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检索条件"机构=Vision and Image Processing Lab Systems Design Engineering"

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Multi-scale saliency-guided compressive sensing approach to efficient robotic laser range measurements

Multi-scale saliency-guided compressive sensing approach to ...

引用

2012 9th Conference on Computer and Robot vision, CRV 2012

作者： Schwartz, Shimon Wong, Alexander Clausi, David A. Department of Systems Design Engineering University of Waterloo Vision and Image Processing Lab. Research Group Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9780769546834

Improving laser range data acquisition speed is important for many robotic applications such as mapping and localization. One approach to reducing acquisition time is to acquire laser range data through a dynamically small subset of measurement locations. The reconstruction can then be performed based on the concept of compressed sensing (CS), where a sparse signal representation allows for signal reconstruction at sub-Nyquist measurements. Motivated by this, a novel multi-scale saliency-guided CS-based algorithm is proposed for an efficient robotic laser range data acquisition for robotic vision. The proposed system samples the objects of interest through an optimized probability density function derived based on multi-scale saliency rather than the uniform random distribution used in traditional CS systems. Experimental results with laser range data from indoor and outdoor environments show that the proposed approach requires less than half the samples needed by existing CS-based approaches while maintaining the same reconstruction performance. In addition, the proposed method offers significant improvement in reconstruction SNR compared to current CS-based approaches. © 2012 IEEE.

关键词： Compressed sensing

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Illumination correction in dermatological photographs using multi-stage illumination modeling for skin lesion analysis

Illumination correction in dermatological photographs using ...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jeffrey Glaister Alexander Wong David A. Clausi Vision and Image Processing Laboratory with the Department of Systems Design Engineering University of Waterloo ONT Canada

A novel algorithm for correcting illumination variation in dermatological photographs via a multi-stage modeling of the underlying illumination is proposed for the purpose of skin lesion analysis. First, an initial illumination estimate is obtained via a non-parametric modeling strategy based on Monte Carlo sampling. Next, a subset of pixels from the non-parametric estimate is used to determine a parametric estimate of the illumination based on a quadratic surface model. Using the parametric illumination estimate, the reflectance map is obtained and used to correct the photograph. The photographs corrected using the proposed algorithm are compared to uncorrected photographs and to a state-of-the-art correction algorithm. Qualitatively, a visual comparison is performed, while quantitatively, the coefficient of variation of skin pixel intensities is calculated and the precision-recall curve for segmentation of skin lesions is graphed. Results show that the proposed algorithm has a lower coefficient of variation and an improved precision-recall curve.

关键词： Lighting Skin Lesions Algorithm design and analysis Monte Carlo methods Malignant tumors image segmentation

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Perceptual Structure Distortion Ratio: An image Quality Metric Based on Robust Measures of Complex Phase Order

Perceptual Structure Distortion Ratio: An Image Quality Metr...

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Canadian Conference on Computer and Robot vision, CRV

作者： Alexander Wong Vision and Image Processing (VIP) Research Group Department of Systems Design Engineering University of Waterloo Waterloo Canada

This paper investigates a novel perceptual driven approach to image quality assessment using complex wavelets called PSDR (Perceptual Structure Distortion Ratio). The measure is grounded in the modeling of the human vision system as a frequency-based processing system and that perceptual structural significance can be derived based on the concept of complex phase order. Built upon a robust complex phase order framework for measuring structural significance, preliminary results using test images under different types of distortions show that PSDR can be a promising direction for evaluating the quality of visual media.

关键词： image quality PSNR Phase measurement Humans Wavelet transforms Transform coding

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Extracting High-Level Intuitive Features (HLIF) for Classifying Skin Lesions Using Standard Camera images

Extracting High-Level Intuitive Features (HLIF) for Classify...

引用

Canadian Conference on Computer and Robot vision, CRV

作者： Robert Amelard Alexander Wong David A. Clausi Vision and Image Processing (VIP) Research Group Department of Systems Design Engineering University of Waterloo Waterloo Canada

High-level intuitive features (HLIF) that measure asymmetry of skin lesion images obtained using standard cameras are presented. These features can be used to help dermatologists objectively diagnose lesions as cancerous (melanoma) or benign with intuitive rationale. Existing work defines large sets of low-level statistical features for analysing skin lesions. The proposed HLIFs are designed such that smaller sets of HLIFs can capture more deterministic information than large sets of low-level features. Analytical reasoning is given for each feature to show how it aptly describes asymmetry. Promising experimental results show that classification using the proposed HLIF set, although only one-tenth the size of the existing state-of-the-art low-level feature set, labels the data with better or comparable success. The best classification is obtained by combining the low-level feature set with the HLIF set.

关键词： Lesions Malignant tumors Feature extraction Shape Histograms image color analysis Skin

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Multi-Scale Saliency-Guided Compressive Sensing Approach to Efficient Robotic Laser Range Measurements

Multi-Scale Saliency-Guided Compressive Sensing Approach to ...

