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

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Heat Kernels for Non-Rigid Shape Retrieval: Sparse Representation and Efficient Classification

Heat Kernels for Non-Rigid Shape Retrieval: Sparse Represent...

引用

Canadian Conference on computer and Robot vision, CRV

作者： Mostafa Abdelrahman Moumen El-Melegy Aly Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Electrical Engineering Department Assiut University Asyut Egypt

One of the major goals of computer vision and machine intelligence is the development of flexible and efficient methods for shape representation. This paper presents an approach for shape retrieval based on sparse representation of scale-invariant heat kernel. We use the Laplace-Beltrami eigen functions to detect a small number of critical points on the shape surface. Then a shape descriptor is formed based on the heat kernels at the detected critical points for different scales, combined with the normalized eigen values of the Lap lace-Beltrami operator. Sparse representation is used to reduce the dimensionality of the calculated descriptor. The proposed descriptor is used for classification via the collaborative representation-based classification with regularized least square algorithm. We compare our approach to two well-known approaches on two different data sets: the nonrigid world data set and the SHREC 2011. The results have indeed confirmed the improved performance of the proposed approach, yet reducing the time and space complicity of the shape retrieval problem.

关键词： Shape Heating Eigenvalues and eigenfunctions Kernel Vectors Manifolds Heat transfer

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Coral reef mosaicking using Teardrop and Fast image Labeling

Coral reef mosaicking using Teardrop and Fast Image Labeling

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OCEANS 2012 MTS/IEEE Yeosu Conference: The Living Ocean and Coast - Diversity of Resources and Sustainable Activities

作者： Corpuz, Francis James Naval, 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 Department of Computer Science College of Engineering University of the Philippines Diliman Quezon City Philippines Mapua Robotics Center Special Projects Laboratory Mapua Institute of Technology Intramuros Manila Philippines

ISBN: (纸本)9781457720895

In this paper, we present an inexpensive system for diverless video capture and fast image stitching of image frames for rapid reef assessment of shallow coral reefs. Our system has two main components: 1) Teardrop, a boat-towable, winged hull apparatus designed to house a commercial digital camera, and 2) a mosaicking algorithm to stitch the coral reef video into mosaics for further appreciation and analysis. The captured reef video is then separated into image frames which are to be stitched sequentially using Fast image Labeling. The overlap between image frames is estimated using Single-Step DFT, an efficient sub-pixel estimation algorithm. The estimated overlap is used to compute for the area to be added to the mosaic space. The overlapping section between succeeding image-pairs are stitched along a seam determined by a minimal-cost path using dynamic programming. The visibility of the seam boundaries is further minimized by utilizing blending on multi-resolution splines. Experimental results on automated reef mosaics creation from actual coral reef video taken using Teardrop shows the performance of the system described. The main contribution of this work is the demonstration of a rapid reef visualization system using a diverless system and commercially available, non-research-grade imaging equipment. © 2012 IEEE.

关键词： Reefs

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Thermographic image analysis method in detection of canine bone cancer (osteosarcoma)

Thermographic image analysis method in detection of canine b...

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Congress on image and Signal processing, CISP

作者： Maryamsadat Amini Peng Liu Scott E Umbaugh Dominic J. Marino Catherine A. Loughin Computer Vision and Image Processing Laboratory Electrical and Computer Engineering Southern Illinois University Edwardsville IL USA Long Island Veterinary Specialists Plainview NY USA

Bone cancer is a pathologic condition which may occur for both humans and canines. This tumor develops quickly from within the bone tissue and become painful as it grows outward. A metastasized bone tumor may be cured by amputation, otherwise it will be fatal. The current diagnostic imaging methods for bone cancer are X-rays, Computed Tomography scan (CT scan), and Magnetic Resonance Imaging (MRI). The disadvantages of these methods include not enough detail in X-ray images, high dose of radiation from CT scans, and high expense of the time-consuming MRI method. In most of the bone cancer cases, when this tumor is detected by these imaging methods, it has already metastasized. The study is to investigate whether it is possible to detect canine bone cancer by thermography imaging. This alternative imaging method will decrease diagnostic time, expenses and prevent radiation exposure. The best classification success rate obtained in this study is 80.77%.

关键词： Feature extraction image color analysis Cancer Pattern classification Bones Software Histograms

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Coral reef mosaicking using Teardrop and Fast image Labeling

Coral reef mosaicking using Teardrop and Fast Image Labeling

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OCEANS - Europe

作者： Francis James Corpuz Prospero Naval Eusebio Capili Jaylord Jauod Roel John Judilla Maricor Soriano Vision and Image Processing Group Instrumentation Physics Laboratory National Institute of Physics Computer Vision and Machine Intelligence Group Department of Computer Science College of Engineering University of Philippines Quezon Philippines Computer Vision and Machine Intelligence Group Department of Computer Science College of Engineering University of Philippines Quezon Philippines Mapua Robotics Center Special Projects Laboratory Institutional Laboratory Mgmt Office Mapua Institute of Technology Manila Philippines Vision and Image Processing Group Instrumentation Physics Laboratory National Institute of Physics University of Philippines Quezon Philippines

关键词： image segmentation Sea measurements Cameras Labeling Discrete Fourier transforms Sea floor

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

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International Conference on Systems, Signals and image processing, IWSSIP

作者： Gayane Shalunts Yll Haxhimusa Robert Sablatnig Institute of Computer Aided Automation Computer Vision Laboratory University of Technology Vienna Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab University of Technology Vienna Austria

Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate.

