Towards a true spherical camera

Krishnan, Gurunandan; Nayar, Shree K.

doi:10.1117/12.817149

Cited by 40 publications

(18 citation statements)

References 21 publications

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“…Krishnan and Nayar proposed an omnidirectional sensor consisting of a transparent sphere, around which sensing elements are uniformly arranged on a spherical surface, with free space between neighboring elements [290]. Hence, individual sensing elements can see through the transparent sphere and other sensing elements, to perceive the scene.…”

Section: Spherical Lens and Spherical Image Areamentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

153

121

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A printed and bound version is available at a special discount price of US35 from Now Publishers. This can be obtained by entering the promotional code CGV006023 on https://www.nowpublishers.com/bookorder.aspx?doi=0600000023&product=CGVInternational audienceThis survey is mainly motivated by the increased availability and use of panoramic image acquisition devices, in computer vision and various of its applications. Different technologies and different computational models thereof exist and algorithms and theoretical studies for geometric computer vision ("structure-from-motion") are often re-developed without highlighting common underlying principles. One of the goals of this survey is to give an overview of image acquisition methods used in computer vision and especially, of the vast number of camera models that have been proposed and investigated over the years, where we try to point out similarities between different models. Results on epipolar and multi-view geometry for different camera models are reviewed as well as various calibration and self-calibration approaches, with an emphasis on non-perspective cameras. We finally describe what we consider are fundamental building blocks for geometric computer vision or structure-from-motion: epipolar geometry, pose and motion estimation, 3D scene modeling, and bundle adjustment. The main goal here is to highlight the main principles of these, which are independent of specific camera models

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Section: Spherical Lens and Spherical Image Areamentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

153

121

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“…It was shown that the sapphire ball lens has a better imaging quality and higher refractive index [22], which could improve the lateral resolution and decrease the noise. Also by using spherical sensor arrays with a spherical ball lens, uniform spatial resolution [23] and gigapixel resolution can be reached [24].…”

Section: Image Source Target Corresponding Pixel Area For Magnetic Beadsmentioning

confidence: 99%

Image Processing and Cell Phone Microscopy to Analyze the Immunomagnetic Beads on Micro-Contact Printed Gratings

İçöz

2016

Applied Sciences

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Abstract:In this paper we report an ultra-low-cost spherical ball lens based cell phone microscopy and image processing algorithms to analyze the amount of immunomagnetic beads on micro-contact printed gratings. The spherical ball lens provides approximately 100× magnification but the recorded images are not clear and are noisy. By using the image-processing algorithms, the noise can be reduced and the images can be enhanced to quantify the amount of immunomagnetic beads on micro-contact printed lines. This method, which is portable and low-cost, can be an alternative read out mechanism for biosensing applications using immunomagnetic beads on micro-contact printed surface receptors. Further, 0.0335 mg/mL was the lowest magnetic bead concentration that could be detected above the inherent noise level of the spherical ball lens.

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“…al proposed a small diffraction limited camera consisting of a ball lens and curved sensor [16]. Krishnan and Nayar proposed the use of a large ball lens and spherical sensor together with deblurring to create a single viewpoint, fully spherical FOV camera [17]. While several researchers have made progress towards developing curved sensors [18] [19] [20], the technology is not yet ready for commercialization.…”

Section: C Monocentric Optics and Curved Sensorsmentioning

confidence: 99%

Scaling law for computational imaging using spherical optics

2011

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The resolution of a camera system determines the fidelity of visual features in captured images. Higher resolution implies greater fidelity, and thus greater accuracy when performing automated vision tasks such as object detection, recognition, and tracking. However, the resolution of any camera is fundamentally limited by geometric aberrations. In the past, it has generally been accepted that the resolution of lenses with geometric aberrations cannot be increased beyond a certain threshold. We derive an analytic scaling law showing that, for lenses with spherical aberrations, resolution can be increased beyond the aberration limit by applying a post-capture deblurring step. We then show that resolution can be further increased when image priors are introduced.Based on our analysis, we advocate for computational camera designs consisting of a spherical lens shared by several small planar sensors. We show example images captured with a proof-of-concept gigapixel camera, demonstrating that high resolution can be achieved with a compact form factor and low complexity. We conclude with an analysis on the trade-off between performance and complexity for computational imaging systems with spherical lenses.

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Towards a true spherical camera

Cited by 40 publications

References 21 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Image Processing and Cell Phone Microscopy to Analyze the Immunomagnetic Beads on Micro-Contact Printed Gratings

Scaling law for computational imaging using spherical optics

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