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Canadian Conference on Computer and Robot vision, CRV

作者： Shimon Schwartz Alexander Wong David A. Clausi Department of Systems Design Engineering Vision and Image Processing Laboratory research group University of Waterloo Waterloo ONT Canada

关键词： Measurement by laser beam image reconstruction image coding Robot sensing systems Noise measurement Signal to noise ratio

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Saliency-guided compressive fluorescence microscopy

Saliency-guided compressive fluorescence microscopy

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Shimon Schwartz Alexander Wong David A. Clausi Department of Systems Design Engineering University of Waterloo Vision and Image Processing Lab research group Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424441198

A novel saliency-guided approach is proposed for improving the acquisition speed of compressive fluorescence microscopy systems. By adaptively optimizing the sampling probability density based on regions of interest instead of the traditional unguided random sampling approach, the proposed saliency-guided compressive fluorescence microscopy approach can achieve high-quality microscopy images using less than half of the number of fluorescence microscopy data measurements required by existing compressive fluorescence microscopy systems to achieve the same level of quality.

关键词： Saliency-guided microscopy high-quality

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Large Pose Invariant Face Recognition Using Feature-based Recurrent Neural Network

Large Pose Invariant Face Recognition Using Feature-based Re...

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International Joint Conference on Neural Networks

作者： Teddy Salan Khan M. Iftekharuddin Intelligent Systems and Image Processing Lab Department of Electrical and Computer Engineering University of Memphis Vision Lab Department of Electrical and Computer Engineering Old Dominion University

ISBN: (纸本)9781467314886

Cellular Simultaneous Recurrent Network (CSRN) is a novel bio-inspired recurrent neural network that mimics reinforcement learning in the brain. CSRN has been proven to be a powerful tool for learning and predicting temporal information in face image sequences. In this work, we propose a novel implementation of feature-based CSRN for large-scale pose invariant face recognition. We also report systematic evaluation and performance comparison of our feature-based CSRN method with other well-known standard algorithms (PCA, LDA, Bayesian Classifier and EBGM) using face recognition technology standards for large-scale pose invariant face recognition.

关键词： Large pose invariant face recognition Cellular Simultaneous Neural Network Feature extraction Motion unit Face Recognition Vendor Test Face Recognition Technology (FERET) program

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Autocalibration: Finding infinity in a projective reconstruction

Autocalibration: Finding infinity in a projective reconstruc...

引用

Canadian Conference on Computer and Robot vision

作者： Cavan, Neil Fieguth, Paul Clausi, David A. Vision and Image Processing Lab. Department of Systems Design Engineering University of Waterloo Waterloo ON Canada

ISBN: (纸本)9780769543628

In order to extract accurate 3D models from uncalibrated image data it is necessary to upgrade the generated projective reconstructions to a metric space, a process known as auto calibration. The key challenge associated with auto calibration is the nonlinear optimization of a cost function based on extracting camera intrinsics from a potential upgrading transform, and evaluating fitness with respect to prior knowledge of physical cameras. The nonlinearity of the problem leads, in general, to poor convergence and a failure of the calibration process. This paper presents a novel auto calibration pipeline that seeks to develop a more robust approach to the nonlinear optimization. After testing a variety of methods, none of which yielded satisfactory solutions, we have developed a strategy combining the best aspects of two methods representing the current state of the art. The former method preconditions the projective space by ensuring it is quasi-affine with respect to camera centers, allowing a naive initialization in the new space, and uses a fitness measure resistant to focal length collapse. The latter method initializes using the best results of an exhaustive search over reasonable values of focal length. Our novel approach, presented here, uses the exhaustive search initialization of the latter combined with the improved fitness measure of the former, producing results that outperform both of its predecessors. © 2011 IEEE.

关键词： Cameras

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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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Probabilistic shape-based segmentation using level sets

Probabilistic shape-based segmentation using level sets

引用

2011 IEEE International Conference on Computer vision Workshops, ICCV Workshops 2011

作者： Aslan, Melih S. Abdelmunim, Hossam Farag, Aly A. Computer Vision and Image Processing Lab University of Louisville Louisville KY 40299 United States Computer and Systems Engineering Department Ain Shams University Cairo Egypt

ISBN: (纸本)9781467300629

In this paper, we present a new dynamic and probabilistic shape based segmentation method using statistical and variational approaches. We use two models in this paper: i) intensity and ii) shape. In the first phase, the intensity based segmentation is done using a basic statistical level set method. In the second phase, to which we contribute, the shape model is constructed using the implicit representation of the training shapes. The resulting probability density function is used to embed the shape model into the image domain with a new energy minimization solution. Our method' s invariance to parameter initialization is evaluated through validation, and various synthetic and clinical shape registration examples are implemented. Experiments show that our proposed algorithm enhances the conventional global registration results, overcomes segmentation challenges, and is robust under various noise levels, severe occlusions, and missing parts. © 2011 IEEE.

关键词： Probability density function

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