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作者： Sirmacek, Beril Unsalan, Cem Wessling 82234 Germany Computer Vision Research Laboratory Department of Electrical and Electronics Engineering Yeditepe University Istanbul 34755 Turkey

Detecting buildings from very high resolution (VHR) aerial and satellite images is extremely useful in map making, urban planning, and land use analysis. Although it is possible to manually locate buildings from these VHR images, this operation may not be robust and fast. Therefore, automated systems to detect buildings from VHR aerial and satellite images are needed. Unfortunately, such systems must cope with major problems. First, buildings have diverse characteristics, and their appearance (illumination, viewing angle, etc.) is uncontrolled in these images. Second, buildings in urban areas are generally dense and complex. It is hard to detect separate buildings from them. To overcome these difficulties, we propose a novel building detection method using local feature vectors and a probabilistic framework. We first introduce four different local feature vector extraction methods. Extracted local feature vectors serve as observations of the probability density function (pdf) to be estimated. Using a variable-kernel density estimation method, we estimate the corresponding pdf. In other words, we represent building locations (to be detected) in the image as joint random variables and estimate their pdf. Using the modes of the estimated density, as well as other probabilistic properties, we detect building locations in the image. We also introduce data and decision fusion methods based on our probabilistic framework to detect building locations. We pick certain crops of VHR panchromatic aerial and Ikonos satellite images to test our method. We assume that these crops are detected using our previous urban region detection method. Our test images are acquired by two different sensors, and they have different spatial resolutions. Also, buildings in these images have diverse characteristics. Therefore, we can test our methods on a diverse data set. Extensive tests indicate that our method can be used to automatically detect buildings in a robust and fast manner in Ikonos

关键词： Land use

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Prolog to the section on optics and photonics

Prolog to the section on optics and photonics

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作者： Willner, Alan E. Byer, Robert L. Chang-Hasnain, Constance J. Forrest, Stephen R. Kressel, Henry Kogelnik, Herwig Tearney, Guillermo J. Townes, Charles H. Zervas, Michalis N. Department of Electrical Engineering University of Southern California Los Angeles CA 90089-2565 United States E.L. Ginzton Laboratory Stanford University Stanford CA 94305-4088 United States Electrical Engineering and Computer Science Department University of California Berkeley Berkeley CA 94720-1770 United States Vice President for Research Ann Arbor MI 48109-1340 United States Warburg Pincus LLC New York NY 10017 United States Bell Labs Alcatel-Lucent Holmdel NJ 07733-0400 United States Harvard Medical School Boston MA 02114 United States Graduate School University of California Berkeley Berkeley CA 94720 United States Optoelectronics Research Centre University of SouthamptonHighfield Hampshire SO17 1BJ United Kingdom

A section of the Centennial Special Issue of the proceedings of the IEEE, entitled 'Optics and Photonics', contains an article in which different topics are included. Many technical advances are measured in terms of orders of magnitude and an ever-growing list exists of industries that are critically enabled by these dramatic advances. Over the past several decades, optics and photonics have cemented themselves as key enabling technologies for a myriad of industries. The paper in this section tries to capture some of the excitement of this field, and it is divided into several different subsections, including: lasers, materials and devices, communications, bioimaging, displays, manufacturing, and the evolution of the photonics industry. Each subsection is divided further into subsections, introduction: context and importance of the topic, past: key past milestones and perspectives, present: state of the art and current thinking, and future: speculative vision into the future.

关键词： Display devices

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Generalized Fuzzy C-Means Clustering with Improved Fuzzy Partitions and Shadowed Sets

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International Scholarly research Notices 2012年第1期2012卷

作者： Seyed Mohsen Zabihi Mohammad-R Akbarzadeh-T Machine Vision Laboratory Computer Engineering Department Ferdowsi University of Mashhad Mashhad *** Iranum.ac.ir Departments of Electrical Engineering and Computer Engineering Ferdowsi University of Mashhad Mashhad *** Iranum.ac.ir

Clustering involves grouping data points together according to some measure of similarity. Clustering is one of the most significant unsupervised learning problems and do not need any labeled data. There are many clustering algorithms, among which fuzzy c-means (FCM) is one of the most popular approaches. FCM has an objective function based on Euclidean distance. Some improved versions of FCM with rather different objective functions are proposed in recent years. Generalized Improved fuzzy partitions FCM (GIFP-FCM) is one of them, which uses L p norm distance measure and competitive learning and outperforms the previous algorithms in this field. In this paper, we present a novel FCM clustering method with improved fuzzy partitions that utilizes shadowed sets and try to improve GIFP-FCM in noisy data sets. It enhances the efficiency of GIFP-FCM and improves the clustering results by correctly eliminating most outliers during steps of clustering. We name the novel fuzzy clustering method shadowed set-based GIFP-FCM (SGIFP-FCM). Several experiments on vessel segmentation in retinal images of DRIVE database illustrate the efficiency of the proposed method.

