Camera Models and Optical Systems Used in Computer Graphics: Part I, Object-Based Techniques

Barsky, Brian A.; Horn, Daniel Reiter; Klein, Stanley A.; Pang, Jeffrey A.; Yu, Meng

doi:10.1007/3-540-44842-x_26

Cited by 31 publications

(21 citation statements)

References 9 publications

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“…Many methods for accelerating or approximating blur due to defocus have been developed [Fearing 1995; Rokita 1996; Barsky et al 2003a; 2003b; Mulder and van Liere 2000], and the problem of rendering such effects remains an active area of research.…”

Section: Introductionmentioning

confidence: 99%

Using blur to affect perceived distance and size

et al. 2010

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We present a probabilistic model of how viewers may use defocus blur in conjunction with other pictorial cues to estimate the absolute distances to objects in a scene. Our model explains how the pattern of blur in an image together with relative depth cues indicates the apparent scale of the image's contents. From the model, we develop a semiautomated algorithm that applies blur to a sharply rendered image and thereby changes the apparent distance and scale of the scene's contents. To examine the correspondence between the model/algorithm and actual viewer experience, we conducted an experiment with human viewers and compared their estimates of absolute distance to the model's predictions. We did this for images with geometrically correct blur due to defocus and for images with commonly used approximations to the correct blur. The agreement between the experimental data and model predictions was excellent. The model predicts that some approximations should work well and that others should not. Human viewers responded to the various types of blur in much the way the model predicts. The model and algorithm allow one to manipulate blur precisely and to achieve the desired perceived scale efficiently.

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Section: Introductionmentioning

confidence: 99%

Using blur to affect perceived distance and size

et al. 2010

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“…Further details are provided in our survey paper on object space techniques [3]. Additionally, simulating depth of field using these distributed ray tracing approaches is more computationally expensive than standard ray tracing.…”

Section: Object Space Methods For Depth Of Fieldmentioning

confidence: 99%

“…This transformation is parameterized by a camera angle and location. There are algorithms that can be used to apply blur in object space [3] where the entire geometry of the scene is present. Yet, after the transformation to image space, there are a wider variety of methods for applying depth of field [4].…”

Section: Introductionmentioning

confidence: 99%

Elimination of artifacts due to occlusion and discretization problems in image space blurring techniques

et al. 2005

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Traditional computer graphics methods render images that appear sharp at all depths. Adding blur can add realism to a scene, provide a sense of scale, and draw a viewerÕs attention to a particular region of a scene. Our image-based blur algorithm needs to distinguish whether a portion of an image is either from a single object or is part of more than one object. This motivates two approaches to identify objects after an image has been rendered. We illustrate how these techniques can be used in conjunction with our image space method to add blur to a scene.

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“…The origin Oxyz of the reference system is fixed as the projection of the point C on the horizontal xy-plane; the point C represents the center of camera lenses assuming that the thin lens model is implemented to realize the propose triangulation system. As a matter of fact, an optical system composed of more lenses can be approximated by a unique lens of reduced thickness (negligible with respect to the radii of curvature of the lens surfaces) whether specific conditions are satisfied [41]. It is worth noticing that we have implemented the thin lens model rather than pin-hole model since several experiments have proven that the former is better than the latter and provides more accuracy in the vehicle pose estimation.…”

Section: Mathematical Reviewmentioning

confidence: 99%

An Embedded Vision System for Real-Time Autonomous Localization Using Laser Profilometry

Patruno

Marani

Nitti

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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In this paper, we propose an embedded vision system based on laser profilometry able to get the pose of a vehicle and its relative displacements with reference to the constitutive media of a structured environment. Fundamental equations for laser triangulation are developed and encoded for their actual implementation on an embedded system. It is made of a laser source that projects a line-shaped beam onto the environment and an on-chip camera able to frame the laser light. Images are then sent to the inexpensive Raspberry Pi onboard computer, which is responsible for processing tasks. For the first time, laser profilometry is coupled with the correlation of laser signatures on a low-cost and low-resource processing board for vehicle localization purposes. Several validation tests of the proposed sensor have proven the effectiveness of the system with respect to commercially available sensors such as inductive sensors and standard odometers, which fail when the vehicle crosses path interceptions or its wheels undergo unavoidable slippages. Moreover, further comparisons with other vision-based techniques have also proven the good performances of this embedded system for real-time localization of vehicles.Index Terms-Visual odometry, image processing, laser profilometry, indoor autonomous vehicle navigation, real-time embedded systems. 1524-9050

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Camera Models and Optical Systems Used in Computer Graphics: Part I, Object-Based Techniques

Cited by 31 publications

References 9 publications

Using blur to affect perceived distance and size

Using blur to affect perceived distance and size

Elimination of artifacts due to occlusion and discretization problems in image space blurring techniques

An Embedded Vision System for Real-Time Autonomous Localization Using Laser Profilometry

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