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IEEE International Geoscience and Remote Sensing Symposium

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

ISBN: (纸本)9781479979301

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 wave-length 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.

关键词： Segmentation Classification Hyperspectral imaging

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Accurate scale estimation for robust visual tracking 25

Accurate scale estimation for robust visual tracking

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25th British Machine vision Conference, BMVC 2014

作者： Danelljan, Martin Häger, Gustav Khan, Fahad Shahbaz Felsberg, Michael Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping Sweden

Robust scale estimation is a challenging problem in visual object tracking. Most existing methods fail to handle large scale variations in complex image sequences. This paper presents a novel approach for robust scale estimation in a tracking-by-detection framework. The proposed approach works by learning discriminative correlation filters based on a scale pyramid representation. We learn separate filters for translation and scale estimation, and show that this improves the performance compared to an exhaustive scale search. Our scale estimation approach is generic as it can be incorporated into any tracking method with no inherent scale estimation. Experiments are performed on 28 benchmark sequences with significant scale variations. Our results show that the proposed approach significantly improves the performance by 18:8% in median distance precision compared to our baseline. Finally, we provide both quantitative and qualitative comparison of our approach with state-of-the-art trackers in literature. The proposed method is shown to outperform the best existing tracker by 16:6% in median distance precision, while operating at real-time. © 2014. The copyright of this document resides with its authors.

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Biologically inspired online learning of visual autonomous driving 25

Biologically inspired online learning of visual autonomous d...

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25th British Machine vision Conference, BMVC 2014

作者： Öfjäll, Kristoffer Felsberg, Michael Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping Sweden

While autonomously driving systems accumulate more and more sensors as well as highly specialized visual features and engineered solutions, the human visual system provides evidence that visual input and simple low level image features are sufficient for successful driving. In this paper we propose extensions (non-linear update and coherence weighting) to one of the simplest biologically inspired learning schemes (Hebbian learning). We show that this is sufficient for online learning of visual autonomous driving, where the system learns to directly map low level image features to control signals. After the initial training period, the system seamlessly continues autonomously. This extended Hebbian algorithm, qHebb, has constant bounds on time and memory complexity for training and evaluation, independent of the number of training samples presented to the system. Further, the proposed algorithm compares favorably to state of the art engineered batch learning algorithms. © 2014. The copyright of this document resides with its authors.

关键词： E-learning

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2D-PCA shape models: Application to 3D reconstruction of the human teeth from a single image

2D-PCA shape models: Application to 3D reconstruction of the...

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3rd International MICCAI Workshop on Medical computer vision, MCV 2013

作者： Abdelrehim, Aly S. Farag, Aly A. Shalaby, Ahmed M. El-Melegy, Moumen T. Computer Vision and Image Processing Laboratory University of Louisville Louisville KY 40292 United States Electrical Engineering Department Assiut University Assiut 71516 Egypt

ISBN: (纸本)9783319055299

Accurate modeling of human teeth is mandatory for many reasons. Some of them are (1) Providing comfort to patients during mold process, and (2) Enhancing the accuracy level for oral orthodontist and dental care personnel. Shape from Shading (SFS) proved to be of great help in this problem, considering the fact that human tooth surface is textureless. In this paper, we address the problem of 3D tooth reconstruction, and improve the current algorithms. The approached developed in this paper reconstructs the teeth from single image shading with 2D-PCA shape priors that have more sophisticated reflectance model. Oren-Nayar-Wolff model was used for modeling the surface reflectance. This formulation uses shape priors as got from a set of training CT scans of real human teeth. Our experiments show promising quantitative results, which builds the infrastructure for having an optical based approach that accounts for inexpensive and radiationless human tooth reconstruction. © 2014 Springer International Publishing Switzerland.

