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检索条件"机构=Comp. Vis. and Image Proc. Lab."

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Fine-Grained Blind Face Inpainting with 3D Face comp.nent Disentanglement 48

Fine-Grained Blind Face Inpainting with 3D Face Component Di...

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48th IEEE International Conference on Acoustics, Speech and Signal proc.ssing, ICASSP 2023

作者： Bai, Yu He, Ruian Tan, Weimin Yan, Bo Lin, Yangle Fudan University School of Computer Science Shanghai Key Lab. of Intelligent Info. Proc. Shanghai Collab. Innov. Ctr. of Intelligent Vis. Comp. Shanghai China

ISBN: (纸本)9781728163277

Inpainting is a task to restore occlusion or other corruption on images. However, previous works require mask of the occluded area to restore the occluded image, which is inconvenient for application. Blind face inpainting aims to automatically restore the occluded face without position information of the corrupt region. In this paper, we propose a novel fine-grained blind face inpainting framework, combining 3D face comp.nents disentanglement with generative network. Canonical face texture and shape disentangled by unsupervis.d 3D face model is restored separately to get occlusion-free rendered result. Finally, the pixel-to-pixel generative module utilize the occlusion image and the coarse de-occlusion face to get refined inpainted result. We also build up a new dataset called CelebO-3D which consists of occluded face images synthesized with 3D occlusion and rendered by 3D face model. Extensive experiments show that the proposed method is effective and robust in face blind inpainting both in synthesized and real images. Extensive evaluations and comp.rison with previous methods also show our superior effectiveness and lightweight architecture. © 2023 IEEE.

关键词： Pixels

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Data modeling for content-based support environment (C-BASE): Application on epilepsy data mining

Data modeling for content-based support environment (C-BASE)...

引用

17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007

作者： Siadat, Mohammad-Reza Soltanian-Zadeh, Hamid Fotouhi, Farshad Eetemadi, Ameen Elisevich, Kost Department ofComputer Science and Engineering Oakland University Rochester MI 48309 United States Radiology Image Analysis Lab. Henry Ford Health System Detroit MI 48202 United States Control and Intelligent Proc. Center of Excellence Dept. of Elec. and Comp. Eng. Univ. of Tehran Tehran 14395-515 Iran Department of Computer Science Wayne State University Detroit MI 48202 United States Department of Neurosurgery Henry Ford Health System Detroit MI 48202 United States

ISBN: (纸本)0769530192

Data Modeling is an essential first step for data preparation in any data mining proc.dure. Conventional entity-relational (E-R) data modeling is lossy, irreproducible, and time-consuming especially when dealing with unstructured image data associated with comp.ex systems like the human brain. We propose a methodological framework for more objective E-R data modeling by eliminating the structured content-dependent metadata associated with the unstructured data. The proposed method is applied to epilepsy-related image data and a system called the human brain image database system (HBIDS) is developed accordingly. Supported with navigation, segmentation, data fusion, and feature extraction modules, HBIDS provides a content-based support environment (C-BASE). Such an environment potentially provides an unlimited (ad hoc) query support with a reproducible and efficient database schema. Switching between different modalities of data, while confining the feature extractors within the object(s) of interest, HBIDS yields anatomically specific query results. The price of such scheme is large storage requirements and relatively high comp.tational cost. Examples of navigation through unstructured image data and content-based retrieval are presented in this paper. The results show the potential of HBIDS in content-based data management for decision support systems in real life medical applications. © 2007 IEEE.

关键词： Data mining

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Mean field theory for density estimation using support vector machines

Mean field theory for density estimation using support vecto...

引用

proc.edings of the Seventh International Conference on Information Fusion, FUSION 2004

作者： Mohamed, Refaat M. Farag, Aly A. Comp. Vis. and Image Proc. Lab. University of Louisville Louisville KY 40292

ISBN: (纸本)917056115X

This paper presents a novel algorithm for density estimation which is based on the support vector machines (SVM) approach and it uses the Mean Field (MF) theory for developing an easy and efficient learning proc.dure for the SVM. The traditional formulation of the SVM density estimation decomp.ses the parameters of the problem into a quadratic optimization which can be solved using standard optimization techniques. The proposed algorithm approximates the distribution of the SVM parameters as a Gaussian proc.ss and uses the Mean Field theory to easily estimate these parameters. The new algorithm selects the weights of the mixture of kernels used in the SVM estimate more accurately and faster than traditional quadratic programming-based algorithms. The performance of the proposed algorithm is illustrated using a number of simulated densities. The evaluation shows that the method provides satisfactory results while keeping a reasonable convergence speed.

