Beyond Silhouettes: Surface Reconstruction Using Multi-Flash Photography

Crispell, Daniel; Lanman, Douglas; Sibley, Peter G.; Zhao, Yong; Taubin, Gabriel

doi:10.1109/3dpvt.2006.37

Cited by 23 publications

(27 citation statements)

References 19 publications

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“…Unfortunately, they require the use of a screen to project fringes, which cannot be achieved with only a mobile phone. Looking further, Crispell, Lanman, et al show how to reconstruct surfaces using only a camera in [4]. However, their method works by detecting depth discontinuities, which do not exist on an eye.…”

Section: A Three-dimensional Reconstruction Methodsmentioning

confidence: 99%

Detecting topographical eye disorders

Patel¹,

Gouia‐Zarrad

2017

BIOMATH

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Abstract-The goal of this paper is to outline a novel method of detecting topographical eye diseases using a mobile phone. Because the shape of the eye is of concern, its exterior will be evaluated. The description and current implementation of the technique will be discussed, where the keratoconus disease is used as an example. The technique uses pictures taken of the eye using the onboard camera, and each one is compared to idealized images rendered in the phone. The pictures taken will be compared to the idealized images to search for the presence of the disease.

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Section: A Three-dimensional Reconstruction Methodsmentioning

confidence: 99%

Detecting topographical eye disorders

Patel¹,

Gouia‐Zarrad

2017

BIOMATH

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“…detail transfer, white-balancing, continuous flash and red-eye removal) [52,54,22], specular artifacts removal [55], estimation of the ambient illumination [56], depth estimation [57][58][59][60], light field transfer [48], shadow detection [61], and extracting middle-level features (e.g. shape, surface, and reflectance) [14].…”

Section: Computational Samplingmentioning

confidence: 99%

“…(iii) Capture of multi-flash images [57][58][59][60]55] or flash/no-flash image pairs [10,70,56,54,52].…”

Section: Computational Samplingmentioning

confidence: 99%

An overview of computational photography

Suo

Dai

2012

Sci. China Inf. Sci.

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Computational photography is an emerging multidisciplinary field. Over the last two decades, it has integrated studies across computer vision, computer graphics, signal processing, applied optics and related disciplines. Researchers are exploring new ways to break through the limitations of traditional digital imaging for the benefit of photographers, vision and graphics researchers, and image processing programmers. Thanks to much effort in various associated fields, the large variety of issues related to these new methods of photography are described and discussed extensively in this paper. To give the reader the full picture of the voluminous literature related to computational photography, this paper briefly reviews the wide range of topics in this new field, covering a number of different aspects, including: (i) the various elements of computational imaging systems and new sampling and reconstruction mechanisms; (ii) the different image properties which benefit from computational photography, e.g. depth of field, dynamic range; and (iii) the sampling subspaces of visual scenes in the real world. Based on this systematic review of the previous and ongoing work in this field, we also discuss some open issues and potential new directions in computational photography. This paper aims to help the reader get to know this new field, including its history, ultimate goals, hot topics, research methodologies, and future directions, and thus build a foundation for further research and related developments.

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“…These edges can be distinguished by finding white-to-black transitions in the captured image along the epipolar line defined by the light source position [16]. 4 (Left) Depth edge extraction in 3D using a ring light source having a continuous hue circle. At a depth edge, a half of the hue circle is occluded and the hues in the other half are integrated in the shadow region.…”

Section: Principle In 2dmentioning

confidence: 99%

Rainbow Flash Camera: Depth Edge Extraction Using Complementary Colors

Taguchi

2012

Computer Vision – ECCV 2012

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We present a novel color multiplexing method for extracting depth edges in a scene. It has been shown that casting shadows from different light positions provides a simple yet robust cue for extracting depth edges. Instead of flashing a single light source at a time as in conventional methods, our method flashes all light sources simultaneously to reduce the number of captured images. We use a ring light source around a camera and arrange colors on the ring such that the colors form a hue circle. Since complementary colors are arranged at any position and its antipole on the ring, shadow regions where a half of the hue circle is occluded are colorized according to the orientations of depth edges, while non-shadow regions where all the hues are mixed have a neutral color in the captured image. Thus the colored shadows in the single image directly provide depth edges and their orientations in an ideal situation. We present an algorithm that extracts depth edges from a single image by analyzing the colored shadows. We also present a more robust depth edge extraction algorithm using an additional image captured by rotating the hue circle with 180 degrees to compensate for scene textures and ambient lights. We compare our approach with conventional methods for various scenes using a camera prototype consisting of a standard camera and 8 color LEDs. We also demonstrate a bin-picking system using the camera prototype mounted on a robot arm. International Journal 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. Abstract We present a novel color multiplexing method for extracting depth edges in a scene. It has been shown that casting shadows from different light positions provides a simple yet robust cue for extracting depth edges. Instead of flashing a single light source at a time as in conventional methods, our method flashes all light sources simultaneously to reduce the number of captured images. We use a ring light source around a camera and arrange colors on the ring such that the colors form a hue circle. Since complementary colors are arranged at any position and its antipole on the ring, shadow regions where a half of the hue circle is occluded are colorized according to the orientations of depth edges, while non-shadow regions where all the hues are mixed have a neutral color in the captured image. Thus the colored shadows in the single image directly provide dept...

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Beyond Silhouettes: Surface Reconstruction Using Multi-Flash Photography

Cited by 23 publications

References 19 publications

Detecting topographical eye disorders

Detecting topographical eye disorders

An overview of computational photography

Rainbow Flash Camera: Depth Edge Extraction Using Complementary Colors

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