关键词： Shape Three dimensional displays Visualization computer vision Histograms Windows

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2D-3D registration of human ankle using X-ray and CT images

2D-3D registration of human ankle using X-ray and CT images

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Cairo International Biomedical Engineering Conference, CIBEC

作者： Ahmed Shalaby Aly A. Farag Adrianne Ross Todd Hockenbury Computer Vision and Image Processing Laboratory ECE Department University of Louisville Louisville KY USA Department of Orthopedic Surgery University of Louisville Louisville KY USA

In this paper, we introduce a new framework for registering pre-operative 3D volumetric data to intra-operative 2D images. We are particularly interested in examining the problem of aligning CT volumes to corresponding X-ray images. Our objective is to apply the 2D-3D registration in the field of orthopedics, specifically on ankle surgery. The long-term goal of this work is to apply this technique to ankle fusion surgery to determine the proper size and orientation of the screws which are used for fusing the bones together. In addition, we try to localize the best bone region to fix these screws. Our framework adopts the shear-warp factorization method to generate synthetic 2D images from the given 3D volume. Also, the alignment score is determined according to Exponential Correlation (EC) as a new similarity measure. We used a variety of methods to evaluate registration quality of our system. It has been tested on 15 clinical CT datasets. Evaluation results indicate the degree of accuracy of our registration framework.

关键词： Computed tomography X-ray imaging Surgery Fasteners Biomedical measurement Biomedical imaging Optimization

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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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OCCLUSAL SURFACE RECONSTRUCTION OF HUMAN TEETH FROM A SINGLE image BASED ON OBJECT AND SENSOR PHYSICAL CHARACTERISTICS

OCCLUSAL SURFACE RECONSTRUCTION OF HUMAN TEETH FROM A SINGLE...

引用

IEEE International Conference on image processing

作者： Aly Abdelrahim Aly Farag Shireen Elhabian Eslam Mostafa Wael Aboelmaaty Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Faculty of Dentistry Mansoura University Egypt Department of Oral Medicine and Periodontology University of Louisville Louisville KY USA

image formation involves understanding sensor characteristics and object reflectance. In dentistry, an accurate 3-D representation of the human jaw may be used for diagnostic and treatment purposes. Photogrammetry can offer a flexible, cost effective solution for accurate 3-D representation of the human teeth, which can be used for diagnostic and treatment purposes. Nonetheless there are several challenges, such as the non-friendly image acquisition environment inside the human mouth, problems with lighting and errors due to the data acquisition sensors. In this paper, we focus on the 3D surface reconstruction aspect for human teeth based on a single image. We introduce a more realistic formulation of the shape-from-shading (SFS) problem by considering the image formation components;the camera, the light source, and the surface reflectance. We propose a non-Lambertian SFS algorithm under perspective projection which benefits from camera calibration parameters. We take into account the attenuation of illumination due to near-field imaging. The surface reflectance is modeled using Oren-Nayar-Wolff model which accounts for the retro-reflection case. Our experiments provide promising quantitative metric results for the proposed approach.

关键词： Teeth Surface reconstruction Rough surfaces Surface roughness Cameras Humans image reconstruction

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Iterative data adaptive anisotropic image filtering

Iterative data adaptive anisotropic image filtering

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2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Yang, Chang-Cai Zheng, Xin-Yi Tian, Jin-Wen Shang, Ke Tian, Xin Hao, Wei Institute for Pattern Recognition and Artificial Intelligence Science and Technology on Multi-Spectral Information Processing Laboratory Huazhong University of Science and Technology Wuhan 430074 China Electrical Engineering and Renewable Energy School China Three Gorges University Yichang 443002 China Institute of Intelligent Vision and Image Information College of Computer and Information Technology China Three Gorges University Yichang 443002 China

ISBN: (纸本)9781467314855

This paper presents a novel data-adaptive anisotropic filtering technique built on top of an iterative scheme. This new technique can preserve the original significant structures while suppressing noises to the largest extent. To achieve this goal, dominant orientation information of all gradients is used to control the local kernel adaptively. This results in elongated and elliptical contours spread along the directions of the local edge structure. With these locally adapted kernels, the smoothing is mostly effective along the edges, rather than across them. Therefore details in the original input can be preserved. The performance of the proposed data-adaptive anisotropic filter together with its iterative scheme are compared with the conventional Gaussian filter, anisotropic diffusion filter, the structure-adaptive anisotropic filter, and the iterative kernel regression scheme. The experimental results on both simulated Gaussian and Poission noisy images indicate that the proposed method performs the best, and the iterative scheme can significantly improve the performance of the noise suppressing while delivering high computation efficiency. © 2012 IEEE.

关键词： Iterative methods

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Algorithm for Detection of Single Isolated Human Insulin Crystals for In-Situ Microscopy

Algorithm for Detection of Single Isolated Human Insulin Cry...

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Electronics, Robotics and Automotive Mechanics Conference

作者： Geovanni Martinez Patrick Lindner Arne Bluma Thomas Scheper Image Processing and Computer Vision Laboratory Escuela de Ingenieria Electrica Universidad de Costa Rica San Jose Costa Rica Institut fuer Technische Chemie Leibniz Universitaet Hannover Hannover Germany

An algorithm is presented that is able to detect the regions which correspond to the single isolated human insulin crystals in a group of previously segmented foreground regions. It is based on a single nearest prototype rule, which requires the knowledge of a class prototype for each class of segmented foreground regions. Each class prototype represents a 7-dimensional mean vector of rotation, translation and scale invariant shape characteristics of several class members extracted a priori from a training set of images. An arbitrary segmented foreground region is detected as the region of an isolated human insulin crystal if the region's vector of rotation, translation and scale invariant shape characteristics is much closer to the class prototype of the regions of single isolated human insulin crystals than to the other foreground region class prototypes. The Euclidian distance is used to compute the closeness between two vectors. Experimental results with real data revealed an average processing time of 0.15 seconds/image and a detection reliability of 95 percent.

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