关键词： Learning systems

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On the fusion of 3-D reconstruction techniques

On the fusion of 3-D reconstruction techniques

引用

proc.edings of the Seventh International Conference on Information Fusion, FUSION 2004

作者： Eid, Ahmed Farag, Aly Comp. Vis. and Image Proc. Lab. University of Louisville Louisville KY 40292 United States

ISBN: (纸本)917056115X

The commercial 3-D laser scanners provide high quality reconstructions of different surfaces. However if the reconstructed surfaces are made of diffusing materials and/or exhibit self occluding properties then errors are guaranteed in the output reconstruction. In this paper, we try to rectify this situation by fusing the 3-D reconstructions generated by a 3-D laser scanner and other 3-D reconstruction techniques that may not exhibit similar reconstruction errors. We propose a fusion technique that employees a 3-D alignment criterion and a local quality assessment technique as well. The fusion decision in the proposed technique is guided by the extracted contours from the input images of the object under-reconstruction. The approach can be applied to different 3-D reconstructions from different sensors. The approach exhibits promising results on the fusion of the space carving technique and the 3-D reconstruction by a 3-D laser scanner.

关键词： image reconstruction

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Automatic adaptation of a human face model for model-based coding

Automatic adaptation of a human face model for model-based c...

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Picture Coding Symposium 2004

作者： Hess, Mauricio Martinez, Geovanni Image Proc. and Comp. Vis. Res. Lab. Escuela de Ingenieria Electrica Universidad de Costa Rica Codigo postal 2060 San Jose Costa Rica

For coding of videophone sequences at very low bit rates, model-based coding is investigated. In a model-based coder, the human face in the videophone sequence is described by a three-dimensional (3D) face model. At the beginning of the videophone sequence, the model has to be adapted automatically to the shape, position and orientation of the real face present in the scene. In this paper, a new face model adaptation algorithm is presented. Facial features such as eye corners, mouth corners, chin and cheek borders, and nose corners are supposed to be known or previously estimated. First, the orientation, shape and position of the model are adapted using the positions of the mouth corners, outer corners of the eyes and top pixels of the cheek borders. Then, the shape of the model is locally adapted using the chin and cheek borders as well as the nose corners. The algorithm was implemented and applied to the first 40 frames of the videophone sequence Claire (CIF, 10Hz) with very encouraging results, even in cases when the person's eyes are closed or the person is facing downward.

关键词： image coding

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Facial feature extraction based on the Smallest Univalue Segment Assimilating Nucleus (SUSAN) Algorithm

Facial feature extraction based on the Smallest Univalue Seg...

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Picture Coding Symposium 2004

作者： Hess, Mauricio Martinez, Geovanni Image Proc. and Comp. Vis. Res. Lab Escuela de Ingenieria Electrica Universidad de Costa Rica Codigo postal 2060 San Jose Costa Rica

For coding of videophone sequences at very low bit rates, model-based coding is investigated. In a model-based coder, the human face in the videophone sequence is described by a three-dimensional (3D) face model. At the beginning of the videophone sequence, the model has to be adapted automatically to the size, position and orientation of the real face present in the scene. First, the face of the person is segmented in the current image. Then, facial features such as eye corners and centers, mouth corners and center, chin and cheek borders, nose corners, etc. are extracted. In the final step the model is adapted using the extracted facial features. In this paper, a new facial feature extraction algorithm is presented. First, all corners and borders are extracted using the Smallest Univalue Segment Assimilating Nucleus (SUSAN) Algorithm. Then, the facial features are detected by applying knowledge-based rules. The algorithm was implemented and applied to the first 40 frames of the videophone sequence Claire (CIF, 10Hz) with very encouraging results.

关键词： Feature extraction

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Clustering-based framework for comp.ring fMRI analysis methods

Clustering-based framework for comparing fMRI analysis metho...

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2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano

作者： Hossein-Zadeh, Gholam-Ali Golestani, Ali-Mohammad Soltanian-Zadeh, Hamid Elec. and Comp. Eng. Department Contr./Intelligent Proc. Ctr. E. University of Tehran Tehran 14395-515 Iran Image Analysis Lab. Radiology Department Henry Ford Health System Detroit MI 48202 United States

ISBN: (纸本)0780383885

In this paper, a cluster-based framework is introduced for comp.ring analysis methods of functional magnetic resonance images (fMRI). In the proposed framework, fMRI data is replaced with a feature space and each method considered as a clustering method in the new space. As a result, different methods can be comp.red by means of a cluster validity measure. The feature space is comp.ted using a non-parametric method (principal comp.nent analysis-PCA). Four subjects have been analyzed with three methods and the proposed cluster-based framework has evaluated performance of the methods. The results are identical to those of the modified receiver operating characteristics (ROC). This validates the proposed approach. © 2004 IEEE.

