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IEEE Southwest Symposium on image analysis and Interpretation

作者： Neeraj J. Gadgil Paul Salama Kenneth W. Dunn Edward J. Delp Video and Image Processing Laboratory Purdue University West Lafayette Indiana Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis Indiana Division of Nephrology Indiana University Indianapolis Indiana

Microscope image analysis is challenging because of non-uniform brightness, decreasing image contrast with tissue depth, poor edge details and irregular and unknown structures. In this paper, we present a nuclei segmentation and counting method using midpoint analysis, shape-based function optimization and a MPP simulation with a spatial birth-death process. Preliminary results demonstrate efficacy of the proposed method.

关键词： image segmentation Microscopy Shape Three-dimensional displays Brightness Biology Stochastic processes

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Jelly filling segmentation of fluorescence microscopy images containing incomplete labeling

Jelly filling segmentation of fluorescence microscopy images...

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IEEE International Symposium on Biomedical Imaging

ISBN: (纸本)9781479923519

Biological images acquired using fluorescence microscopy typically suffer from poor edge details, non-uniform brightness, decreasing image contrast with tissue depth and irregular/unknown structure. Hence they are consequently challenging to segment. In this paper, we present a segmentation method we call "Jelly filling" for analyzing biological volumes that also contain incomplete labeling. Our method uses adaptive thresholding, component analysis, and three-dimensional structural consistency to iteratively refine segments that represent physical quantities. Preliminary results obtained using fluorescence microscopy images of rat kidneys demonstrate the efficacy of the proposed method.

关键词： image segmentation Biology Three-dimensional displays Microscopy Labeling image edge detection Filling

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An In-Mold Monitoring and Protection System of the Injection Molding Machine Using Offset Correction and Dynamic Safe Range Learning Approach 1

An In-Mold Monitoring and Protection System of the Injection...

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1st International Conference on Computational Intelligence Theory, Systems and Applications, CCITSA 2015

作者： Chi, Ting-Yun Huang, Chun-Yen Xin, Men-Chang College of Electrical Engineering and Computer Science National Taiwan University Taipei106 Taiwan Department of Vision Analysis MoRemote Inc. Neihu Rd. Neihu Dist. Taipei114 Taiwan

ISBN: (纸本)9781467386005

An in-mold monitoring and protecting system is a system to monitoring and protects the mold of a plastic injection molding machine. It is used to protect the mold from been damaged by the stuck ejectors or plastic pieces. However, the difference of unsafe status cannot be simply compared from two captured images due to the positioning errors of the male die. Besides, there will be false alarm when the luminance changed of the environment that affects the captured image. We propose an in-mold monitoring and protecting system of the injection molding machine to overcome these errors. By the two approaches of offset correction and dynamic safe range learning, the proposed system is able to correctly detect the unsafe in-mold status of the stuck ejectors or pieces. © 2015 IEEE.

关键词： Injection molding

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Segmentation as postprocessing for hyperspectral image classification

Segmentation as postprocessing for hyperspectral image class...

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IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015

作者： Jimenez, L.I. Ayma, V.A. Achanccaray, P. Costa, G.A.O.P. Feitosa, R.Q. Plaza, A. Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura Caceres Spain Computer Vision Lab Department of Electrical Engineering Catholic University of Rio de Janeiro Rio de Janeiro Brazil Department of Computer Engineering Rio de Janeiro State University Rio de Janeiro Brazil

ISBN: (纸本)9781479979295

Hyperspectral imaging is a new technique in remote sensing that collects hundreds of images at differents wavelength values for the same area of the Earth. For instance the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) sensor of NASA capable to obtain 224 spectral channels in a wavelength range between 40 and 250 nanometers. As a result each pixel of the image can be represented as a spectral signature. image segmentation is the process of dividing a digital image into groups of pixels or objects. Hyperspectral image classification is an important and active area dedicated to identifying each pixel in the image with an exclusive material/object class. Several efforts had been done in this field using spectral and spatial information separately or simultaneously in order to improve the performance of the classification techniques. In this work we have developed a new technique that uses a segmentation algorithm to post-process the classification results obtained using a widely used classifier such as the support vector machine (SVM). Experimental results with a real hyperspectral data set collected over the city of Pavia, Italy, are provided. © 2015 IEEE.