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RANSAC-based Training Data Selection for speaker state recognition

RANSAC-based Training Data Selection for speaker state recog...

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12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011

作者： Bozkurt, Elif Erzin, Engin Erdem, Çiǧdem Eroǧlu Erdem, A.Tanju Multimedia Vision and Graphics Laboratory Koç University Istanbul Turkey Department of Electrical and Electronics Engineering Bahçeşehir University Istanbul Turkey Department of Electrical and Computer Engineering Özyeǧin University Istanbul Turkey

We present a Random Sampling Consensus (RANSAC) based training approach for the problem of speaker state recognition from spontaneous speech. Our system is trained and tested with the INTERSPEECH 2011 Speaker State Challenge corpora that includes the Intoxication and the Sleepiness Sub-challenges, where each sub-challenge defines a two-class classification task. We aim to perform a RANSAC-based training data selection coupled with the Support Vector Machine (SVM) based classification to prune possible outliers, which exist in the training data. Our experimental evaluations indicate that utilization of RANSAC-based training data selection provides 66.32 % and 65.38 % unweighted average (UA) recall rate on the development and test sets for the Sleepiness Sub-challenge, respectively and a slight improvement on the Intoxication Sub-challenge performance. Copyright © 2011 ISCA.

关键词： Support vector machines

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VHR image segmentation using over segmented regions in a MRF model

VHR image segmentation using over segmented regions in a MRF...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Örsan Aytekin Yousef Rezaeitabar İlkay Ulusoy Computer Vision and Intelligent Systems Research Laboratory Department of Electrical and Electronics Engineering METU Ankara Turkey Orta Dogu Teknik Universitesi Ankara Ankara TR

This paper presents a segmentation method that exploits object based region merging to delineate the context of the image under investigation. There are two main steps. Initially, primitive objects are obtained by morphological operations that generate spectrally homogenous primitives. We assume that primitives are components of semantic objects that are of interest. Next, these primitives therefore are modeled and merged based on expectation maximization. We presented the results of the experiments applied to QuickBird images of rural and urban areas taken from the city of Ankara, Turkey. Experimental results demonstrate the capabilities of these methods along with their limitations.

关键词： Image segmentation Morphology Clustering algorithms Urban areas Remote sensing Image reconstruction Cities and towns

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Retinal vessel tortuosity evaluation via Circular Hough Transform

Retinal vessel tortuosity evaluation via Circular Hough Tran...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Farnoosh Ghadiri HamidReza Pourreza Touka Banaee Morteza Delgir Computer Engineering Department Machine Vision Laboratory Ferdowsi University of Mashhad Iran Computer Engineering Department Machine Vision Laboratory University of Ferdowsi Mashhad Iran Ophthalmic Research Center Khatam-Al-Anbia Hospital Medical science University of Mashhad Iran Computer Science Department Robotics Laboratory Northeastern University USA

Retinal vessel tortuosity has shown to be significantly associated with cardiovascular diseases such as hypertension and diabetes. Despite importance of this field a few techniques have been proposed yet. All previous methods rely on a vessel extraction phase, which its accuracy affects final output and also time consuming. Nobility of presented algorithm is to introduce a method for evaluating retinal vessel tortuosity without any explicit vessel detection. We use the Circular Hough Transform (CHT) based on gradient field of the retinal image. Each vessel curve is detected as a semi-circle by Hough transform and tortuosity of the curve is determined with the help of accumulated value of circle center and its radius. As there are no any specific database for tortuosity evaluation, the algorithm was tasted on database consisting of 40 images, mixture of DRIVE database and images from Khatam-Al-Anbia Hospital consisting of 40 retinal images, of which 20 were tortuous and 20 were non-tortuous. The proposed algorithm can achieve classification rate of 92% along with less computation time in compare of previous methods.

