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Performance of diffuse reflectance infrared Fourier transform spectroscopy and chemometrics for detection of multiple adulterants in roasted and ground coffee

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LWT-FOOD SCIENCE AND technology 2013年第2期53卷 395-401页

作者： Reis, Nadia Franca, Adriana S. Oliveira, Leandro S. Univ Fed Minas Gerais PPGCA BR-31270901 Belo Horizonte MG Brazil Univ Fed Minas Gerais DEMEC BR-31270901 Belo Horizonte MG Brazil

The quality of roasted and ground coffee is an important issue since it has been the target of fraudulent admixtures with a variety of cheaper materials, including spent coffee grounds, coffee husks and other roasted grains. Given the successful application of Diffuse Reflectance infrared Fourier Transform Spectroscopy (DRIFTS) for discrimination between roasted coffee, corn and coffee husks, the objective of this work was to confirm the potential of such technique for discrimination between pure roasted coffee and coffee samples adulterated with coffee husks, corn, barley and spent coffee grounds, regardless of roasting conditions. Principal Components Analysis (PCA) was employed for selection of target spectra regions responsible for group discrimination. classification models were developed based on Linear Discriminant Analysis (LDA) and recognition and prediction abilities of these models were 100%, with the samples being separated into six groups: pure coffee, adulterated coffee (adulteration levels as low as 1 g/100 g), spent coffee grounds, coffee husks, corn and barley. Such results confirm that DRIFTS can be a valuable analytical tool for detection of adulteration in ground and roasted coffee. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Barley Corn Coffee adulteration Coffee husks Spent coffee grounds

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Human Pose classification within the Context of Near-IR Imagery Tracking

Human Pose Classification within the Context of Near-IR Imag...

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Conference on Optics and Photonics for Counterterrorism, Crime Fighting and Defence IX;and Optical Materials and Biomaterials in Security and Defence Systems technology X

作者： Han, Jiwan Gaszczak, Anna Maciol, Ryszard Barnes, Stuart E. Breckon, Toby P. Cranfield Univ Sch Engn Cranfield MK43 0AL Beds England

ISBN: (纸本)9780819497703

We address the challenge of human behaviour analysis within automated image understanding. Whilst prior work concentrates on this task within visible-band (EO) imagery, by contrast we target basic human pose classification in thermal-band (infrared, IR) imagery. By leveraging the key advantages of limb localization this imagery offers we target two distinct human pose classification problems of varying complexity: 1) identifying passive or active individuals within the scene and 2) the identification of individuals potentially carrying weapons. Both approaches use a discrete set of features capturing body pose characteristics from which a range of machine learning techniques are then employed for final classification. Significant success is shown on these challenging tasks over a wide range of environmental conditions within the wider context of automated human target tracking in thermal-band (IR) imagery.

关键词： Weapons Thermography Image classification infrared imaging Head Scene classification target detection Feature extraction Neural networks Visualization

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Single frame IR point target detection based on a Gaussian mixture model classification

Single frame IR point target detection based on a Gaussian m...

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Conference on Electro-Optical and infrared Systems - technology and Applications IX

作者： Genin, Laure Champagnat, Frederic Le Besnerais, Guy Astrium Satellites F-31402 Toulouse 4 France Onera French Aerosp Lab F-91761 Palaiseau France

ISBN: (纸本)9780819492821

This paper deals with point target detection in non-stationary background such as cloud scenes in aerial or satellite imaging. We propose a new method to estimate second order background statistics within a spatial detection method by Matched Filter (MF). Our approach consists in classifying the pixels of the image by gathering the pixels of similar covariance matrices and in estimating one covariance matrix for each class. We use a classification Expectation-Maximization (CEM) algorithm based on a zero mean Gaussian mixture model. A key issue is the robustness of the classification with regard to target presence. This problem is efficiently tackled in a very simple manner. The resulting Gaussian Mixture Matched Filter (GMMF) has the advantage to adapt itself to second order non-stationary backgrounds and requires only the tuning of the window size used to build the observation vector associated with each pixel. Efficiency of the proposed GMMF approach is demonstrated on a large variety of cloudy sky backgrounds.

关键词： Point target detection Gaussian Mixture Model Image Segmentation Matched Filter

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Hyperspectral Reconnaissance in Urban Environment

Hyperspectral Reconnaissance in Urban Environment

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Conference on infrared technology and Applications XXXIX

作者： Renhorn, Ingmar Achard, Veronique Axelsson, Maria Benoist, Koen Borghys, Dirk Briottet, Xavier Dekker, Rob Dimmeler, Alwin Friman, Ola Kasen, Ingebjorg Matteoli, Stefania Lo Moro, Maria Opsahl, Thomas Olsvik van Persie, Mark Resta, Salvatore Schilling, Hendrik Schwering, Piet Shimoni, Michal Haavardsholm, Trym Vegard Viallefont, Francoise Swedish Def Res Agcy Stockholm Sweden

ISBN: (纸本)9780819494955

Seven countries within the European Defence Agency (EDA) framework are joining effort in a four year project (20092013) on detection in Urban scenario using Combined Airborne imaging Sensors (DUCAS). Data has been collected in a joint field trial including instrumentation for 3D mapping, hyperspectral and high resolution imagery together with in situ instrumentation for target, background and atmospheric characterization. Extensive analysis with respect to detection and classification has been performed. Progress in performance has been shown using combinations of hyperspectral and high spatial resolution sensors.

