Computational Cameras: Convergence of Optics and Processing

Zhou, Changyin; Nayar, S.K.

doi:10.1109/tip.2011.2171700

Cited by 103 publications

(14 citation statements)

References 211 publications

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“…The world coordinate system coincides with the coordinate system of the light field camera, where Oz is the optical axis. We use the plane and tangent direction presentation [35] for the light field image, where a scene ray is described by a four-dimensional function, I I I(x, y, s,t). (x, y) denotes parameter of a sub-aperture that a scene ray passes through, while (x, y, 0) gives 3-D location of the sub-aperture on the Oxy plane, and (s,t) represents the tangent direction of the scene ray.…”

Section: Image Generative Modelmentioning

confidence: 99%

Reflectance and Shape Estimation with a Light Field Camera under Natural Illumination

Ngo

Nagahara

Nishino

et al. 2017

Procedings of the British Machine Vision Conference 2017

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Reflectance and shape are two important components in visually perceiving the real world. Inferring the reflectance and shape of an object through cameras is a fundamental research topic in the field of computer vision. While three-dimensional shape recovery is pervasive with varieties of approaches and practical applications, reflectance recovery has only emerged recently. Reflectance recovery is a challenging task that is usually conducted in controlled environments, such as a laboratory environment with a special apparatus. However, it is desirable that the reflectance be recovered in the field with a handy camera so that reflectance can be jointly recovered with the shape. To that end, we present a solution that simultaneously recovers the reflectance and shape (i.e., dense depth and normal maps) of an object under natural illumination with commercially available handy cameras. We employ a light field camera to capture one light field image of the object, and a 360-degree camera to capture the illumination. The proposed method provides promising results in simulation and real-world experiments.

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Section: Image Generative Modelmentioning

confidence: 99%

Reflectance and Shape Estimation with a Light Field Camera under Natural Illumination

Ngo

Nagahara

Nishino

et al. 2017

Procedings of the British Machine Vision Conference 2017

View full text Add to dashboard Cite

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“…These systems can produce a new type of image that is potentially useful for re-focusing, scene segmentation and 3D computer vision. Computational imaging systems can be broadly classified into six categories, as shown in Figure 1 [ 9 ].…”

Section: Dual Off-axis Color-filtered Aperture-based Cameramentioning

confidence: 99%

“… Six types of computational cameras and the proposed dual off-axis color-filtered aperture (DCA) camera [ 9 ]. …”

Section: Figurementioning

confidence: 99%

“…Another single camera-based depth estimation approach is the pupil plane coding method that modifies an aperture of a lens for coding geometric depth information into the image [ 9 ]. Recently, three apertures covered with red, green and blue filters have been used for the same purpose [ 10 ], where three color-filtered apertures (TCA) generate color shifts among channels depending on the distance of an object.…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Object Motion and Velocity Estimation Using a Single Computational RGB-D Camera

Lee

Jeong

Park

et al. 2015

Sensors

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In this paper, a three-dimensional (3D) object moving direction and velocity estimation method is presented using a dual off-axis color-filtered aperture (DCA)-based computational camera. Conventional object tracking methods provided only two-dimensional (2D) states of an object in the image for the target representation. The proposed method estimates depth information in the object region from a single DCA camera that transforms 2D spatial information into 3D model parameters of the object. We also present a calibration method of the DCA camera to estimate the entire set of camera parameters for a practical implementation. Experimental results show that the proposed DCA-based color and depth (RGB-D) camera can calculate the 3D object moving direction and velocity of a randomly moving object in a single-camera framework.

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“…With the advent of computational cameras, a new approach to achieve high-speed imaging is offered. Computational cameras combine the features of the computer and the camera, it samples the light field in a completely different way to create new and useful forms of visual information [ 17 ]. In recent years, many scientists in this way make some beneficial attempts.…”

Section: Introductionmentioning

confidence: 99%

Per-Pixel Coded Exposure for High-Speed and High-Resolution Imaging Using a Digital Micromirror Device Camera

Feng

Zhang

et al. 2016

Sensors

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High-speed photography is an important tool for studying rapid physical phenomena. However, low-frame-rate CCD (charge coupled device) or CMOS (complementary metal oxide semiconductor) camera cannot effectively capture the rapid phenomena with high-speed and high-resolution. In this paper, we incorporate the hardware restrictions of existing image sensors, design the sampling functions, and implement a hardware prototype with a digital micromirror device (DMD) camera in which spatial and temporal information can be flexibly modulated. Combined with the optical model of DMD camera, we theoretically analyze the per-pixel coded exposure and propose a three-element median quicksort method to increase the temporal resolution of the imaging system. Theoretically, this approach can rapidly increase the temporal resolution several, or even hundreds, of times without increasing bandwidth requirements of the camera. We demonstrate the effectiveness of our method via extensive examples and achieve 100 fps (frames per second) gain in temporal resolution by using a 25 fps camera.

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Computational Cameras: Convergence of Optics and Processing

Cited by 103 publications

References 211 publications

Reflectance and Shape Estimation with a Light Field Camera under Natural Illumination

Reflectance and Shape Estimation with a Light Field Camera under Natural Illumination

Three-Dimensional Object Motion and Velocity Estimation Using a Single Computational RGB-D Camera

Per-Pixel Coded Exposure for High-Speed and High-Resolution Imaging Using a Digital Micromirror Device Camera

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