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

共 343 条记录，以下是101-110 订阅

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MODELING image IRRADIANCE UNDER NATURAL ILLUMINATION AND ISOTROPIC SURFACE REFLECTANCE

MODELING IMAGE IRRADIANCE UNDER NATURAL ILLUMINATION AND ISO...

引用

IEEE International Conference on image processing

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

Discounting subsurface scattering and surface emittance, this work seeks to address the question;how to accurately represent image irradiance under unknown general illumination and reflectance, given that a surface point sees its surrounding world through the local upper hemisphere oriented by the surface normal at this point. In this paper, we formulate the image formation process of isotropic surfaces under arbitrary distant illumination in the frequency space while addressing the physical compliance of hemispherical basis for representing surface reflectance, e.g. Helmholtz reciprocity and isotropy. This representation is further reduced in dimensionality through analytically deriving the principal components of the image irradiance. A database of natural illumination and real world surface materials are used to compute a general-purpose basis which captures the full behavior of illumination and reflectance in a lower-dimensional subspace for image irradiance representation. Compared to similar basis in literature, our proposed basis achieves higher accuracy levels at lower illumination orders.

关键词： Lighting Surface treatment Materials Harmonic analysis Eigenvalues and eigenfunctions Rendering (computer graphics) Databases

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Dynamic Weighting of Facial Features for Automatic Pose-Invariant Face Recognition

Dynamic Weighting of Facial Features for Automatic Pose-Inva...

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Canadian Conference on computer and Robot vision, CRV

作者： Eslam A. Mostafa Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

This paper proposes an automatic pose-invariant face recognition system. In our approach, we consider the texture information around the facial features to compute the similarity measure between the probe and gallery images. The weight of each facial feature is dynamically estimated based on its robustness to the pose of the captured image. An approach to extract the 9 facial features used to initialize the Active shape model is proposed. The approach is not dependent on the texture around the facial feature only but incorporates the information obtained about the facial feature relations. Our face recognition system is tested on common datasets in pose evaluation CMU-PIE and FERET. The results show out-performance of the state of the art automatic face recognition systems.

关键词： Facial features Face Shape Face recognition Feature extraction Detectors Active appearance model

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Computationally light forehead segmentation from thermal images

Computationally light forehead segmentation from thermal ima...

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IEEE International Conference on image processing

作者： Travis R. Gault Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

Thermal images are often paired with visible spectrum images captured simultaneously-as surface features are easier to identify with proper illumination-to determine the thermal feature locations. Not all thermal images are necessarily recorded this way, yet determining salient feature regions is beneficial to other tasks. The forehead region is useful to the field of vital signs analysis and the goal of this work is automatically identifying the forehead region solely from thermal images. The anatomy of the forehead and its thermo-dynamic properties lend itself to minimizing the variance of a facial thermogram. Experiments conducted indoors on 32 subjects under normal, exercise and pain conditions. The proposed solution identifies the forehead region with 90-95% accuracy in 80ms or less, without initialization or training data, and can be easily implemented in parallel.

关键词： Forehead Skin Hair image segmentation Imaging Blood Heating

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New Approach for Classification of Autistic vs. Typically Developing Brain Using White Matter Volumes

New Approach for Classification of Autistic vs. Typically De...

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Canadian Conference on computer and Robot vision, CRV

作者： Mostafa Abdelrahman Asem Ali Ahmed Farag Manuel F. Casanova Aly Farag Computer Vision & Image Processing Laboratory (CVIP) Univ. of Louisville Louisville KY Department of Psychiatry and Behavioral Sciences Univ. of Louisville Louisville KY

Autism is a complex developmental disability, characterized by deficits in social interaction, communication skills, range of interests, and occasionally the presence of stereotyped behaviors. Several studies show that changes in brain weight and volume over aging follow a unique trajectory in those affected by the condition~\cite{MICCAIMost00}. In this work, we develop a robust technique for evaluating the volume of white matter (WM), and use it as the main classification criteria. We perform MRI-based analysis on the brains of 14 autistic and 28 control subjects, male and female between aged 7 to 38 years. The proposed framework consists of several stages. First, the entire T1-weighted MRI scans are filtered out from noise using anisotropic diffusion filter. Then, the white matter (WM) is segmented from the skull. The segmentation framework is the search for maximum-a-posterior configurations in a Markov Gibbs Random Field (MGRF) model. A 3D mesh is then generated from the segmented WM. Finally, the volume of the 3D mesh is computed using a new algorithm. The experiments show accurate classification results of the proposed framework.

