Reflective surfaces for panoramic imaging

Chahl, Javaan; Srinivasan, M. V.

doi:10.1364/ao.36.008275

Cited by 196 publications

(123 citation statements)

References 4 publications

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“…Ollis et al showed how to compute mirror surfaces that provide an equiangular catadioptric sensor [386]. Previously, Chahl and Srinivasan obtained a similar result, a mirror shape where the angle between the optical axis and a ray back-projected from the camera, is proportional to the same angle after reflecting the ray in the mirror [84]. Conroy and Moore achieved a sensor with solid angle pixel density invariance, i.e., where the surface area of a circular image portion is proportional to the solid angle of the corresponding field of view [99].…”

Section: Non-central Catadioptric Camerasmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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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: Non-central Catadioptric Camerasmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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“…We moved a panoramic imaging device (Chahl and Srinivasan 1997) along the previously recorded flight paths with the aid of the robotic gantry (Zeil et al 2003). The robotic gantry was levelled and positioned at the same place on different days by using the embedded nails as fixed external markers.…”

Section: Reconstructing Natural Optic Flowmentioning

confidence: 99%

“…The recording site is indicated by an arrow grabber and stored directly on the hard disk of a computer. These sequences were transformed (''unwarped'') offline from polar to Cartesian coordinates (Chahl et al 1997) and processed by 3-D rendering software (Open Inventor, Silicon Graphics) to interface with the data format required by the replay screen (see below). Since the panoramic imaging device cannot be rotated, the resulting image sequences contained only the translational optic flow component.…”

Section: Reconstructing Natural Optic Flowmentioning

confidence: 99%

Responses of blowfly motion-sensitive neurons to reconstructed optic flow along outdoor flight paths

et al. 2005

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The retinal image flow a blowfly experiences in its daily life on the wing is determined by both the structure of the environment and the animal's own movements. To understand the design of visual processing mechanisms, there is thus a need to analyse the performance of neurons under natural operating conditions. To this end, we recorded flight paths of flies outdoors and reconstructed what they had seen, by moving a panoramic camera along exactly the same paths. The reconstructed image sequences were later replayed on a fast, panoramic flight simulator to identified, motion sensitive neurons of the so-called horizontal system (HS) in the lobula plate of the blowfly, which are assumed to extract self-motion parameters from optic flow. We show that under real life conditions HS-cells not only encode information about self-rotation, but are also sensitive to translational optic flow and, thus, indirectly signal information about the depth structure of the environment. These properties do not require an elaboration of the known model of these neurons, because the natural optic flow sequences generate-at least qualitatively-the same depth-related response properties when used as input to a computational HS-cell model and to real neurons.

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“…The panoramic imaging device consisted of a black and white video camera (Samsung BW-410CA) looking down onto a parabolic mirror which was optimised for constant spatial resolution (Chahl and Srinivasan, 1997). In the captured images the azimuth and elevation are represented in polar coordinates, as angular position and distance from the origin in the image centre, respectively (see figure 1A).…”

Section: A B D Cmentioning

confidence: 99%

An Analysis of the Motion Signal Distributions Emerging from Locomotion through a Natural Environment

Zanker

Zeil

2002

Biologically Motivated Computer Vision

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Abstract. Some 50 years have passed since Gibson drew attention to the characteristic field of velocity vectors generated on the retina when an observer is moving through the three-dimensional world. Many theoretical, psychophysical, and physiological studies have demonstrated the use of such optic flowfields for a number of navigational tasks under laboratory conditions, but little is known about the actual flowfield structure under natural operating conditions. To study the motion information available to the visual system in the real world, we moved a panoramic imaging device outdoors on accurately defined paths and simulated a biologically inspired motion detector network to analyse the distribution of motion signals. We found that motion signals are sparsely distributed in space and that local directions can be ambiguous and noisy. Spatial or temporal integration would be required to retrieve reliable information on the local motion vectors. Nevertheless, a surprisingly simple algorithm can retrieve rather accurately the direction of heading from sparse and noisy motion signal maps without the need for such pooling. Our approach thus may help to assess the role of specific environmental and computational constraints in natural optic flow processing. BackgroundVisual motion information is crucial for maintaining course, avoiding obstacles, estimating distance, and for segmenting complex scenes into discrete objects. Active locomotion generates large-scale retinal image motion that contains information both about observer movement -egomotion -and the three-dimensional layout of the world. The significance of optic flowfields has been recognised since Gibson (1950) illustrated the dynamic events in the image plane resulting from egomotion by sets of homogenously distributed velocity vectors. The actual structure of the twodimensional motion signal distributions experienced by the visual system under natural operating conditions, however, is not only determined by the pattern of locomotion, but also by the specific three-dimensional layout of the local environment and by the motion detection mechanism employed. To understand the design of the neural processing mechanisms underlying flowfield analysis, and in particular the coding strategies of motion sensitive neurones, we thus need to know more about the actual motion signal distributions under real-life conditions.

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Reflective surfaces for panoramic imaging

Cited by 196 publications

References 4 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Responses of blowfly motion-sensitive neurons to reconstructed optic flow along outdoor flight paths

An Analysis of the Motion Signal Distributions Emerging from Locomotion through a Natural Environment

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