Structured Light Based Depth Edge Detection for Object Shape Recovery

Kim, Cheolhwon; Park, Jiyoung; Yi, Juneho; Turk, Matthew

doi:10.1109/cvpr.2005.536

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops

DOI: 10.1109/cvpr.2005.536

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Structured Light Based Depth Edge Detection for Object Shape Recovery

Cheolhwon Kim

Jiyoung Park

Juneho Yi

et al.

Abstract: This research features a novel approach that efficiently detects depth edges in real world scenes. Depth edges play a very important role in many computer vision problems because they represent object contours. We strategically project structured light and exploit distortion of light pattern in the structured light image along depth discontinuities to reliably detect depth edges. Distortion along depth discontinuities may not occur or be large enough to detect depending on the distance from the camera or proje… Show more

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Cited by 8 publications

References 14 publications

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A 2D unknown contour recovery method immune to system non-linear effects

Juan¹,

Tur²

2006

2006 IEEE International Conference on Robotics and Biomimetics

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Abstract-A method to recover general 2D a priori unknown contours using a kind of special optic sensor is described. Contour recovery is an important task for exploratory operations in unknown environments as well as for more practical applications such as grinding or deburring. It is not an easy task since the recovered contour (generally obtained using encoder data) is severely distorted due to errors in the kinematic model of the robot and to the non-linearities of its actuators. Some mathematical models have been presented to partially compensate for those effects, but they require a deep knowledge of both the robot and sensor models which are difficult to obtain accurately, and normally imply an adaptive non-linear control to estimate some of the unknown parameters of the model.Our approach, in despite of its simplicity, is intrinsically immune to non-linearities, which allows us to eliminate most of the distortions added to the sensor data. A simple algorithm to follow unknown planar contours is presented and used to test the performance of this approach in comparison to the one using encoder data. Experimental results and practical problems are also discussed.

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A 2D unknown contour recovery method immune to system non-linear effects

Juan¹,

Tur²

2006

2006 IEEE International Conference on Robotics and Biomimetics

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Detecting 3D geometric boundaries of indoor scenes under varying lighting

Marks

Tuzel

et al. 2014

IEEE Winter Conference on Applications of Computer Vision

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The goal of this research is to identify 3D geometric boundaries in a set of 2D photographs of a static indoor scene under unknown, changing lighting conditions. A 3D geometric boundary is a contour located at a 3D depth discontinuity or a discontinuity in the surface normal. These boundaries can be used effectively for reasoning about the 3D layout of a scene. To distinguish 3D geometric boundaries from 2D texture edges, we analyze the illumination subspace of local appearance at each image location. In indoor time-lapse photography and surveillance video, we frequently see images that are lit by unknown combinations of uncalibrated light sources. We introduce an algorithm for semi-binary non-negative matrix factorization (SBNMF) to decompose such images into a set of lighting basis images, each of which shows the scene lit by a single light source. These basis images provide a natural, succinct representation of the scene, enabling tasks such as scene editing (e.g., relighting) and shadow edge identification WACV 2014: IEEE Winter Conference on Applications of Computer VisionThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. AbstractThe goal of this research is to identify 3D geometric boundaries in a set of 2D photographs of a static indoor scene under unknown, changing lighting conditions. A 3D geometric boundary is a contour located at a 3D depth discontinuity or a discontinuity in the surface normal. These boundaries can be used effectively for reasoning about the 3D layout of a scene. To distinguish 3D geometric boundaries from 2D texture edges, we analyze the illumination subspace of local appearance at each image location. In indoor time-lapse photography and surveillance video, we frequently see images that are lit by unknown combinations of uncalibrated light sources. We introduce an algorithm for semi-binary nonnegative matrix factorization (SBNMF) to decompose such images into a set of lighting basis images, each of which shows the scene lit by a single light source. These basis images provide a natural, succinct representation of the scene, enabling tasks such as scene editing (e.g., relighting) and shadow edge identification.

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A method for edge detection of textile preforms using a light-section sensor for the automated manufacturing of fibre-reinforced plastics

2007

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The present work describes a new method to measure the contour position of plane reinforcement fabrics for the manufacturing of structural composite parts. The pursued approach uses optical metrology based on laser light-section technology. In detail, a laser line is projected over the edge of a fabric layer and acquired with a digital camera, which is located under an offset angle to the laser sensor. This leads to a distinctive displacement of the laser line in the acquired image, which is proportional to the distance between the sensor and the fabric layer. The distorted line can be described as a step profile, to which an analytical function is fitted to calculate the horizontal edge position with sub-pixel accuracy. To measure the whole layer position, the edges are scanned with the laser sensor to provide multiple contour points. This allows the interpolation of the object contour. The interpolated contour can be compared with the specified position and dimension of the textile layer. This enables a closed-loop control of the cutting and build-up process of the preform. Thus, an efficient production process of fibre-reinforced plastics through an automated inline measurement is possible.

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Structured Light Based Depth Edge Detection for Object Shape Recovery

Cited by 8 publications

References 14 publications

A 2D unknown contour recovery method immune to system non-linear effects

A 2D unknown contour recovery method immune to system non-linear effects

Detecting 3D geometric boundaries of indoor scenes under varying lighting

A method for edge detection of textile preforms using a light-section sensor for the automated manufacturing of fibre-reinforced plastics

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