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检索条件"任意字段=Canadian Conference on Computer and Robot Vision, CRV"

共 936 条记录，以下是61-70 订阅

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vision-Based Qualitative Path-Following Control of Quadrotor Aerial Vehicle with Speeded-Up Robust Features

Vision-Based Qualitative Path-Following Control of Quadrotor...

引用

11th canadian conference on computer and robot vision (crv)

作者： Trung Nguyen Mann, George K. I. Gosine, Raymond G. Mem Univ Newfoundland Fac Engn & Appl Sci IS Lab St John NF A1B 3X5 Canada

ISBN: (纸本)9781479943388

This paper describes a vision-based 3D navigation technique for path-following control of Quadrotor Aerial Visual-Teach-and-Repeat system. The navigation method is developed on Funnel Lane theory, which defines possible positions to fly straight. The navigation calculation utilizes the reference images and features to compute the desired heading angle and height during path following. The type of feature is Speeded-Up Robust Features (SURF). The tracking feature method between images is performed by matching SURF feature's descriptors. The Quadrotor is able to independently perform path following in indoor environment without the support of an external tracking system. Simulation is conducted on robot Operating System (ROS) and Gazebo simulator. The application of the proposed method is visual-homing and visual-servoing in GPS-denied environment.

关键词： quadrotor aerial vehicle path following visual servoing speeded-up robust features visual teach and repeat

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Generic detection of multi-part objects by high-level analysis

Generic detection of multi-part objects by high-level analys...

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3rd canadian conference on computer and robot vision, crv 2006

作者： Bernier, Jean-François Bergevin, Robert Laval University Computer Vision and Systems Laboratory Que. Canada

ISBN: (纸本)0769525423

A method is proposed to detect multi-part man-made or natural objects in complex images. It consists in first extracting simple curves and straight lines from the edge map. Then, a search tree is expanded by selecting and ordering the segmented primitives on the basis of generic local and global grouping criteria. The set of partial contours provided by the parallel search are combined into more complex forms. Global scores produce a sorted list of potential object silhouettes. © 2006 IEEE.

关键词： Object recognition

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Registration uncertainty for robot self-localization in 3D 2

Registration uncertainty for robot self-localization in 3D

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2nd canadian conference on computer and robot vision, crv 2005

作者： Zhang, Pifu Gu, J. Milios, E.E. Faculty of Computer Science Dalhousie University Canada Dep. of Electrical and Computer Engi. Dalhousie University Canada

ISBN: (纸本)0769523196

Stereo camera is a very important sensor for mobile robot localization and mapping. Its consecutive images can be used to estimate the location of the robot with respect to its environment. This estimation will be fused with location estimates from other sensors for a globally optimal location estimate. In the data fusion context, it is important to compute the uncertainty of the stereo-based localization. In this paper, we propose an approach to obtain the uncertainty of localization when a correspondence-based method is used to estimate the robot pose. The computational complexity of this approach is O(n). Where n is the number of corresponding image points. Experimental results show that this approach is promising. © 2005 IEEE.

关键词： Data fusion

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OpenVL: Abstracting vision Tasks Using a Segment-Based Language Model

OpenVL: Abstracting Vision Tasks Using a Segment-Based Langu...

引用

10th International conference on computer and robot vision (crv)

作者： Miller, Gregor Oldridge, Steve Fels, Sidney Univ British Columbia Human Commun Technol Lab Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)9780769549835

computer vision is a complex field which can be challenging for those outside the community to apply in the real world. In this paper we show one method to provide access to sophisticated computer vision methods to general developers, hobbyists or researchers outside the field. Our contribution is an abstraction utilising fundamental vision operations based on a single unit, the segment, can be used to describe images, local image conditions and between-image conditions. We illustrate how a descriptive language model can be built on the segment to provide an intuitive mental model of computer vision to mainstream developers. We demonstrate how we can map a description of the task composed of the segment-based language into the space of algorithms, to choose an appropriate method to solve the problem. We use the problems of segmentation, correspondence and image registration to show how end-to-end problems may be constructed using our novel metaphor.

关键词： vision abstraction applications software tools

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Unbiased Sparse Subspace Clustering By Selective Pursuit 14

Unbiased Sparse Subspace Clustering By Selective Pursuit

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14th conference on computer and robot vision (crv)

作者： Ackermann, Hanno Rosenhahn, Bodo Yang, Michael Ying Leibniz Univ Hannover Inst Informat Proc Hannover Germany Univ Twente Scene Understanding Grp Enschede Netherlands

ISBN: (纸本)9781538628188

Sparse subspace clustering (SSC) is an elegant approach for unsupervised segmentation if the data points of each cluster are located in linear subspaces. This model applies, for instance, in motion segmentation if some restrictions on the camera model hold. SSC requires that problems based on the l(1)-norm are solved to infer which points belong to the same subspace. If these unknown subspaces are well-separated this algorithm is guaranteed to succeed. The question how the distribution of points on the same subspace effects their clustering has received less attention. One case has been reported in which points of the same model are erroneously classified to belong to different subspaces. In this work, it will be theoretically shown when and why such spurious clusters occur. This claim is further substantiated by experimental evidence. Two algorithms based on the Dantzig selector and subspace selector are proposed to overcome this problem, and good results are reported.

关键词： Sparse matrices Clustering algorithms Motion segmentation computer vision Data models Algorithm design and analysis Distributed databases

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Towards an Improved vision-based Web Page Segmentation Algorithm 14

Towards an Improved Vision-based Web Page Segmentation Algor...

