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Polychromatic image fusion algorithm and fusion metric for automatized microscopes -: art. no. 093201

OPTICAL ENGINEERING 2005年第9期44卷

作者： Bueno, MA Alvarez-Borrego, J Acho, L Chávez-Sánchez, MC IPN CITEDI San Diego CA USA CICESE Direcc Telemat Ensenada 22860 Baja California Mexico Ctr Invest Alimentac & Desarrollo AC Unidad Mazatlan Acuicultura & Manejo Ambiental Mazatlan 82010 Sinaloa Mexico

We propose a new algorithm to determine the multifocus image fusion from several polychromatic images captured from the best focusing region where the best in focus image is included from a biological sample. This focusing region is built by including several images up and down starting from the Z position of the best image in focus. These captured RGB images are converted to YCbCr color space to have the color CbCr and intensity Y channels separated with the objective to preserve the color information of the best in focus image. Several approaches have been developed to fuse images, like those algorithms based on the wavelets transform, Laplacian, ratio, contrast or morphological pyramids selection, fusion by averaging, Bayesian methods, fuzzy sets, and artificial networks. However, this algorithm utilizes the Fourier approach by using the Y channel frequency content via analyzing the Fourier coefficients to retrieve the high frequencies to obtain the best possible characteristics of every captured image. After the completion of this process, we continue to construct the fused image with these coefficients and color information for the optimum in focus image in the YCbCr color space;as a result, we obtain a precise final RGB fused image. (c) 2005 Society of Photo-Optical Instrumentation Engineers.

关键词： automatized microscope fusion algorithms multifocus image fusion YCbCr color space Fourier transform fusion metrics fusion quality measures

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Algorithm Research Based on 2D LiDAR-Binocular Camera fusion 6

Algorithm Research Based on 2D LiDAR-Binocular Camera Fusion

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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Wang, Chuyue Wang, Meiling Zhan, Wenfeng Ye, Xiaodong Chinese Acad Sci Hefei Inst Phys Sci Hefei Anhui Peoples R China Univ Sci & Technol China Hefei Hefei Anhui Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

Autonomous navigation algorithms for mobile robots are widely used. At present, the mainstream civil indoor mobile robot autonomous navigation algorithms are using low cost 2D LiDAR, which obtains less environmental information. In this paper, a lower-cost binocular camera and 2D LiDAR are used as sensors to obtain more environmental information. Firstly, the binocular camera is calibrated and fitted with depth error. Secondly, the 3D information of the binocular camera is intercepted and projected to obtain two-bit point cloud data. Finally, the processed 2D camera point cloud data is fitted with the LiDAR point cloud for interpolation, and the fused 2D point cloud is obtained for autonomous robot navigation. The experimental verification can determine that this algorithm has significantly improved the autonomous navigation effect compared to using 2D LiDAR alone.

关键词： Autonomous Navigation fusion algorithms Lidar Binocular depth camera

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Overcoming adverse weather conditions with a common optical path, multiple sensors and intelligent image fusion system

Overcoming adverse weather conditions with a common optical ...

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Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications

作者： Ng, Joseph Piacentino, Michael Caldwell, Brian Panavis Fed Syst LLC Suite 84021300 Victory Blvd Woodland Hills CA 91367 USA Sarnoff Corp Princeton NJ 08540 USA

ISBN: (纸本)9780819471376

Mission success is highly dependent on the ability to accomplish Surveillance, Situation Awareness, Target Detection and Classification, but is challenging under adverse weather conditions. This paper introduces an engineering prototype to address the image collection challenges using a Common Optical Path, Multiple Sensors and an Intelligent Image fusion System, and provides illustrations and sample fusion images. Panavision's advanced wide spectrum optical design has permitted a suite of imagers to perform observations through a common optical path with a common field of view, thereby aligning images and facilitating optimized downstream image processing. The adaptable design also supports continuous zoom or Galilean lenses for multiple field of views. The Multiple Sensors include: (1) High-definition imaging sensors that are small, have low power consumption and a wide dynamic range;(2) EMCCD sensors that transition from daylight to starlight, even under poor weather conditions, with sensitivity down to 0.00025 Lux;and (3) SWIR sensors that, with the advancement in InGaAs, are able to generate ultra-high sensitivity images from 1-1.7 mu m reflective light and can achieve imaging through haze and some types of camouflage. The intelligent fusion of multiple sensors provides high-resolution color information with previously impossible sensitivity and contrast. With the integration of Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs), real-time Image Processing and fusion algorithms can facilitate mission success in a small, low power package.