关键词： Hyperspectral imaging Atmospheric compensation Georeferencing target detection Spectral matching

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Real-time target detection technology of large view-field infrared image based on multicore DSP parallel processing

Real-time target detection technology of large view-field in...

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8th Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Parallel Processing of Images and Optimization and Medical Imaging Processing

作者： Sun, Gang Liu, Songlin Wang, Weihua Chen, Zengping Natl Univ Def Technol Sci & Technol Automat Target Recognit Lab Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9780819498052

In order to implement real-time detection of hedgehopping target in large view-field infrared (LVIR) image, the paper proposes a fast algorithm flow to extract the target region of interest (ROI). The ground building region was rejected quickly and target ROI was segmented roughly through the background classification. Then the background image containing target ROI was matched with previous frame based on a mean removal normalized product correlation (MRNPC) similarity measure function. Finally, the target motion area was extracted by inter-frame difference in time domain. According to the proposed algorithm flow, this paper designs the high-speed real-time signal processing hardware platform based on FPGA + DSP, and also presents a new parallel processing strategy that called function-level and task-level, which could parallel process LVIR image by multi-core and multi-task. Experimental results show that the algorithm can extract low altitude aero target with complex background in large view effectively, and the new design hardware platform could implement real time processing of the IR image with 50000x 288 pixels per second in large view-field infrared search system (LVIRSS).

关键词： Large View-Field infrared image registration real-time target detection parallel processing multi-core DSP

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Differentiation of big-six non-O157 Shiga-toxin producing Escherichia coli (STEC) on spread plates of mixed cultures using hyperspectral imaging

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JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2013年第2期7卷 47-59页

作者： Yoon, Seung Chul Windham, William R. Ladely, Scott Heitschmidt, Gerald W. Lawrence, Kurt C. Park, Bosoon Narang, Neelam Cray, William C. ARS USDA Richard Russell Res Ctr Athens GA 30605 USA USDA FSIS Outbreaks Eastern Lab Athens GA 30605 USA

There have been considerable recent advances in the technology for rapidly detecting foodborne pathogens. However, a traditional culture method is still the "gold standard'' for presumptive-positive pathogen screening although it is labor-intensive, ineffective in testing large amount of food samples, and cannot completely prevent unwanted background microflora from growing together with target microorganisms on agar media. We have developed multivariate classification models based on visible and near-infrared hyperspectral imaging for rapid presumptive-positive screening of six representative non-O157 Shigatoxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) on agar plates of pure and mixed cultures. The classification models were developed with spread plates of pure cultures. In this study, we evaluated the performance of the classification models with independent validation samples of mixed cultures that were not used during training and found the best classification model for differentiating non-O157 STEC colonies on spread plates of mixed cultures. A validation protocol appropriate to hyperspectral imaging of mixed cultures was developed. An additional independent validation set of 12 spread plates with pure cultures was used as positive controls to help the validation process with the mixed cultures and to affirm the model performance. One imaging experiment with colonies obtained from two serial dilutions was performed. A total of six agar plates of mixed cultures were prepared, where O45, O111 and O121 serogroups that were relatively easy to differentiate were inoculated into all six plates and then each of O26, O103 and O145 serogroups was added into the mixture of the three common bacterial cultures. The number of mixed colonies grown after 24-h incubation was 331 and the number of pixels associated with the grown colonies was 16,379. The best model found from this validation study was based on pre-processing with stand

关键词： Pathogen detection Hyperspectral imaging Shiga-toxin producing Escherichia coli (STEC) Non-O157 serogroups Foodborne pathogen Spread plates Mixed cultures

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Research on target type recognition algorithm of aerial infrared image

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Journal of Multimedia 2013年第5期8卷 468-474页

作者： Liu, Bin Fan, Yangyu Guo, Jian Institute of Electronic and Information Northwestern Polytechnical University Xi'an China Luoyang Optoelectro Technology Development Center Luoyang China

In order to improve the target type recognition rate of aerial infrared image under the new requirements of omnidirectional crossing and multitarget types, a recognition algorithm which has four steps is researched in this paper. Firstly the maximum between-cluster variance (Ostu) algorithm is applied to segment target from the infrared image. Secondly a new edge detection algorithm is proposed to get the target edge in the segmentation image. Thirdly the edge points are fitted to be a polygon and its Fourier descriptor is extracted to obtain the target feature. Finally the target type is identified by a classification recognition algorithm based on the BP neural network and the Fourier descriptor of target. The simulation results show that the recognition rate of the target type recognition algorithm is more than 80% for the typical aerial target on every complex condition, and the processing time is just 0.1s. So the researched algorithm can meet the requirements of real-time performance and high recognition rate. © 2013 ACADEMY PUBLISHER.

