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检索条件"机构=Computer Vision and Image Processing"

共 524 条记录，以下是151-160 订阅

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Analytic Bilinear Appearance Subspace Construction for Modeling image Irradiance Under Natural Illumination and Non-Lambertian Reflectance

Analytic Bilinear Appearance Subspace Construction for Model...

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IEEE Conference on computer vision and Pattern Recognition

作者： Shireen Y. Elhabian Aly A. Farag Computer Vision and Image Processing Laboratory ECE Dept. University of Louisville

ISBN: (纸本)9781467364102

Conventional subspace construction approaches suffer from the need of "large-enough" image ensemble rendering numerical methods intractable. In this paper, we propose an analytic formulation for low-dimensional subspace construction in which shading cues lie while preserving the natural structure of an image sample. Using the frequency-space representation of the image irradiance equation, the process of finding such subspace is cast as establishing a relation between its principal components and that of a deterministic set of basis functions, termed as irradiance harmonics. Representing images as matrices further lessen the number of parameters to be estimated to define a bilinear projection which maps the image sample to a lower-dimensional bilinear subspace. Results show significant impact on dimensionality reduction with minimal loss of information as well as robustness against noise.

关键词： Lighting Harmonic analysis Principal component analysis image reconstruction Materials Gold Surface reconstruction

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CONTOURLET TRANSFORM BASED MOVING OBJECT SEGMENTATION

CONTOURLET TRANSFORM BASED MOVING OBJECT SEGMENTATION

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IEEE Conference on Information & Communication Technologies

作者： Manish Khare Swati Nigam Rajneesh Kumar Srivastava Ashish Khare Image Processing and Computer Vision Lab Department of Electronics and Communication University of Allahabad

ISBN: (纸本)9781467357593

Moving object segmentation is an important step toward development of any computer vision systems. In the present work, we have proposed a new method for segmentation of moving objects, which is based on single change detection method applied on Contourlet coefficients of two consecutive frames. We have chosen contourlet transform as it has high directionality and represents salient features of image such as edges, curves and contours in better way as compared with wavelet transform. The proposed method is simple and does not require any other parameter except contourlet coefficients. Results after applying the proposed method for segmentation of moving objects are compared with other state-of-the-art methods in terms of visual as well as quantitative performance measures viz. Average difference, Normalized absolute error and Pixel classification based measure. The proposed method is found to be better than other methods.

关键词： Contourlet transform Moving object segmentation Single change detection Performance measures for segmentation

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Clinically desired segmentation method for vertebral bodies

Clinically desired segmentation method for vertebral bodies

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IEEE International Symposium on Biomedical Imaging

作者： Melih S. Aslan Ahmed Shalaby Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

ISBN: (纸本)9781467364560

In this paper, we propose a clinically desired segmentation method for vertebral bodies (VBs) in computed tomography (CT) images. Three pieces of information (intensity, spatial interaction, and shape) are modeled to optimize a new probabilistic energy functional; and hence to obtain the optimum segmentation. The information of the intensity and spatial interaction are modeled using the Gaussian and Gibbs distribution, respectively. A shape model is proposed using a new probabilistic function to enhance the segmentation results. This model is a generic shape information which is obtained using the cervical, lumbar, and thoracic spinal regions. We propose a semiautomated segmentation algorithm which uses limited interventions only in the VB separation process. The overall segmentation process completes the task in very low execution time which is one of the most important contribution of this paper. The proposed method is validated with clinical CT images and on a phantom with various Gaussian noise levels. This study reveals that the proposed method is robust under various noise levels, less variant to the initialization, and quite faster than alternative methods. One of the most important contributions of our paper is to offer a segmentation framework which can be suitable to the clinical works with acceptable results.

关键词： image segmentation Shape Computed tomography Probabilistic logic Object segmentation Level set Training

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BIORTHOGONAL WAVELET TRANSFORM BASED image FUSION USING ABSOLUTE MAXIMUM FUSION RULE

BIORTHOGONAL WAVELET TRANSFORM BASED IMAGE FUSION USING ABSO...