关键词： Autism image segmentation Solid modeling Magnetic resonance imaging Object segmentation Labeling Visualization

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Rapid shallow coastal coral reef mapping using the teardrop system

Rapid shallow coastal coral reef mapping using the teardrop ...

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OCEANS 2012 MTS/IEEE Hampton Roads Conference: Harnessing the Power of the Ocean

作者： Corpuz, Francis James Naval Jr., Prospero Capili, Eusebio Jauod, Jaylord Judilla, Roel John Soriano, Maricor Vision and Image Processing Group National Institute of Physics University of the Philippines Diliman Quezon City Philippines Computer Vision and Machine Intelligence Group Department of Computer Science University of the Philippines Diliman Quezon City Philippines Mapua Robotics Center Institutional Laboratory Mgmt Office Mapua Institute of Technology Intramuros Manila Philippines

ISBN: (纸本)9781467308298

We present a rapid shallow coastal reef mapping system using a previously developed inexpensive diver less video capture system coupled with a fast-image mosaicking technique, using an affordable GPS logging device. The system replaces the traditional manta tow system of assessing and monitoring large areas of coastal coral reefs and at the same time provides historical visual record for future references. The system utilizes Teardrop, a hollow, teardrop-shaped that houses a commercial underwater point-and-shoot camera. The camera is set to video mode and captures video of the reef while the Teardrop is being towed by a wooden fishing boat. The resulting video is parsed into frames and mosaicked by using fast image labeling. The approximate location of the mosaicked images is recorded by using an inexpensive GPS logger. The coordinates are embedded on the mosaicked images and overlaid on mapping software such as Google Earth for visual record and spatial information integration. The same points are used to compute for the total distance covered by the capturing process. The whole process from coral reef video capture to mosaicking the images and finally embedding the mosaics in mapping software can be done in as short as 1 day, providing coastal managers and local government units with a tool to rapidly visualize and monitor shallow coral reefs. © 2012 IEEE.

关键词： Mapping

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Small-size lung nodule modeling and detection with clinical evaluation

Small-size lung nodule modeling and detection with clinical ...

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Cairo International Biomedical Engineering Conference, CIBEC

作者： Amal Farag James Graham Hossam Abdelmunim Salwa Elshazly Mohamed Ei-Mogy Sabry Ei-Mogy Robert Falk Aly A. Farag Computer Vision and Image Processing Laboratory (CVIP Lab) University of Louisville Louisville KY Computer & Systems Engineering Department Ain Shams University Cairo Mogy-Scan Center Mansoura Egypt Jewish Hospital and 3DR Louisville Kentucky

In this paper examination of the template modeling process using the Active Appearance Modeling (AAM) approach for automatic detection of lung nodules is investigated. A template matching approach is formulated to compute a similarity score between the AAM templates and the input lung CT slice, where the goal is to maximize the similarity measure at different image pixels to increase nodule detection. The template matching approach is implemented using nine similarity measures. Performance validation for the robustness of the generated models is tested on three clinical databases.

关键词： Lungs Databases Computed tomography Principal component analysis Computational modeling Solid modeling

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Human action concentric video retrieval system using features weight updating method as relevance feedback

Human action concentric video retrieval system using feature...

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IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

作者： Munaf Rashid S. A. R. Abu-Bakar Musa Mokji Aliyu Abdu Micro Electronics and Computer Engineering Department Computer Vision Videos and image processing Laboratory (CVVIP) Universiti Teknologi Malaysia Johor Bahru Malaysia

Retrieving videos based on its contents is becoming an increasingly popular area of research, because of enormous growth in the availability of multimedia information on public databases like Google and YouTube. Usually videos contain large variety of data but majority of online videos contain human as a subject of interest. In this paper, a human action based video retrieval system is presented which can be used to retrieve videos based on the contents of the query. The proposed system can search videos containing particular action on large databases efficiently. Furthermore, it is also shown that by using features weight updating approach as a Relevance feedback (RF), it is possible to involve user concepts interactively so that complex human action queries can be searched quickly to achieve useful results. Three popular Human action datasets namely Weizmann, KTH and UCF (sports) have been utilized in order to validate the performance of the proposed system. Experimental results and simulations show the efficacy of the proposed system. Even with number of visual challenges proposed approach will manage to get better accuracy as compare to other existing methods.

