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检索条件"机构=Computer Vision and Image Analysis Laboratory Department of Electrical and Computer Engineering"

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Damaged building detection in aerial images using shadow Information

Damaged building detection in aerial images using shadow Inf...

引用

International Conference on Recent Advances in Space Technologies (RAST)

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

Automatic detection of damaged buildings from aerial and satellite images is an important problem for rescue planners and military personnel. In this study, we present a novel approach for automatic detection of damaged buildings in color aerial images. Our method is based on color invariants for building rooftop segmentation. Then, we benefit from grayscale histogram to extract shadow segments. After building verification using shadow information, we define a new damage measure for each building. Experimentally, we show that using our damage measure it is possible to discriminate nearby damaged and undamaged buildings. We present our experimental results on aerial images.

关键词： Data mining Buildings image segmentation Personnel Earthquakes Gray-scale Hurricanes Military satellites computer vision Laboratories

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Color quantization using principal components for initialization of Kohonen SOFM

Color quantization using principal components for initializa...

引用

IEEE International Conference on image Processing

作者： Dimitris Mavridis Nikos Papamarkos Image Processing and Multimedia Laboratory Department of Electrical & Computer Engineering Democritus University of Thrace Greece

A new method is proposed for initializing Kohonen's self-organizing feature maps (SOFM) of fixed zero neighborhood radius for use in color quantization. The method employs the two largest principal components of the input image so that the initial weights of a number of neurons approach the input image color distribution. The rest of the neurons are initialized using the smallest principal component of the input image. Namely, standard SOFM is applied to the projection of the input image pixels onto the plane spanned by the two largest principal components and to pixels of the original image defined by the smallest principal component. The neuron values which emerge initialize the final SOFM of fixed zero neighborhood radius that performs the color quantization of the original image. Experimental results show that the proposed method can often produce smaller quantization errors than standard SOFM and other color quantization methods.

关键词： Quantization Neurons Neural networks Pixel image color analysis image processing Clustering algorithms Fuzzy neural networks Vectors Lattices

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SILT: Scale-invariant line transform

SILT: Scale-invariant line transform

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IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA)

作者： Bahador Khaleghi Malek Baklouti Fakhreddin O. Karray Pattern Analysis and Machine Intelligence Laboratory Department of Electrical and Computer Engineering University of Waterloo Canada

ISBN: (纸本)9781424448081

Line matching is useful in many computer vision tasks such as object recognition, image registration, and 3D reconstruction. The literature on line matching has advanced in recent years, nevertheless, compared to other features (such as point and region matching approaches) it has made little progress. Especially, very few algorithms address the problem of image scaling. In this paper, we present a new line detection and matching algorithm that is invariant to image scale variation (SILT). The algorithm detects line segments as local extrema in the scale-space. Each detected line segment is represented in a distinctive manner using Haar-like features. PCA is further deployed to improve upon the compactness and robustness of representation. Experimental results demonstrate the effectiveness of the proposed approach to deal with image scale variations.

关键词： image segmentation Layout computer vision Object recognition Robustness Lighting Detection algorithms Pattern analysis Machine intelligence Pattern matching

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Locating texture boundaries using a fast unsupervised approach based on clustering algorithms fusion and level set

Locating texture boundaries using a fast unsupervised approa...

引用

IEEE International Conference on Signal and image Processing Applications (ICSIPA)

作者： Mehryar Emambakhsh Mohammad Hossein Sedaaghi Hossein Ebrahimnezhad Computer Vision Laboratory Department of Electrical Engineering Sahand University of Technology Tabriz Iran Department of Electrical Engineering Sahand University of Technology Tabriz Iran

image segmentation deals with partitioning an input image into disjoint/non-overlapping regions. Among different segmentation algorithms, level set methods have been very popular. Less sensitivity to initialization, ability to split and merge the contour, and also, involving statistical inference have made level set even more accepted than similar methods like snakes. However, it is very time-consuming. To solve this problem, in this paper a fast variational approach is presented for texture segmentation. For this purpose, first a feature space based on non-linear diffusion is set up from CIE L*a*b* colour components. Then, this feature space is clustered by fusion of clustering algorithms. Finally, the produced cluster map is used in level set for contour evolution. As it is shown in the simulation results, our algorithm is robust in segmenting noisy texture. Also, it is faster than previous level set approaches for texture segmentation.