关键词： computerized tomography

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Automatic landing for fixed-wing UAV using stereo vision with a single camera and an orientation sensor: A concept

Automatic landing for fixed-wing UAV using stereo vision wit...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Montika Sereewattana Miti Ruchanurucks Panjawee Rakprayoon Supakorn Siddhichai Shoichi Hasegawa ICT for Embedded System Kasetsart University Bangkok Thailand Department of Electrical Engineering Kasetsart University Bangkok Thailand Image Processing Laboratory National Electronics and Computer Technology Center Pathumthani Thailand Precision and Intelligence Laboratory Tokyo Institute of Technology Yokohama Japan

This paper presents concepts and some experiment on UAV automatic landing on any flat area without prior knowledge of (detachable) markers' positions. The process consists of two key notions. First, the UAV approximates 3D positions of four color markers using a single camera stereoscopic scheme. In other words, the aircraft flies and captures images of ground to create depth map of the four markers. Second, it relies on a pose estimation algorithm (here, Homography) to approximate rotational and, especially, translational information between air to ground. What is novel in this concept is z-coordinate of markers on ground can always be normalized based on our kinematic chain. Thus, homography can be used. Additionally, depth estimation using stereo vision with a camera is shown in experimental result. Furthermore, the whole process is also possible without GPS data, which might post problem in some certain situations, e.g. inadequate positioning accuracy for small landing area, or unreliability during warfare.

关键词： Cameras Global Positioning System Aircraft image color analysis Calibration Transmission line matrix methods Three-dimensional displays

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 1

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BMC Neurosci 2017年第SUPPL 1期18卷 58-58页

作者： Sue Denham Panayiota Poirazi Erik De Schutter Karl Friston Ho Ka Chan Thomas Nowotny Dongqi Han Sungho Hong Sophie Rosay Tanja Wernle Alessandro Treves Sarah Goethals Romain Brette Tomas Van Pottelbergh Rodolphe Sepulchre Alex D. Bird Hermann Cuntz Pedro J. Gonçalves Jan-Matthis Lueckmann Giacomo Bassetto Marcel Nonnenmacher Jakob H. Macke Audrey J. Sederberg Jason N. MacLean Stephanie E. Palmer Ulisse Ferrari Christophe Gardella Olivier Marre Thierry Mora Emina Ibrahimovic Martin Müller Jean-Pascal Pfister Tushar Chauhan Timothée Masquelier Alexandre Montlibert Benoit R. Cottereau Moritz Helias Jannis Schuecker David Dahmen Sven Goedeke Alexandre Hyafil Ainhoa Hermoso-Mendizabal Pavel E. Rueda-Orozco Santiago Jaramillo David Robbe Jaime de la Rocha Marie Rooy Fani Koukouli David DiGregorio Uwe Maskos Boris Gutkin Andrey Yu Verisokin Darya V. Verveyko Dmitry E. Postnov Willy Wong Omid Talakoub Robert Chen Milos Popovic Dmitriy Lisitsyn Eric Drebitz Iris Grothe Sunita Mandon Andreas Kreiter Udo Ernst Peter A. Robinson Xuelong Zhao Kevin M. Aquino John D. Griffiths Grishma Mehta-Pandejee Natasha Gabay James MacLaurin Somwrita Sarkar Tim Kunze Jens Haueisen Thomas R. Knösche Subutai Ahmad Yuwei Cui Marcus Lewis Jeff Hawkins Simona Olmi Spase Petkoski Fabrice Bartolomei Maxime Guye Viktor Jirsa Hazem Toutounji Daniel Durstewitz Matteo Cantarelli Adrian Quintana Boris Marin Matt Earnshaw Padraig Gleeson Robert Court Robert McDougal R. Angus Silver Salvador Dura-Bernal Stephen Larson William W. Lytton Giovanni Idili Lorenzo Posani Simona Cocco Karel Ježek Rémi Monasson School of Psychology Faculty of Health & Human Sciences Plymouth University Plymouth Devon PL4 8AA UK Institute of Molecular Biology and Biotechnology (IMBB) Foundation for Research and Technology-Hellas (FORTH) Heraklion Crete Greece Okinawa Institute of Science and Technology Graduate University Okinawa Japan Wellcome Trust Centre for Neuroimaging Institute of Neurology UCL London WC1N 3BG UK School of Engineering and Informatics University of Sussex Brighton BN1 9QJ UK Graduate School Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Computational Neuroscience Unit Okinawa Institute of Science and Technology Okinawa 904-0495 Japan Cognitive Neuroscience Area Scuola Internazionale Superiore di Studi Avanzati Trieste 34136 Italy Kavli Institute for Systems Neuroscience NTNU Trondheim 7030 Norway Institut de la Vision Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Paris 75012 France Department of Engineering University of Cambridge Cambridge Cambridgeshire CB2 1PZ UK Ernst Strüngmann Institute for Neuroscience Max Planck Society Frankfurt Germany Frankfurt Institute for Advanced Studies Frankfurt Germany Neural Systems Analysis Research Center Caesar An Associate of the Max Planck Society Bonn Germany Max Planck Institute for Biological Cybernetics Tübingen Germany Bernstein Center for Computational Neuroscience Tübingen Germany Present Address: Biomedical Engineering Department Georgia Institute of Technology Atlanta GA 30306 USA Department of Neurobiology University of Chicago Chicago IL 60637 USA Department of Organismal Biology and Anatomy University of Chicago Chicago IL 60637 USA Committee on Computational Neuroscience University of Chicago Chicago IL 60637 USA Institut de la Vision Sorbonne Universités INSERM Paris France Laboratoire de Physique Statistique Ecole Normale Supérieure Paris France Institute of Molecular Life Sciences University of Zürich 8057 Zürich Switzerland Institute of Neuroinformatics Univer