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2D PCA-based shape prior for level sets segmentation framework of the vertebral body

2D PCA-based shape prior for level sets segmentation framewo...

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Cairo International Biomedical Engineering Conference, CIBEC

作者： Ahmed Shalaby Melih Aslan Hossam Abdelmunim Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville KY USA Computer & Systems Engineering Department Ain Shams University Cairo Egypt

In this paper, a novel statistical shape modeling method is developed for the vertebral body (VB) segmentation framework. Two-dimensional principle component analysis (2D-PCA) technique is exploited to extract the shape prior. The obtained shape prior is then embedded into the image domain to develop a new shape-based segmentation approach. Our framework consists of four main steps: i) shape model construction using 2D-PCA, ii) Detection of the VB region using the Matched filter, iii) Initial segmentation using the graph cuts which integrates the intensity and spatial interaction models, and iv) Registration of the shape prior and initially segmented region to obtain the final segmentation. The proposed method is validated on a Phantom as well as clinical CT images with various Gaussian noise levels. The experimental results show that the noise immunity and the segmentation accuracy of 2D-PCA based approach are much higher than conventional PCA approach.

关键词： Shape image segmentation Training Principal component analysis Accuracy Computed tomography Vectors

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Produce recognition system using data mining algorithm

Produce recognition system using data mining algorithm

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IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

作者： J. K. Chaw M. M. Mokji Computer Vision Video and Image Processing (CvviP) Laboratory Department of Microelectronic and Computer Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia Malaysia

Produce recognition system is a system that can categorize types of vegetables and fruits based on features extracted from the images. However, there are numerous features that can be extracted from fruits and vegetables such as colour, texture and shape. As a result, it is effort consuming to identify suitable features ad hoc. Thus, data mining is required to discover the most discriminative features for recognition. This paper aims to extend the usage of data mining algorithm to image domain. Data mining algorithm is preferred to other feature selection algorithms because it discovers nuggets of knowledge that can be understood by human whereas classic feature selection techniques provide outputs that can only be managed by learning algorithms.

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An AAM-based detection approach of lung nodules from LDCT scans

An AAM-based detection approach of lung nodules from LDCT sc...

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IEEE International Symposium on Biomedical Imaging

作者： Amal Farag Hossam Abdelmunim James Graham Aly A. Farag Cambron Carter Salwa Elshazly Mohamed S. El-Mogy Sabry El-Mogy Robert Falk Computer Vision and Image Processing Laboratory (CVIP Lab) University of Louisville Louisville KY 40292 University of Louisville Louisville KY US Mogy Scan Center Mansoura Egypt Jewish Hospital and 3DR Louisville Kentucky

ISBN: (纸本)9781457718571

In this, paper a new approach for lung nodules detection from LDCT scans is proposed. Intensity models of the nodules are generated using an active appearance model formulation. Template matching is used to compute a similarity score between the AAM template and the input image. The goal is to maximize the similarity measure at different image pixels to increase nodule detection. Conventional template matching does not account for rotation variations. Our proposed template matching approach is formulated as an energy optimization problem that computes a transformation that includes rotation(s) parameters as well as the AAM weighting coefficients. The approach is flexible to different scans and different nodule locations because of the ability to handle the variations in the rotation between the template and the input images. The approach can employ different similarity measures. Experimental results will be shown using three similarity measures from the literature: NCC, ZNCC and ZSSD; which illustrate the efficiency of the proposed approach. ROC curves for various nodule types are constructed on a clinical study with known ground truth, showing significant enhancements over conventional parametric nodule models and traditional template matching criterion.

关键词： Lungs Computational modeling Active appearance model Solid modeling Correlation Computed tomography Shape

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