关键词： Clustering algorithms Level set image segmentation Partitioning algorithms Robustness Cities and towns Biomedical imaging Diffusion tensor imaging Signal processing image processing

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A variational framework for 3D colonic polyp visualization in virtual colonoscopy

A variational framework for 3D colonic polyp visualization i...

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IEEE International Conference on image Processing

作者： Dongqing Chen Aly A. Farag Robert L. Falk Gerald W. Dryden Computer Vision & Image Processing (CVIP) Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Jewish Hospital Louisville KY USA Division of Gastroenterology/Hepatology University of Louisville Louisville KY USA

Colorectal cancer includes cancer of the colon, rectum, anus and appendix. Since it is largely preventable, it is extremely important to detect and treat the colorectal cancer in the earliest stage. Virtual colonoscopy is an emerging screening technique for colon cancer. One component of virtual colonoscopy, image pre-processing, is important for colonic polyp detection/ diagnosis, feature extraction and classification. This paper introduces a general variational approach based framework for a computer aided diagnosis system for colorectal cancer. It includes techniques for 3D colon segmentation, 3D colon object reconstruction by iso-surface generation, and 3D centerline extraction. The proposed framework has been validated on 22 real CT colonography datasets.

关键词： Colonic polyps Visualization Virtual colonoscopy Cancer Colon Colonography image segmentation Optical computing Computed tomography image reconstruction

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Automatic MRI brain segmentation using local features, Self-Organizing Maps, and watershed

Automatic MRI brain segmentation using local features, Self-...

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IEEE International Conference on Signal and image Processing Applications (ICSIPA)

作者： Mehryar Emambakhsh Mohammad Hossein Sedaaghi Computer Vision Laboratory Department of Electrical Engineering Sahand University of Technology Sahand New Iran Department of Electrical Engineering Sahand University of Technology Sahand New Iran

image segmentation is the process of partitioning an input image into non-overlapping/disjoint regions. Various methods have been used for image segmentation. Among these methods, watershed-based algorithms have been widely utilized due to their fast computational speed. However, their sensitivity to noise, and also over-segmentation, has made watershed approaches unsuitable for noisy images. In this paper, a novel method for MRI segmentation is proposed. For this purposed, a simple and fast feature extraction method is used. Then, the feature space, is clustered by Self-Organizing Map Neural Networks (SOMNN). After that, an edge map is set up from the clustering result. Finally, watershed transformation is utilized on the edge map. Our algorithm is robust against noise. Although watershed transformation is used in our approach, a region merging and denoising algorithms are not utilized as pre- and post- processing, respectively. This significantly improves the segmentation speed.

关键词： Magnetic resonance imaging Self organizing feature maps image segmentation Clustering algorithms Biomedical imaging Level set Humans image processing Application software computer vision

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Building detection using local Gabor features in very high resolution satellite images

Building detection using local Gabor features in very high r...

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International Conference on Recent Advances in Space Technologies (RAST)

作者： Beril Sirmacek Cem Unsalan Computer Vision Research Laboratory Department of Electrical and Electronics Engineering Yeditepe University Istanbul Turkey Yeditepe Universitesi Istanbul TR

Building detection from very high resolution satellite imagery is an important task for land planners. However, manually locating buildings from these images is a difficult and time consuming process. Therefore, researchers focused on building detection using automated image processing and computer vision techniques. The main problems here are as follows. Buildings have diverse characteristics and their appearance (illumination, viewing angle, etc.) is uncontrolled. On the other hand, buildings often have similar cues like parallel edges and roof corners that can be merged. In this study, we propose an automated approach for building detection based on Gabor filters and spatial voting. We extract features (representing buildings) using Gabor filter responses. Using these features, we form a spatial voting matrix to detect buildings. We tested our algorithm on very high resolution grayscale Ikonos satellite images and obtained promising results.