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IEEE Conference on Information & Communication Technologies

作者： Om Prakash Richa Srivastava Ashish Khare Image Processing and Computer Vision Lab Department of Electronics and Communication University of Allahabad

ISBN: (纸本)9781467357593

The objective of image fusion is to combine relevant information from two or more images of the same scene into a single composite image which is more informative and is more suitable for human and machine perception. In recent past, different methods of image fusion have been proposed in literature both in spatial domain and wavelet domain. Spatial domain based methods produce spatial distortions in the fused image. Spatial domain distortion can be well handled by the use of wavelet transform based image fusion methods. In this paper, we propose a pixel-level image fusion scheme using multiresolution Biorthogonal wavelet transform (BWT). Wavelet coefficients at different decomposion levels are fused using absolute maximum fusion rule. Two important properties wavelet symmetry and linear phase of BWT have been exploited for image fusion because they are capable to preserve edge information and hence reducing the distortions in the fused image. The performance of the proposed method have been extensively tested on several pairs of multifocus and multimodal images both free from any noise and in presence of additive white Gaussian noise and compared visually and quantitatively against existing spatial domain methods. Experimental results show that the proposed method improves fusion quality by reducing loss of significant information available in individual images. Fusion factor, entropy and standard deviation are used as quantitative quality measures of the fused image.

关键词： image fusion Multifocus images Multimodality Biorthogonal wavelet transform Fusion rules

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LICENSE PLATE LOCALIZATION BASED ON EDGE-GEOMETRICAL FEATURES USING MORPHOLOGICAL APPROACH

LICENSE PLATE LOCALIZATION BASED ON EDGE-GEOMETRICAL FEATURE...

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IEEE International Conference on image processing

作者： Jinn-Li Tan Syed A.R Abu-Bakar Musa M. Mokji Computer Vision Video and Image Processing Lab ECE Department Universiti Teknologi Malaysia

ISBN: (纸本)9781479923427

Malaysian car plates in general appear in different character styles, types (either single or double row), sizes, spacing and character counts. Such variations cause even detecting and localizing these plates a difficult problem. The problem of localization is aggravated further during night time due to poor illumination. In this paper, we introduce the idea of edgegeometrical features in detecting these plates. The edge part is obtained from the use of Difference of Gaussian operation followed by Sobel vertical edge mask. Prior to that, gamma correction is applied to increase the detection of edges. We then apply morphological operations to get the plate region candidates. Using these regions, together with the edge image, we calculate geometrical features of these regions and use rule-based classifier to correctly identify the true plate region. Finally, we test out method using our own data set which contained 250 images captured during day time and 100 images captured during night time. The result of the proposed method shows 96.9% success rate.

关键词： Gamma correction Difference of Gaussian Sobel vertical mask Rule-based classifier Bone Plate PLATES edge image plate Licence Plates Edge detection Morphological operations double-row

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Heat front propagation contours for 3D face recognition

Heat front propagation contours for 3D face recognition

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IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS)

作者： Mostafa Abdelrahman Aly A. Farag Moumen El-Melegy Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA University of Louisville Louisville KY US

Face recognition is a key biometric method aiming at identifying individuals by the features of face. Due to the challenges facing face recognition from 2D images, researchers have resorted to 3D face recognition. Our work in this paper is motivated by the recent and remarkable success of heat-based features for 3D object classification and retrieval. We propose an approach for 3D face recognition based on the front contours of heat propagation over the face surface. The front contours are extracted automatically as heat is propagating starting from a detected set of landmarks. The propagation contours are used to successfully discriminate the various faces. The proposed approach is evaluated on the largest publicly available database of 3D facial images and successfully compared to the state-of-the-art approaches in the literature.

关键词： Face Heating Three-dimensional displays Face recognition Mathematical model Kernel Equations

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Single Cluster PHD SLAM: Application to Autonomous Underwater Vehicles using Stereo vision

Single Cluster PHD SLAM: Application to Autonomous Underwate...

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Oceans 2013 - Norway

作者： Sharad Nagappa Narcis Palomeras Chee Sing Lee Nuno Gracias Daniel E. Clark Joaquim Salvi Computer Vision and Robotics Group University of Girona Joint Research Institute in Signal and Image Processing Heriot-Watt University

ISBN: (纸本)9781479900022

This paper considers the application of feature-based simultaneous localisation and mapping (SLAM) using a random finite sets (RFS) framework for an autonomous underwater vehicle. SLAM allows for reduction in localisation error by tracking features which provide a fixed external reference. The SLAM problem is addressed here using a single-cluster probability hypothesis density (PHD) filter. The filter uses a particle approximation for the vehicle position with a conditional Gaussian mixture PHD for the feature map. Map features are selected as unique point features generated from a stereo camera on-board the vehicle. We demonstrate the improvement in localisation applying the algorithm to a dataset obtained in an indoor test tank.

