Discrete camera calibration from pixel streams

Grossmann, Etienne; Gaspar, José António; Orabona, Francesco

doi:10.1016/j.cviu.2009.03.009

Cited by 13 publications

(21 citation statements)

References 21 publications

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“…Grossmann et al have considered what might be the most discrete camera model, consisting of a sparse set of pixels and associated camera rays, with no requirement that rays have close-by neighboring rays [192,194]. Their proposed self-calibration approach (see Section 5.3.3) only makes the assumption that the camera is central.…”

Section: "Isolated" Camera Raysmentioning

confidence: 99%

“…Grossmann et al showed that a highly discrete camera model, consisting of "isolated" camera rays (see Section 3.3.3), can be selfcalibrated just by taking images of unknown scenes, based on simple assumptions about their appearance [192,194]. Roughly speaking, it is assumed that the smaller the angle between two camera rays, the more likely it is that the associated pixels produce similar greylevels; this is in some sense, an assumption on the statistics of natural images.…”

Section: Image Statisticsmentioning

confidence: 99%

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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: "Isolated" Camera Raysmentioning

confidence: 99%

Section: Image Statisticsmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

View full text Add to dashboard Cite

show abstract

“…This paper departs from traditional calibration in the sense that the pixels forming the camera have a completely unknown topology. Previous works have shown that the statistical properties of natural scenes, and a uniform motion of a camera both in translation and rotation, allow determining the topology of a central camera [10,6]. Here we show that there is a quasi-linear relationship between time-correlation and angular inter-pixel distance, considering small angles and a simple scenario encompassing one bright light on a dark background.…”

mentioning

confidence: 82%

“…Hyvarinen et al showed that imaging natural scenes allow defining a neuronal topography using Independent Subspace Analysis [7]. In [6], Grossmann et al proposed a method for calibrating a central imaging sensor based on a number of photocells. They need to know (estimate) a priori a function curve relating correlation (or informationdistance) and distance-angles.…”

Section: Introductionmentioning

confidence: 99%

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Topological Auto-Calibration of Central Imaging Sensors

Galego

Ferreira

Bernardino

et al. 2013

Pattern Recognition and Image Analysis

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Abstract. This paper departs from traditional calibration in the sense that the pixels forming the camera have a completely unknown topology. Previous works have shown that the statistical properties of natural scenes, and a uniform motion of a camera both in translation and rotation, allow determining the topology of a central camera [10,6]. Here we show that there is a quasi-linear relationship between time-correlation and angular inter-pixel distance, considering small angles and a simple scenario encompassing one bright light on a dark background. The topology reconstruction algorithm is therefore based on correlating time series (pixel streams) acquired by the pixels of the moving camera. Correlations are converted to inter-pixel distances using a fixed linear transformation. Distances are finally embedded on a plane using a manifold learning methodology, namely Isomap. Experiments on real datasets have been conducted and have shown that the theoretical derivations are accurate for the considered scenario.

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Calibration from Statistical Properties of the Visual World

Grossmann

Gaspar²,

Orabona

2008

Lecture Notes in Computer Science

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Abstract. What does a blind entity need in order to determine the geometry of the set of photocells that it carries through a changing lightfield? In this paper, we show that very crude knowledge of some statistical properties of the environment is sufficient for this task. We show that some dissimilarity measures between pairs of signals produced by photocells are strongly related to the angular separation between the photocells. Based on real-world data, we model this relation quantitatively, using dissimilarity measures based on the correlation and conditional entropy. We show that this model allows to estimate the angular separation from the dissimilarity. Although the resulting estimators are not very accurate, they maintain their performance throughout different visual environments, suggesting that the model encodes a very general property of our visual world. Finally, leveraging this method to estimate angles from signal pairs, we show how distance geometry techniques allow to recover the complete sensor geometry.

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Discrete camera calibration from pixel streams

Cited by 13 publications

References 21 publications

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Topological Auto-Calibration of Central Imaging Sensors

Calibration from Statistical Properties of the Visual World

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