关键词： classification hyperspectral imaging Segmentation

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Detecting rails and obstacles using a train-mounted thermal camera 19th

Detecting rails and obstacles using a train-mounted thermal ...

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19th Scandinavian Conference on image analysis, SCIA 2015

作者： Berg, Amanda Öfjäll, Kristoffer Ahlberg, Jörgen Felsberg, Michael Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping581 83 Sweden Termisk Systemteknik AB Diskettgatan 11 B Linköping583 35 Sweden

ISBN: (纸本)9783319196640

We propose a method for detecting obstacles on the railway in front of a moving train using a monocular thermal camera. The problem is motivated by the large number of collisions between trains and various obstacles, resulting in reduced safety and high costs. The proposed method includes a novel way of detecting the rails in the imagery, as well as a way to detect anomalies on the railway. While the problem at a first glance looks similar to road and lane detection, which in the past has been a popular research topic, a closer look reveals that the problem at hand is previously unaddressed. As a consequence, relevant datasets are missing as well, and thus our contribution is two-fold: We propose an approach to the novel problem of obstacle detection on railways and we describe the acquisition of a novel data set. © Springer International Publishing Switzerland 2015.

关键词： computer vision

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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An In-Mold Monitoring and Protection System of the Injection Molding Machine Using Offset Correction and Dynamic Safe Range Learning Approach

An In-Mold Monitoring and Protection System of the Injection...

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International Conference on Computational Intelligence Theory, Systems and Applications (CCITSA)

作者： Ting-Yun Chi Chun-Yen Huang Men-Chang Xin College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Department of Vision Analysis MoRemote Inc. Taipei Taiwan

关键词： Monitoring Injection molding Plastics Dynamic range Inspection Pins

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EREL: Extremal regions of extremum levels

EREL: Extremal regions of extremum levels

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IEEE International Conference on image Processing

作者： Mehdi Faraji Jamshid Shanbehzadeh Kamal Nasrollahi Thomas B. Moeslund Department of Electrical and Computer Engineering Kharazmi University Tehran Iran Aalborg University Visual Analysis of People Laboratory Aalborg Denmark

Extremal Regions of Extremum Levels (EREL) are regions detected from a set of all extremal regions of an image. Maximally Stable Extremal Regions (MSER) which is a novel affine covariant region detector, detects regions from a same set of extremal regions as well. Although MSER results in regions with almost high repeatability, it is heavily dependent on the union-find approach which is a fairly complicated algorithm, and should be completed sequentially. Furthermore, it detects regions with low repeatability under the blur transformations. The reason for the latter shortcoming is the absence of boundaries information in stability criterion. To tackle these problems we propose to employ prior information about boundaries of regions, which results in a novel region detector algorithm that not only outperforms MSER, but avoids the MSER's rather complicated steps of union-finding. To achieve that, we introduce Maxima of Gradient Magnitudes (MGMs) and use them to find handful of Extremum Levels (ELs). The chosen ELs are then scanned to detect their Extremal Regions (ER). The proposed algorithm which is called Extremal Regions of Extremum Levels (EREL) has been tested on the public benchmark dataset of Mikolajczyk [1]. Our experimental evaluations illustrate that, in many cases EREL achieves higher repeatability scores than MSER even for very low overlap errors.

关键词： Stability criteria Cleaning Detectors Benchmark testing Feature extraction Detection algorithms

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Probabilistic edge detection in 3D optical microscopy images of tissue samples

Probabilistic edge detection in 3D optical microscopy images...

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IEEE International Symposium on Biomedical Imaging

作者： Kaustav Nandy Prabhkar R. Gudla Rama Chellappa Stephen J. Lockett Optical Microscopy and Analysis Lab Frederick National Laboratory for Cancer Research Frederick MD Department of Electrical and Computer Engineering University of Maryland MD

Inspired by a state of the art 2D algorithm for edge detection in natural images, in this work we report the development of an accurate 3D probabilistic edge detector for fluorescence optical microscopy images of tissue samples. The method utilizes multi-orientation and multi-scale brightness, textural and spectral properties of the data to compute a probabilistic edge map. To demonstrate the strengths and accuracy of the proposed algorithm, comparisons of the edge maps produced by our algorithm and several other popular edge detectors on simulated and real 3D microscopic volumes are provided.

关键词： image edge detection Three-dimensional displays Microscopy Optical microscopy Probabilistic logic Optical imaging Signal to noise ratio

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