关键词： Autonomous underwater vehicles Stereoviews supersonic low altitude missile underwater vehicles Automatic vehicle location Stereo cameras simultaneous localization and mapping

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SHREC'13 Track: Retrieval of objects captured with low-cost depth-sensing cameras 6

SHREC'13 Track: Retrieval of objects captured with low-cost ...

引用

6th Eurographics Workshop on 3D Object Retrieval, 3DOR 2013

作者： Machado, J. Ferreira, A. Pascoal, P.B. Abdelrahman, M. Aono, M. El-Melegy, M. Farag, A. Johan, H. Li, B. Lu, Y. Tatsuma, A. INESC-ID IST Technical University of Lisbon Portugal Microsoft Language Development Center Portugal Toyohashi University of Technology Japan Department of Computer Science Texas State University San Marcos United States Fraunhofer IDM at NTU Singapore Singapore Computer Vision and Image Processing Laboratory University of Louisville Louisville United States

ISBN: (纸本)9783905674446

The SHREC'13 Track: Retrieval of Objects Captured with Low-Cost Depth-Sensing Cameras is a first attempt at evaluating the effectiveness of 3D shape retrieval algorithms in low fidelity model databases, such as the ones captured with commodity depth cameras. Both target and query set are composed by objects captured with a Kinect camera and the objective is to retrieve the models in the target set who were considered relevant by a human-generated ground truth. Given how widespread such devices are, and how easy it is becoming for an everyday user to capture models in his household, the necessity of algorithms for these new types of 3D models is also increasing. Three groups have participated in the contest, providing rank lists for the set of queries, which is composed of 12 models from the target set. © The Eurographics Association 2013.

关键词： Cameras

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DEFORMABLE MODELS FOR RANDOM SMALL-SIZE OBJECTS: CASE OF LUNG NODULES IN CT TOMOGRAPHY

DEFORMABLE MODELS FOR RANDOM SMALL-SIZE OBJECTS: CASE OF LUN...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Amal A. Farag James H. Graham Aly A. Farag Imaging Biomarkers and Computer-Aided Diagnosis Laboratory National Institutes of Health Bethesda MD Computer Vision and Image Processing Laboratory University of Louisville Louisville KY

ISBN: (纸本)9781479903573

Deformable models are common in image modeling and analysis. Random objects provide major challenges as shapes and appearances are hard to quantify;hence, formulation of deformable models are much harder to construct and validate. In this paper, we examine the effect of randomness on building the shape and appearance models for small-size lung nodules (< 1cm) which appear in computed tomography (CT) of the human chest. We devise an approach for annotation, which lends a standard mechanism for building traditional active appearance (AAM), active shape (ASM) and active tensor models (ATM). We illustrate the effectiveness of AAM for nodule detection.

关键词： CT ASM AAM Army Achievement Medal X-Ray Computed Tomography Deformable models computer aided software engineering Packaging Case Root nodules Shape Lung nodules Tomography ANNOTATIONS

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Automatic normal-abnormal video frame classification for colonoscopy

Automatic normal-abnormal video frame classification for col...

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IEEE International Symposium on Biomedical Imaging

作者： Siyamalan Manivannan Ruixuan Wang Emanuele Trucco Adrian Hood CVIP Computer Vision and Image Processing group School of Computing University of Dundee UK Leeds Institute of Molecular Medicine University of Leeds UK

ISBN: (纸本)9781467364560

Two novel schemes are proposed to represent intermediate-scale features for normal-abnormal classification of colonoscopy images. The first scheme works on the full-resolution image, the second on a multi-scale pyramid space. Both schemes support any feature descriptor; here we use multi-resolution local binary patterns which outperformed other features reported in the literature in our comparative experiments. We also compared experimentally two types of features not previously used in colonoscopy image classification, bag of features and sparse coding, each with and without spatial pyramid matching (SPM). We find that SPM improves performance, therefore supporting the importance of intermediate-scale features as in the proposed schemes for classification. Within normal-abnormal frame classification, we show that our representational schemes outperforms other features reported in the literature in leave-N-out tests with a database of 2100 colonoscopy images.

关键词： image color analysis Feature extraction Colonoscopy Histograms Accuracy Hemorrhaging Lesions

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