关键词： Transforms Retinal vessels Classification algorithms Biomedical measurements Biomedical imaging Image segmentation

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A New NSCT Based Contrast Enhancement Algorithm for Amplification of Early Signs of Ischemic Stroke in Brain CT Images

A New NSCT Based Contrast Enhancement Algorithm for Amplific...

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Iranian Machine vision and Image Processing (MVIP)

作者： Mehdi Sajjadi Rassoul Amirfattahi Mohammad Reza Ahmadzadeh Digital Signal Processing Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Brain CT images are extensively used in detection of Cerebrovascular Accidents (CVA). But CT scan is not really a sensitive test for diagnosis of ischemic stroke. Most of the times, early CT images barely show or even don 't show any noticeable sign of ischemia. But it is possible to put emphasis on subtle signs of ischemia by means of image contrast enhancement. Contrast enhancement using multiscale transforms is one of the most popular approaches in recent years. Nonsubsampled Contourlet Transform (NSCT) is a directional multiscale transform that is able to take geometrical structures into account. In this paper we are about to customize this transform and improve its performance in contrast enhancement of brain CT images. To this end, we propose a set of simpler and more efficient filters for Nonsubsampled Pyramid (NSP) and introduce an improved method for modification of directional subbands' coefficients.

关键词： Filter banks Finite impulse response filter Computed tomography Equations Wavelet transforms Image enhancement

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Scalable control plane architecture for optical flow switched networks

Scalable control plane architecture for optical flow switche...

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Optical Fiber Communication (OFC) Conference

作者： Vincent W.S. Chan Zhang Lei Claude E. Shannon Communication and Network Group Research Laboratory of Electronics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology USA

In this paper an OFS architecture that exploits the strength of optics to serve large transactions is described. The shift towards OFS requires some architectural elements of the network- from the physical layer to the higher network layers, as well as network management and control, be substantially redesigned at the fundamental level for the network architecture to be scalable and implementable.

关键词： Optical switches Entropy Wide area networks Optical fiber networks Image motion analysis computer vision computer architecture

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A deformable surface model based automatic rat brain extraction method

A deformable surface model based automatic rat brain extract...

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IEEE International Symposium on Biomedical Imaging

作者： Jiehua Li Xiaofeng Liu Jiachen Zhuo Rao P. Gullapalli Jason M. Zara Department of Electrical and Computer Engineering George Washington University Washington D.C. DC USA General Electric Global Research Center Visualization and Computer Vision Laboratory Niskayuna NY USA Department of Radiology University of Maryland School of Medicine Baltimore MD USA University of Maryland School of Law Baltimore MD US

ISBN: (纸本)9781424441273

The extraction of the brain portion of a neurological image is often necessary prior to tissue segmentation or image registration. While MR Imaging studies on the rat have gained much interest lately, an automatic and robust rat brain extraction tool is still lacking. In this paper, we present a deformable surface model-based rat brain extraction method which extends the popular human brain extraction tool (BET) by incorporating the brain geometry and MRI tissue characteristics of the rat into consideration for more robust extraction. Our method was demonstrated on T2-weighted MR images for five rats and compared with other rat brain extraction methods. Results showed that our method can reliably and robustly extract the rat brain with high accuracy (>;92% volume overlap).

关键词： Brain modeling Humans Force Surface roughness Rough surfaces Geometry

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Measuring Object Speed Using Stereo Tracking

Measuring Object Speed Using Stereo Tracking

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2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Yuan F. Zheng Junda Zhu Robert E. Ewing Department of Electrical and Computer Engineering The Ohio State University 2015 Neil Avenue Columbu Air Force Research Laboratory Wright Patterson AFB Ohio 45433

Stereo vision provides us the ability to perceive objects in 3D. By continuously tracking an object in stereo videos, the information about object motion can be acquired. In this paper, we propose a novel method which unifies the tracking and speed measuring within the Bayesian filtering framework and solves this problem using particle filtering approach. Vehicle speed measuring is studied as a specific example. Environment constraints are integrated into the tracking for reducing the uncertainty to achieve more robust and accurate results. Experiment results on both synthetic and real traffic sequences demonstrate the accuracy and effectiveness of our proposed method.

关键词： Three dimensional displays Particle measurements Atmospheric measurements Videos Vehicles Radar tracking Cameras

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