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

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Improving Shape from Focus Using Defocus Information

Improving Shape from Focus Using Defocus Information

引用

International Conference on Pattern Recognition

作者： K.S. Pradeep A.N. Rajagopalan Image Processing and Computer Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Madras India

ISBN: (纸本)0769525210

Shape from focus (SFF) method determines the degree of focus in a sequence of observations to estimate the shape of a 3-D object. Existing SFF algorithms use an ad hoc interpolation strategy to account for the error due to the finite step-size by which the translational table is moved while capturing the images. We propose an improved SFF method that uses relative defocus blur derived from actual image data to arrive at the final estimates of the shape of the object. A space-variant image restoration scheme is also proposed to obtain a focused image of the 3-D object. The shape estimates as well as the quality of the restored image using the proposed method are superior to that of traditional SFF

关键词： Shape Focusing Cameras Lenses image restoration Interpolation Pixel Polynomials image sensors image processing

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AN EFFICIENT REPRESENTATION OF DIGITAL CURVES WITH LINE SEGMENTS AND ELLIPTICAL ARCS

AN EFFICIENT REPRESENTATION OF DIGITAL CURVES WITH LINE SEGM...

引用

2006 IET International Conference on Visual Information engineering

作者： C. Paramanand A.N. Rajagopalan Department of Electrical Engineering Image Processing and Computer Vision Laboratory Indian Institute of Technology Chennai India

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ROBUST SPACE-VARIANT SUPER-RESOLUTION

ROBUST SPACE-VARIANT SUPER-RESOLUTION

引用

2006 IET International Conference on Visual Information engineering

作者： K.V. Suresh A.N. Rajagopalan Image Processing and Computer Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

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Autonomous learning of object appearances using colour contour frames

Autonomous learning of object appearances using colour conto...

引用

3rd Canadian Conference on computer and Robot vision, CRV 2006

作者： Forssén, Per-Erik Moe, Anders Laboratory for Computational Intelligence Department of Computer Science 2366 Main Mall BC V6T 1Z4 Canada Computer Vision Laboratory Department of Electrical Engineering Linköping University SE-581 83 Linköping Sweden

ISBN: (纸本)0769525423

In this paper we make use of the idea that a robot can autonomously discover objects and learn their appearances by poking and prodding at interesting parts of a scene. In order to make the resultant object recognition ability more robust, and discriminative, we replace earlier used colour histogram features with an invariant texture-patch method. The texture patches are extracted in a similarity invariant frame which is constructed from short colour contour segments. We demonstrate the robustness of our invariant frames with a repeatability test under general homography transformations of a planar scene. Through the repeatability test, we find that defining the frame using using ellipse segments instead of lines where this is appropriate improves repeatability. We also apply the developed features to autonomous learning of object appearances, and show how the learned objects can be recognised under out-of-plane rotation and scale changes. © 2006 IEEE.

关键词： Object recognition

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Fast Separation of Reflection Components using a Specularity-Invariant image Representation

Fast Separation of Reflection Components using a Specularity...

引用

IEEE International Conference on image Processing

作者： Kuk-jin Yoon Yoojin Choi In So Kweon Department Electrical Engineering and Computer Science KAIST Robotics and Computer Vision Laboratory South Korea Dept. Electrical Engineering and Computer Science Robotics and Computer Vision Lab. Korea

In this paper, we propose a fast method for separating reflection components using a single color image. We first propose a specular-free two-band image that is a specularity-invariant color image representation. Reflection components separation is achieved by comparing local ratios at each pixel and making those ratios equal in an iterative framework. The proposed method is very fast and shows reasonable results for textured indoor/outdoor images.

关键词： Reflection image representation Layout Color Polarization computer vision image segmentation Filters Lighting Argon

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Stereo Matching with Symmetric Cost Functions

Stereo Matching with Symmetric Cost Functions

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： Kuk-Jin Yoon In So Kweon Robotics and Computer Vision Laboratory Department Electrical Engineering and Computer Science KAIST Daejeon South Korea

Recently, many global stereo methods have achieved good results by modeling a disparity surface as a Markov random field (MRF) and by solving an optimization problem with various techniques. However, most global methods mainly focus on how to minimize conventional cost functions efficiently, although it is more important to define cost functions well to improve performance. In this paper, we propose new symmetric cost functions for global stereo methods. We first present a symmetric data cost function for the likelihood and then propose a symmetric discontinuity cost function for the prior in the MRF model for stereo. In defining cost function, both the reference image and the target image are taken into account to improve performance without modeling half-occluded pixels explicitly and without using color segmentation. The performance improvement of stereo matching due to the proposed symmetric cost functions is verified by applying the proposed symmetric cost functions to the belief propagation (BP) based stereo method. Experimental results for standard testbed images show that the performance of the BP based stereo method is greatly improved by the proposed symmetric cost functions.