关键词： surveillance common optical path common field of view continuous zoom multiple sensor intelligent image fusion real-time image processing fusion algorithms

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Functional clusters analysis and research based on differential coexpression networks

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BIOTECHNOLOGY & BIOTECHNOLOGICAL EQUIPMENT 2018年第1期32卷 171-182页

作者： Meng, Shuai Liu, Guixia Su, Lingtao Sun, Liyan Wu, Di Wang, Lingwei Zheng, Zhao Jilin Univ Coll Comp Sci & Technol Changchun Jilin Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun Jilin Peoples R China

Differential coexpression analysis has gradually become an important approach to improve the conventional method of analysing differentially expressed genes. With this approach, it is possible to discover disease mechanisms and underlying regulatory dynamics which remain obscure in differential expression analysis. The detection of differential coexpression links and functional clusters between different disease states is a demanding task. Nevertheless, there is no gold standard for detecting differential coexpression links and functional clusters. Consequently, we developed a novel fusion algorithm FDvDe (fusion of differential vertex and differential edge) to detect differential coexpression links by aggregating the set of 'differential vertex' and 'differential edge.' Then, we constructed differential coexpression networks between normal and tumour states by integrating the differential coexpression links. With this approach, we identified 1823 genes and 29370 links. Then, we developed the algorithms GTHC (GO term hierarchical clusters) to identify functional modules. The distance matrix used in the hierarchical process was formed by the GO semantic similarity. Furthermore, we aggregated the densities among clusters describing the connectivity among clusters and topological property analysis to discover the hub genes and hub pathways which play an important role in disease mechanism. In this paper, we showed that our approach worked well on a data set of breast cancer samples (68 tumour samples) and normal samples (73 normal samples), and revealed the crucial role and biological significance of the modules and hub genes found in this approach.

关键词： Differential coexpression network fusion algorithms GO hierarchical clusters topological property analysis hub gene

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A forecasting solution to the oil spill problem based on a hybrid intelligent system

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INFORMATION SCIENCES 2010年第10期180卷 2029-2043页

作者： Baruque, Bruno Corchado, Emilio Mata, Aitor Corchado, Juan M. Univ Salamanca Dept Informat & Automat E-37008 Salamanca Spain Univ Burgos Dept Civil Engn Burgos 09006 Spain

Oil spills represent one of the most destructive environmental disasters. Predicting the possibility of finding oil slicks in a certain area after an oil spill can be critical in reducing environmental risks. The system presented here uses the Case-Based Reasoning (CBR) methodology to forecast the presence or absence of oil slicks in certain open sea areas after an oil spill. CBR is a computational methodology designed to generate solutions to certain problems by analysing previous solutions given to previously solved problems. The proposed CBR system includes a novel network for data classification and retrieval. This type of network, which is constructed by using an algorithm to summarize the results of an ensemble of Self-Organizing Maps, is explained and analysed in the present study. The Weighted Voting Superposition (WeVoS) algorithm mainly aims to achieve the best topo-graphically ordered representation of a dataset in the map. This study shows how the proposed system, called WeVoS-CBR, uses information such as salinity, temperature, pressure, number and area of the slicks, obtained from various satellites to accurately predict the presence of oil slicks in the north-west of the Galician coast, using historical data. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Case-Based Reasoning Oil spill Self organizing memory Radial Basis Function Ensembles fusion algorithms

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Multimodal recognition of pain intensity and pain modality with machine learning

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SCHMERZ 2020年第5期34卷 400-409页

作者： Walter, S. Al-Hamadi, A. Gruss, S. Frisch, S. Traue, H. C. Werner, P. Univ Klinikum Ulm Klin Psychosomat Med & Psychotherapie Sekt Med Psychol Frauensteige 6 D-89075 Ulm Germany Otto von Guericke Univ Inst Informat & Kommunikat Tech Fachgebiet Neuroinformat Tech Magdeburg Germany Praxis Neurol & Psychiat Leutkirch Germany

Background The objective recording of subjectively experienced pain is a problem that has not been sufficiently solved to date. In recent years, data sets have been created to train artificial intelligence algorithms to recognize patterns of pain intensity. The multimodal recognition of pain with machine learning could provide a way to reduce an over- or undersupply of analgesics, explicitly in patients with limited communication skills. Objectives This study investigated the methodology of automated multimodal recognition of pain intensity and modality using machine-learning techniques of artificial intelligence. Multimodal recognition rates of experimentally induced phasic electrical and heat pain stimuli were compared with uni- and bimodal recognition rates. Material and methods On the basis of the X-ITE Pain Database, healthy subjects were stimulated with phasic electro-induced pain and heat pain, and their corresponding pain responses were recorded with multimodal sensors (acoustic, video-based, physiological). After complex signal processing, machine-learning methods were used to calculate recognition rates with respect to pain intensity (baseline vs. pain threshold, pain tolerance, mean value of pain threshold and tolerance) and pain modality (electrical vs. heat). Finally, a statistical comparison of uni- vs. multimodal and bi- vs. multimodal detection rates was performed. Results With few exceptions, multimodal recognition of pain intensity rates was statistically superior to unimodal recognition rates, regardless of the pain modality. Multimodal pain recognition distinguished significantly better between heat and electro-induced pain. Further, multimodal recognition rates were predominantly superior to bimodal recognition rates. Conclusion Priority should be given to the multimodal approach to the recognition of pain intensity and modality compared with unimodality. Further clinical studies should clarify whether multimodal automated recognition of pain in

关键词： Automated pain recognition Machine learning Artificial intelligence Multimodality fusion algorithms

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Obstacle information detection method based on multiframe three-dimensional lidar point cloud fusion

