava Slovakia Faculty of Mechanical Engineering Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9781538676042

This paper deals with the problematic of eye tonometer measurements, more specifically the missing universal, independent standard for their routine checks and calibration. The paper furthermore presents the principles and initial results of newly developed standard that has the potential to be used for any non-contact eye tonometer device. The initial test measurements have shown a consistency of the generated pressure with increased divergence measured by tow selected contact and non-contact tonometers. © 2018 IEEE.

关键词： Calibration

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Occlusion Robust Tracking for Multiple Faces with Wavelet Packet Transform Feature and BP Neural Network 11

Occlusion Robust Tracking for Multiple Faces with Wavelet Pa...

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11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Zhao, Hui-huang Liu, Han Zheng, Jin-Hua Fu, Bin Hengyang Normal Univ Coll Comp Sci & Technol Hengyang Peoples R China Hunan Prov Key Lab Intelligent Informat Proc & Ap Hengyang Hunan Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff S Glam Wales Univ Texas Rio Grande Valley Dept Comp Sci Edinburg TX USA

ISBN: (纸本)9781538676042

This paper presents an occlusion robust tracking (ORT) method for multiple faces tracking. Given a video having multiple faces, we firstly detect faces in the first frame using the off-the-shelf face detector, and then extract wavelet packet transform (WPT) coefficients and color features from the detected faces, finally we design a back propagation (BP) neural network and track the faces by a particle filter and BP neural network. The main contribution is twofold. Firstly, the WPT coefficients combined with traditional color features is utilized to face tracking. It efficiently describes faces due to their discrimination and simplicity. Secondly, we propose an improved tracking method for occlusion robust tracking based on the BP neural network. When there is an occlusion, BP neural network learns from previous tracking results and is utilized to refine the current result from particle filter. Experimental results have been shown that our ORT method can handle the occlusion effectively and achieve better performance than several previous methods.

关键词： Target tracking Neural networks image color analysis Wavelet packets Feature extraction Color Face detection

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Human Pose Estimation via Multi-Scale Intermediate Supervision Convolution Network

Human Pose Estimation via Multi-Scale Intermediate Supervisi...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Zhuohao Mai Xiao Hu Shaohu Peng Yunshan Wei School of Mechanical and Electrical Engineering Guangzhou University Guangzhou China

Human pose estimation is a technique for locating the key points of a human in the picture and video. It can be used in the fields of human-computer interaction, motion recognition, Character behavior analysis and so on. At present, the supervision information is a series of single-scale heat map without matching the multi-scale key points of ground truth while training the CNN model. This type of supervision makes it more likely that the predicted key points will deviate from the real position. To improve the prediction accuracy, this paper proposes a novel model named human pose estimation via multi-scale intermediate supervision convolution network. Heatmaps are generated on three scale which are determined by the standard deviation of 2D Gaussian distribution. Residual network model is composed of three stage using ResNet50 as the backbone network. Each stage includes a ResNet50 and three deconvolution layers. The output from ResNet50 in three stages corresponds to the heat maps annotation of large, medium and small sizes respectively, and the intermediate supervision is realized twice in the output of the first and second stages. In the test phase, the output from the last stage is used for calculating the final key point's coordinates by non-maximum suppression. To demonstrate effectiveness of our network, two benchmark datasets are used for training and testing: the key point detection subset of COCO dataset and MPII Human Pose dataset. The test result of PCK@0.1 reached 37.2% on the MPII validation dataset, which is 2.1% higher than other methods. The results of the mAP test on the COCO validation dataset reached 75.5%, an increase of 1.2% compared with other methods. The results indicate that the multi-scale relay supervised convolutional network model proposed in this paper can reduce the influence of the non-correspondence between the size of key points and the size of heatmap ground truth in human pose estimation, thus improving the accuracy and achievin

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The quaternion-based anisotropic gradient for the color images 17

The quaternion-based anisotropic gradient for the color imag...

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17th image processing: Algorithms and Systems Conference, IPAS 2019

作者： Voronin, V. Zelensky, A. Agaian, S. Don State Technical University Lab. «Mathematical Methods of Image Processing and Computer Vision Intelligent Systems» Gagarina 1 Rostov on Don Russia Moscow State University of Technology"STANKIN" Moscow Russia CUNY College of Staten Island Dept. of Computer Science New York United States

image gradient, as a preprocessing step is an essential tool in image processing in many research areas such as edge detection, segmentation, smoothing, inpainting, etc. In the present paper, we develop a new gradient by integrated the quaternion framework with LPA-ICI (local polynomial approximation - the intersection of confidence intervals) based on anisotropic gradient concepts for the color image processing applications. Computer simulations on the Berkeley Segmentation Dataset show that the new quaternion anisotropic gradient exhibits fewer color artifacts compared to state-of-the-art techniques including well-knowledge gradient operators. © 2019, Society for Imaging Science and Technology.

关键词： Polynomial approximation

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