关键词： Magnetic resonance imaging

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Parameter estimation for bayesian classification of multispectral data

Parameter estimation for bayesian classification of multispe...

引用

7th International Conference, KES 2003

作者： Mohamed, Refaat M. Farag, Aly A. Comp. Vis. and Image Proc. Lab. University of Louisville Louisville KY 40292 United States

ISBN: (纸本)9783540408031

In this paper, we present two algorithms for estimating the parameters of a Bayes classifier for remote sensing multispectral data. The first algorithm uses the Support Vector Machines (SVM) as a multi-dimensional density estimator. This algorithm is a supervis.d one in the sense that it needs in advance, the specification of the number of classes and a training sample for each class. The second algorithm employs the Expectation Maximization (EM) algorithm, in an unsupervis.d way, for estimating the number of classes and the parameters of each class in the data set. Performance comp.rison of the presented algorithms shows that the SVM- based classifier outperforms those based on Gaussian-based and Parzen window algorithms. We also show that the EM based classifier provides comp.rable results to Gaussian-based and Parzen window-based while it is an unsupervis.d algorithm.

关键词： Parameter estimation

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image segmentation using GMRF models: Parameters estimation and applications

Image segmentation using GMRF models: Parameters estimation ...

引用

proc.edings: 2003 International Conference on image proc.ssing, ICIP-2003

作者： El-Baz, Ayman Farag, Aly A. Comp. Vis./Image Proc. Laboratory University of Louisville Louisville KY 40292

Stochastic models of images are commonly represented in terms of three random proc.sses (random fields) defined on the region of support of the image. The observed image proc.ss G is considered as a comp.site of two random proc.ss: a high level proc.ss Gh, which represents the regions (or classes) that form the observed image;and a low level proc.ss G′, which describes the statistical characteristics of each region (or class). The representation G = (Gh, Gl ) has been widely used in the image proc.ssing literature in the past two decades. In this paper, we consider the low level proc.ss G′ as mixture of normal distributions, and we use the Expectation-Maximization (EM) algorithm to estimate the mean, the variance, and proportion for each distribution. A popular model for the high level proc.ss Gh has been the Gibbs-Markov random field (GMRF) model. We introduce a novel unsupervis.d approach to estimate the parameters of a GMRF model. In this approach, we estimate the model parameters that maximize the posteriori probability of each pixel in a given image. The MAP estimate is obtained using a combination of genetic search and deterministic optimization using the iterated conditional mode (ICM) approach of Besag. The desired estimate of the GMRF parameters is the one corresponding to the MAP estimate. The approach has been applied on real images (Spiral CT slices) and provides satisfactory results.

关键词： image segmentation

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Statistical-based approach for extracting 3D blood vessels from TOF-MyRA data

Statistical-based approach for extracting 3D blood vessels f...

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Medical image comp.ting and comp.ter-Assisted Intervention, MICCAI 2003 - 6th International Conference proc.edings

作者： Hassouna, M. Sabry Farag, Aly A. Hushek, Stephen Moriarty, Thomas Comp. Vis. and Image Proc. Lab. University of Louisville Louisville KY 40292 Department of Neurological Surgery University of Louisville KY 40292

In this paper we present an automatic statistical intensity based-approach for extracting the 3D cerebrovascular system from time-of-flight (TOP) magnetic resonance angiography (MRA) data. The voxels of the dataset are classified as either background tissues, which are modeled by a finite mixture of one Rayleigh and two normal distributions, or blood vessels, which are modeled by one normal distribution. We show that the proposed models fit the clinical data properly and result in fewer misclassified vessel voxels. We estimated the parameters of each distribution using the expectation maximization (EM) algorithm. Since the convergence of the EM is sensitive to the initial estimate of the parameters, a novel method for parameter initialization, based on histogram analysis, is provided. A new geometrical phantom motivated by a statistical analysis was designed to validate the accuracy of our method. The algorithm was also tested on 20 in-vivo datasets. The results showed that the proposed approach provides accurate segmentation, especially those blood vessels of small sizes.

关键词： Medical imaging

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