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OPTICAL ENGINEERING 2019年第11期58卷 116102-116102页

作者： Li, Jing Li, Rui Wang, Junzheng Yan, Min Beijing Inst Technol Sch Automat Key Lab Complex Syst Intelligent Control & Decis Beijing Peoples R China Beijing Inst Technol Sch Automat Beijing Peoples R China

An obstacle detection method based on multiframe point cloud fusion and ground plane estimation is proposed. First, the robot, three-dimensional lidar, and two-axis rotating platform motion models were established. With the pitch rotation of lidar and the movement of the robot, the multiframe point cloud data were superposed to obtain a dense point cloud. Then, the incremental linear fitting method was adopted to estimate the ground plane to eliminate the ground disturbance. Finally, the pairwise linkage clustering method could cluster multiple obstacles and the projection method could obtain the specific size information of obstacles. Therefore, verification of experiments was carried on the Robotics Lab Platform (BIT-NAZA), experimental results showed that multiframe point cloud data can be accurately integrated to obtain dense point cloud and then get information of obstacles. For some low-height obstacles with sparse density of point clouds, the detection accuracy of an object with a height of about 10 cm is about 95%, whereas for other higher objects, the detection rate will be higher. So the proposed algorithm can achieve its detection and related parameter acquisition efficiently. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： obstacle detection fusion algorithms ground plane estimation lidar mobile robots

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Comparison of Attitude and Heading Reference Systems using Foot Mounted MIMU Sensor Data: Basic, Madgwick and Mahony

Comparison of Attitude and Heading Reference Systems using F...

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Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems

作者： Ludwig, Simone A. Burnham, Kaleb D. Jimenez, Antonio R. Touma, Pierre A. North Dakota State Univ Fargo ND 58105 USA UPM CSIC CAR Madrid Spain Axelo Inc Austin TX USA

ISBN: (数字)9781510616936

ISBN: (纸本)9781510616936

A magnetic and inertial measurement unit (MIMU) usually measures acceleration, rotation rate, and earth's magnetic field in order to determine a body's attitude. In order to find the orientation information using all sensors a fusion algorithm is used. This paper compares two approaches used for a Attitude and Heading Reference System (AHRS), namely Madgwick and Mahony with a basic fusion approach. Foot mounted MIMU data is used to estimate the Euler angles as well as the position. The results show that Madgwick obtains better heading orientation than Mahony and the basic AHRS approach in terms of the error (RMSE) of the Euler angles when compared to the ground truth. However, the execution time of Mahony is less than Madgwick with the basic AHRS taking the longest.

关键词： magnetic and inertial measurement unit fusion algorithms foot-mounted sensor data

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Designing an error model for navigation sensors using the Bayesian approach

Designing an error model for navigation sensors using the Ba...

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IEEE International Conference on Multisensor fusion and Integration for Intelligent Systems (MFI)

作者： Motorin, Andrei V. Stepanov, Oleg A. Cent State Res Inst Elektropribor JSC St Petersburg Russia Univ ITMO St Petersburg Russia

ISBN: (纸本)9781479977727

The paper considers a problem of designing error models for navigation sensors the data of which are used in fusion algorithms. Within the Bayesian approach, an algorithm is proposed that allows not only estimation of model parameters, but also identification of its structure. As a result of solving the joint problem of hypotheses recognition and parameter estimation, a shaping filter describing sensor errors is designed. An example is given to illustrate the performance of the proposed algorithm.

关键词： Bayes methods filtering theory parameter estimation sensor fusion Bayesian approach error model designing fusion algorithms hypotheses recognition model parameter estimation navigation sensors shaping filter Algorithm design and analysis Covariance matrices Estimation error Kalman filters Navigation Sensors Bayes methods Parameter estimation Algorithm design and analysis error model navigation filtering theory shaping filter Estimation error Sensors Kalman filters Sensor fusion variance covariance matrix Covariance matrix

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Comparison of the On-line Power Detection and Prediction System

Comparison of the On-line Power Detection and Prediction Sys...

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2nd International Conference on Mechatronics and Applied Mechanics (ICMAM2012)2nd International Conference on Mechatronics and Applied Mechanics (ICMAM2012)

作者： Guo, J. H. Su, K. L. Liao, Y. L. Natl Yunlin Univ Sci & Technol Grad Sch Engn Sci & Technol Yunlin Taiwan Natl Yunlin Univ Sci & Technol Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9783037856512

The article presents a power detection and prediction system (PDPS) using fusion algorithms to be applied in the on-line power monitoring of the target device. The system contains multiple power detection units, a data integration unit, a target device, a power source and a main controller. Each power detection unit measures the assigned power source on real-time, and uses four current sensors to measure the current variety. We use fusion algorithms to be applied in current and voltage detection. We can calculate the real-time power values according to the estimated values of current and voltage measurement values. The main controller predicts the power loading for each power detection unit using auto-regression algorithm, and calculates the error value between the prediction value and the measurement value for each detection value, and compares the relation value on various condition.

关键词： power detection and prediction system fusion algorithms auto-regression algorithm

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