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MULTI-SPECTRAL DOCUMENT image BINARIZATION USING image FUSION AND BACKGROUND SUBTRACTION TECHNIQUES

MULTI-SPECTRAL DOCUMENT IMAGE BINARIZATION USING IMAGE FUSIO...

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IEEE International Conference on image Processing

作者： Nikolaos Mitianoudis Nikolaos Papamarkos Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace

ISBN: (纸本)9781479957521

In this paper, the authors exploit a multispectral image representation to perform more accurate document image binarisation compared to previous color representations. In the first stage, image fusion is employed to create a "document" and a "background" image. In the second stage, the FastICA algorithm is used to perform background subtraction. In the third stage, a spatial kernel K-harmonic means classifier binarizes the FastICA output. The proposed system outperforms previous efforts on document image binarization.

关键词： Document image Binarization image Fusion Multi-spectral imaging Independent Component analysis

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Segmentation of bone from ADC maps in pelvis area using local level-set and prior information 11

Segmentation of bone from ADC maps in pelvis area using loca...

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2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

作者： Sanaei Nezhad, F. Saligheh Rad, H. Soltanian-Zadeh, H. School of Electrical and Computer Engineering College of Engineering Tehran Iran Department of Medical Physics and Biomedical Engineering Tehran University of Medical Sciences Tehran Iran Research Center for Molecular and Cellular Imaging Tehran University of Medical Sciences Tehran Iran Medical Image Analysis Laboratory Henry Ford Health System DetroitMI United States