关键词： Neural networks

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基于TLD的舰船目标跟踪方法研究

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红外技术 2013年第12期35卷 780-787页

作者：齐楠楠揭斐然谢熙吴巍中航工业洛阳电光设备研究所光电控制技术重点实验室河南洛阳471009 武汉理工大学信息工程学院湖北武汉430070

复杂背景下进行舰船目标的跟踪时,在某些帧可能会有目标丢失。为了克服这个问题,采用联合检测-学习-跟踪的TLD算法。其过程是通过训练一种在线可更新的随机蕨分类器对目标跟踪结果进行检测,并使用一种基于时空约束的PN学习策略对分类器... 详细信息

复杂背景下进行舰船目标的跟踪时,在某些帧可能会有目标丢失。为了克服这个问题,采用联合检测-学习-跟踪的TLD算法。其过程是通过训练一种在线可更新的随机蕨分类器对目标跟踪结果进行检测,并使用一种基于时空约束的PN学习策略对分类器进行学习和更新,最后融合跟踪得到的结果对目标进行判别和确定。试验结果表明,该跟踪算法可适用于目标外形改变和遮挡的情况,鲁棒性强,识别率高,误检率低,同时实时性也较好,可以满足一般的在线跟踪系统的要求。

关键词：舰船跟踪随机蕨分类器 TLD算法在线学习

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ES4LUCC: A GIS-tool for remotely monitoring landscape dynamics

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COMPUTERS & GEOSCIENCES 2012年 49卷 72-80页

作者： Forzieri, Giovanni Battistini, Alessandro Catani, Filippo Commiss European Communities Joint Res Ctr Climate Risk Management Unit Inst Environm & Sustainabil I-21027 Ispra VA Italy Univ Florence Dept Earth Sci Florence Italy

Given the potential impacts of land cover changes on surface processes, accurate mapping of landscape dynamics is a crucial task in environmental monitoring. The use of commercial software for remote sensing of landscape changes requires appropriate expertise in sensor technology and computing resources that are not always available to decision makers. This paper presents the development of an experimental prototype of a lightweight and user-friendly GIS tool - ES4LUCC - a semiautomatic software for change detection and classification of land use/cover. The tool is based on image processing techniques applied on multi-temporal remotely sensed spectral and surface model data. The GIS-based tiling approach allows to non-specialists of remote sensing to manage high-dimensional data even from low performance computing platforms. The paper synthesizes the implemented digital image processing that form the basis of ES4LUCC, including data correction, classification and change detection, map refinements. It also describes the software architecture, the main IDL modules and the integration with GIS through a tight coupling approach *** calling functions. The main modelling process is controlled through a powerful GUI developed as part of the ArcMap component of ESRI ArcGIS. The software is tested by using bi-temporal color-infrared ADS40 and Light detection and ranging data acquired on a 80-km transect of the Marecchia river (Italy). The outputs of ES4LUCC give an understanding of the natural- and human-induced surface processes, such as urban planning, agricultural and forest practices, fluvial dynamics and slope instability. The model provides reliable maps (90.77% overall classification accuracy) that represent useful layers for environmental landscape management. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Environmental monitoring classification Change detection Remote sensing ENVI IDL ArcGIS

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Design and Implementation of digital airborne multispectral camera system

Design and Implementation of digital airborne multispectral ...

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Conference on Electro-Optical and infrared Systems - technology and Applications IX

作者： Lin, Zhaorong Zhang, Xuguo Wang, Li Pan, Deai Beijing Inst Space Mech & Elect Beijing 10076 Peoples R China

ISBN: (纸本)9780819492821

The multispectral imaging equipment is a kind of new generation remote sensor, which can obtain the target image and the spectra information simultaneously. A digital airborne multispectral camera system using discrete filter method had been designed and implemented for unmanned aerial vehicle (UAV) and manned aircraft platforms. The digital airborne multispectral camera system has the advantages of larger frame, higher resolution, panchromatic and multispectral imaging. It also has great potential applications in the fields of environmental and agricultural monitoring and target detection and discrimination. In order to enhance the measurement precision and accuracy of position and orientation, Inertial Measurement Unit (IMU) is integrated in the digital airborne multispectral camera. Meanwhile, the Temperature Control Unit (TCU) guarantees that the camera can operate in the normal state in different altitudes to avoid the window fogging and frosting which will degrade the imaging quality greatly. Finally, Flying experiments were conducted to demonstrate the functionality and performance of the digital airborne multispectral camera. The resolution capability, positioning accuracy and classification and recognition ability were validated.

关键词： Multispectral remote sensing spectral division target classification image fusion

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