关键词： image resolution Satellites Gabor filters Voting image processing computer vision Lighting image edge detection Feature extraction Testing

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Gait Recognition Using Wavelet Packet Silhouette Representation and Transductive Support Vector Machines

Gait Recognition Using Wavelet Packet Silhouette Representat...

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Congress on image and Signal Processing, CISP

作者： Farzin Dadashi Babak N. Araabi Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI USA

Gait is an idiosyncratic biometric that can be used for human identification at a distance and as a result gained growing interest in intelligent visual surveillance. In this paper, an efficient gait recognition method based on describing subject outer body contour deformations using wavelet packets is proposed. With the use of matching pursuit algorithm, k bases of wavelet packet tree that have maximum similarity to the signal are selected and corresponding coefficients are used as features. Finally, transductive support vector machine (TSVM) classification is utilized on computed eigengait space for semi-supervised identification. The proposed method of selecting features which uses a complete orthogonal or near orthogonal basis from a wavelet packet library of bases and investigating the correlational structure of gait features for each individual using TSVM, result in encouraging identification performance.

关键词： Wavelet packets Support vector machines Matching pursuit algorithms Biometrics Humans Machine intelligence Surveillance Pursuit algorithms Support vector machine classification Libraries

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Accurate and fast 3D colon segmentation in CT colonography

Accurate and fast 3D colon segmentation in CT colonography

引用

IEEE International Symposium on Biomedical Imaging

作者： Dongqing Chen Rachid Fahmi Aly A. Farag Robert L. Falk Gerald W. Dryden Computer Vision & Image Processing (CVIP) Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Department of Medical Imaging Jewish Hospital and Saint Marys Healthcare Louisville KY USA Division of Gastroenterology/Hepatology Department of Medicine University of Louisville Louisville KY USA

ISBN: (纸本)9781424439317

This paper introduces an adaptive level set method for 3D segmentation of colon tissue in CT colonography filled with air and opacified fluid. First, most of the opacified liquid is removed by a threshold value. The closed contours are propagated toward the desired 3D region boundaries through the iterative evolution of the adaptive level sets function. The proposed method has been tested on 22 real CT colonography datasets with various pathologies, and the segmentation accuracy has achieved 98.40%.

关键词： Colonography Colon Virtual colonoscopy Cancer image segmentation Colonic polyps Level set Deformable models Computed tomography Visualization

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Kernel-based Linear Spectral Mixture analysis for hyperspectral image classification

Kernel-based Linear Spectral Mixture Analysis for hyperspect...

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Workshop on Hyperspectral image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Keng-Hao Liu Englin Wong Chein-I Chang Remote Sensing Signal and Image Processing Laboratory the Department of Computer Science and Electrical Engineering Department University of Maryland Baltimore MD USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

Linear spectral mixture analysis (LSMA) has been widely used in remote sensing community. Recently, kernel-based approaches have received considerable interest in hyperspectral image analysis where nonlinear kernels are used to resolve the issue of nonlinear separability in classification. This paper extends the LSMA to kernel-based LSMA where three least squares-based LSMA techniques, least squares orthogonal subspace projection (LSOSP), non-negativity constrained least squares (NCLS) and fully constrained least squares (FCLS) are extended to their kernel counterparts, KLSOSP, KNCLS and KFCLS.

关键词： image analysis Spectral analysis Hyperspectral imaging image classification Least squares methods Hyperspectral sensors Kernel Subspace constraints Remote sensing image resolution

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