ISBN: (纸本)9781467319591

Lack of anatomical details in diffusion weighted magnetic resonance images limits their utilization and treatment response monitoring, shadowing the useful information they contain. Contemporary methods of utilizing these images are based on manual selection of region of interest, raising concerns about susceptibility of manual ROI placement to human errors, and limiting the investigation in specific spatial regions. In contrary to the whole body bone marrow segmentation with the luxury to include all the diseased bone marrow, high profile analysis could be applied. In this paper, we propose an automatic method for segmentation of pelvic bone with possible bone metastasis in apparent diffusion coefficient (ADC) maps. This method is a multi-parametric registration-segmentation method, taking advantage of prior information of the pelvic anatomy. Intensity inhomogeneity in the bone structure caused by bone marrow metastasis challenges the segmentation process on anatomical MR images. Specifically, we first build a probability map which provides shape and volume constraints for the segmentation. Then, T1-weighted MR images are rigidly registered to the probability map, and then the registered T1-weighted image is non-rigidly registered to its' corresponding ADC maps. Finally, the probability map is coupled with a local level set framework for automatic pelvic bone segmentation of the T1-weighted images. The segmented bone is used as a mask on the ADC map. The method is validated on 10 pairs of ADC/T1 images of breast cancer with bone marrow metastases patients. Both quantitative and qualitative evaluation results demonstrate the validity of the proposed method. © 2014 IEEE.

关键词： Bone

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Auto-adjusting Camera Exposure for Outdoor Robotics using Gradient Information

Auto-adjusting Camera Exposure for Outdoor Robotics using Gr...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Inwook Shim Joon-Young Lee In So Kweon Robotics and Computer Vision Laboratory Division of Future Vehicle KAIST Daejeon 305-701 Korea Robotics and Computer Vision Laboratory Department of Electrical Engineering KAIST Daejeon 305-701 Korea Department of Electrical Engineering KAIST Daejeon 307-701 Korea

ISBN: (纸本)9781479969357

We present a new method to auto-adjust camera exposure for outdoor robotics. In outdoor environments, scene dynamic range may be wider than the dynamic range of the cameras due to sunlight and skylight. This can results in failures of vision-based algorithms because important image features are missing due to under-/over-saturation. To solve the problem, we adjust camera exposure to maximize image features in the gradient domain. By exploiting the gradient domain, our method naturally determines the proper exposure needed to capture important image features in a manner that is robust against illumination conditions. The proposed method is implemented using an off-the-shelf machine vision camera and is evaluated using outdoor robotics applications. Experimental results demonstrate the effectiveness of our method, which improves the performance of robot vision algorithms.

关键词： Cameras Lighting Robot vision systems Manuals Dynamic range Heuristic algorithms

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Atlas based intensity transformation of brain MR images

Atlas based intensity transformation of brain MR images

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3rd International Workshop on Multimodal Brain image analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical image Computing and computer Assisted Intervention, MICCAI 2013

作者： Roy, Snehashis Jog, Amod Carass, Aaron Prince, Jerry L. Image Analysis and Communication Laboratory Department of Electrical and Computer Engineering Johns Hopkins University United States

ISBN: (纸本)9783319021256

Magnetic resonance imaging (MRI) is a noninvasive modality that has been widely used to image the structure of the human brain. Unlike reconstructed x-ray computed tomography images, MRI intensities do not possess a calibrated scale, and the images suffer from wide variability in intensity contrasts due to scanner calibration and pulse sequence variations. Most MR image processing tasks use intensities as the principal feature and therefore the results can vary widely according to the actual tissue intensity contrast. Since it is difficult to control the MR scanner acquisition protocols in multi-scanner cross-sectional studies, results achieved using image processing tools are often difficult to compare in such studies. Similar issues can happen in longitudinal studies, as scanners undergo upgrades or improvements in pulse sequences, leading to new imaging sequences. We propose a novel probabilistic model to transform image contrasts by matching patches of a subject image to a set of patches from a multi-contrast atlas. Although the transformed images are not for diagnostic purpose, the use of such contrast transforms is shown for two applications, (a) to improve segmentation consistency across scanners and pulse sequences, (b) to improve registration accuracy between multi-contrast image pairs by transforming the subject image to the contrast of the reference image and then registering the transformed subject image to the reference image. Contrary to previous intensity transformation methods, our technique does not need any information about landmarks, pulse sequence parameters or imaging equations. It is shown to provide more consistent segmentation across scanners compared to state-of-the-art methods. © 2013 Springer International Publishing.

关键词： Magnetic resonance imaging

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