关键词： Cost function Stereo vision Pixel computer vision Markov random fields Optimization methods image segmentation Belief propagation Robot vision systems computer science

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Visual Modules for Head Gesture analysis in Intelligent Vehicle Systems

Visual Modules for Head Gesture Analysis in Intelligent Vehi...

引用

IEEE Symposium on Intelligent Vehicle

作者： Junwen Wu M.M. Trivedi Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego CA USA

In this paper a coarse-to-fine system framework for analyzing the head gesture is presented. We discuss several important modules from computer vision aspects, including the pose-invariant face detection, face tracking, pose determination and high-resolution image reconstruction for eye pupils detection. Visual cues using intensity images obtained from in-car cameras are explored. A pose-invariant face detection algorithm is used to get the initial face area; afterwards face tracking and validation step is proposed to segment the face region for pose determination. The algorithm is tested on the drivers images under natural driving conditions. Experimental results show that the algorithm is robust to the head pose changes as well as the illumination changes. In this system framework, we propose that when coarse analysis utilizing the head pose alone is not sufficient for driver's behavior analysis, a finer analysis based on the eye gaze tracking is used, which requires images with sufficient resolution. A novel super-resolution reconstruction algorithm is proposed to help reveal more facial details, so as to facilitate the pupil detection. Experiment on the synthesis data shows the effectiveness of the super-resolution reconstruction algorithm

关键词： Intelligent vehicles Face detection Head image analysis Reconstruction algorithms computer vision image reconstruction Cameras image segmentation Testing

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A Regression Model in TensorPCA Subspace for Face image Super-resolution Reconstruction

A Regression Model in TensorPCA Subspace for Face Image Supe...

引用

International Conference on Pattern Recognition

作者： Junwen Wu M.M. Trivedi Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego CA USA

ISBN: (纸本)0769525210

A regression model in the tensorPCA subspace is proposed in this paper for face super-resolution reconstruction. An approximate conditional probability model is used for the tensor subspace coefficients and maximum-likelihood estimator gives a linear regression model. The approximation is corrected by adding non-linear component from a RBF-type regressor. Experiments on face images from FERET database validate the algorithm. Although each projection coefficient is estimated by a local estimator, tensorPCA subspace analysis is still a global descriptor, which makes the algorithm have certain ability to deal with partially occluded images

关键词： image resolution image reconstruction Tensile stress Spatial resolution Principal component analysis Face detection image analysis computer vision Robot vision systems Laboratories

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Autonomous Learning of Object Appearances using Colour Contour Frames

Autonomous Learning of Object Appearances using Colour Conto...

引用

Canadian Conference on computer and Robot vision, CRV

作者： P. Forssen A. Moe Department of Computer Science Laboratory for Computational Intelligence BC Canada Computer Vision Laboratory Department of Electrical Engineering Linköping University Linkoping Sweden

关键词： image segmentation computer vision Layout Testing Solid modeling Laboratories Robot vision systems Object recognition Robustness Histograms

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High SNR flexible top hat monopole probe for 1.5 T MRI

High SNR flexible top hat monopole probe for 1.5 T MRI

引用

作者： Mohammadzadeh, M. Shahabadi, M. Soltanian-Zadeh, H. Tavakoli, A. Rafiei, B. Javidnia, D. Oghabian, M.A. Department of Physics Amir-Kabir University of Technology Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran 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 Health System Detroit MI United States Department of Electrical Engineering Amir-Kabir University of Technology Tehran Iran Medical Imaging Center Imam Khomeyni Hospital Tehran Iran Research Center for Science and Technology in Medicine Tehran University of Medical Sciences Tehran Iran

In this paper, design principles and application of a thin and flexible intravascular top hat monopole probe with increased signal-to-noise ratio (SNR) and improved longitudinal and radial coverage are described and compared with the conventional monopole probe. The top hat probe is constructed by removing a segment of the shield of a coaxial cable. A flexible thin copper foil in contact with the inner conductor of the cable is added to the end section of the cable. The copper foil acts as an antenna top hat load increasing the uniformity of the current distribution on the probe and establishes a uniform region of SNR distribution around the probe. Improvement of the SNR distribution of the top hat probe relative to the conventional monopole probe is evaluated using quantitative indices. Experimental results show that for the top hat probe, the ratio of the maximum image SNR to its relative standard deviation (SR) is 2.73 times higher than that of a conventional probe. © 2006 IOP Publishing Ltd.

关键词： Medical imaging

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