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14th conference on computer and robot vision (crv)

作者： Cormier, Michael Mann, Richard Moffatt, Karyn Cohen, Robin Univ Waterloo Cheriton Sch Comp Sci Waterloo ON Canada McGill Univ Informat Studies Montreal PQ Canada

ISBN: (纸本)9781538628188

In this paper we introduce an edge-based segmentation algorithm designed for web pages. We consider each web page as an image and perform segmentation as the initial stage of a planned parsing system that will also include region classification. The motivation for our work is to enable improved online experiences for users with assistive needs (serving as the back-end process for such front-end tasks as zooming and decluttering the image being presented to those with visual or cognitive challenges, or producing less unwieldy output from screenreaders). Our focus is therefore on the interpretation of a class of man-made images (where web pages consist of one particular set of these images which have important constraints that assist in performing the processing). After clarifying some comparisons with an earlier model of ours, we show validation for our method. Following this, we briefly discuss the contribution for the field of computer vision, offering a contrast with current work in segmentation focused on the processing of natural images.

关键词： segmentation man-made images document analysis assistive technology

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Existence Detection of Objects in Images for robot vision Using Saliency Histogram Features

Existence Detection of Objects in Images for Robot Vision Us...

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10th International conference on computer and robot vision (crv)

作者： Scharfenberger, Christian Waslander, Steven L. Zelek, John S. Clausi, David A. Univ Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9780769549835

In robotics and computer vision, saliency maps are frequently used to identify regions that contain potential objects of interest and to restrict object detection to those regions only. However, common saliency approaches do not provide information as to whether there really is an interesting object triggering saliency and therefore tend to highlight needless background as potential regions of interest. This paper addresses the problem by exploiting histogram features extracted from saliency maps to predict the existence of interesting objects in images and to quickly prune uninteresting images. To validate our approach, we constructed a database that consists of 1000 background and object images captured in the working environment of our robot. Experimental results demonstrate that our approach achieves good detection performance and outperforms an existing existence detection approach [1].

关键词： Existence detection histogram features saliency maps robotics

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Automated Door Detection with a 3D-Sensor

Automated Door Detection with a 3D-Sensor

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11th canadian conference on computer and robot vision (crv)

作者： Borgsen, Sebastian Meyer zu Schoepfer, Matthias Ziegler, Leon Wachsmuth, Sven Univ Bielefeld CITEC Ctr Excellence Cognit Interact Technol D-33615 Bielefeld Germany

ISBN: (纸本)9781479943388

Service robots share the living space of humans. Thus, they should have a similar concept of the environment without having everything labeled beforehand. The detection of closed doors is challenging because they appear with different materials, designs and can even include glass inlays. At the same time their detection is vital in any kind of navigation tasks in domestic environments. A typical 2D object recognition algorithm may not be able to handle the large optical variety of doors. Improvements of low-cost infrared 3D-sensors enable robots to perceive their environment as spatial structure. Therefore we propose a novel door detection algorithm that employs basic structural knowledge about doors and enables to extract parts of doors from point clouds based on constraint region growing. These parts get weighted with Gaussian probabilities and are combined to create an overall probability measure. To show the validity of our approach, a realistic dataset of different doors from different angles and distances was acquired.

关键词： Detection algorithms Feature extraction Glass robot sensing systems Three-dimensional displays 3d depth detection door kinect pcl primesense recognition robot vision

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Belief propagation on the GPU for stereo vision

Belief propagation on the GPU for stereo vision

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3rd canadian conference on computer and robot vision, crv 2006

作者： Brunton, Alan Chang, Shu Roth, Gerhard University of Ottawa Ottawa Ont. Institute for Information Technology National Research Council of Canada Ottawa Ont.

ISBN: (纸本)0769525423

The power of Markov random field formulations of low-level vision problems, such as stereo, has been known for some time. However, recent advances, both algorithmic and in processing power, have made their application practical. This paper presents a novel implementation of Bayesian belief propagation for graphics processing units found in most modern desktop and notebook computers, and applies it to the stereo problem. The stereo problem is used for comparison to other BP algorithms. © 2006 IEEE.

关键词： Markov processes

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Convolutional Neural Networks Regularized by Correlated Noise 15

Convolutional Neural Networks Regularized by Correlated Nois...

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15th conference on computer and robot vision (crv)

作者： Dutta, Shamak Tripp, Bryan Taylor, Graham W. Univ Waterloo Syst Design Engn Waterloo ON Canada Univ Waterloo Ctr Theoret Neurosci Waterloo ON Canada Univ Guelph Sch Engn Canadian Inst Adv Res Vector Inst Artificial Intelligence Guelph ON Canada

ISBN: (纸本)9781538664810

Neurons in the visual cortex are correlated in their variability. The presence of correlation impacts cortical processing because noise cannot be averaged out over many neurons. In an effort to understand the functional purpose of correlated variability, we implement and evaluate correlated noise models in deep convolutional neural networks. Inspired by the cortex, correlation is defined as a function of the distance between neurons and their selectivity. We show how to sample from high-dimensional correlated distributions while keeping the procedure differentiable, so that back-propagation can proceed as usual. The impact of correlated variability is evaluated on the classification of occluded and non-occluded images with and without the presence of other regularization techniques, such as dropout. More work is needed to understand the effects of correlations in various conditions, however in 10/12 of the cases we studied, the best performance on occluded images was obtained from a model with correlated noise.

关键词： Correlated Variability Convolutional Neural Networks Regularization Stochastic Neurons

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