A spherical eye from multiple cameras (makes better models of the world)

Baker, Patrick; Fermüller, Cornelia; Aloimonos, Yiannis; Pless, Robert

doi:10.1109/cvpr.2001.990525

Cited by 35 publications

(34 citation statements)

References 6 publications

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“…Fermüller et al also presented the use of global patterns [8] for the case of spherical eye [7]. Baker et al extended their previous work from planar eye [5] to spherical eye [2]. The scene in view is still assumed to be piecewise smooth.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast to the work of [30], the separation of motion components is not limited to image positions at the equator of each principal axis of the image sphere. Unlike the works of [2], [17], [25], our strategy is to separate the directions of translation and rotation from general motion. This avoids the problem of error propagation from translation to rotation, and vice versa.…”

Section: Related Workmentioning

confidence: 99%

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Determining Motion Directly from Normal Flows Upon the Use of a Spherical Eye Platform

Hui

Chung

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Determining Motion Directly from Normal Flows Upon the Use of a Spherical Eye Platform

Hui

Chung

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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“…As there are many efficient pose estimation methods for single cameras, an intuitive solution for overlapping fields of view in multi-camera systems is to estimate the pose for each camera and then reduce the ambiguities produced by the estimated poses of every camera based on their rigid constraint via fusing or polling policies. The methods presented by Baker et al [14] and Viksté et al [15] belong to this category. However, this kind of method does not obtain a unifying pose for the multi-camera system with all the information from all the cameras simultaneously.…”

Section: Related Workmentioning

confidence: 99%

“…Generally, there are two kinds of multi-camera systems: one is designed for overlapping fields of view [13][14][15][16][17] and the other for non-overlapping fields of view [18,19].…”

Section: Related Workmentioning

confidence: 99%

Robust and Accurate Multiple-Camera Pose Estimation toward Robotic Applications

Liu

Xiong

2014

International Journal of Advanced Robotic Systems

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Pose estimation methods in robotics applications frequently suffer from inaccuracy due to a lack of correspondence and real-time constraints, and instability from a wide range of viewpoints, etc. In this paper, we present a novel approach for estimating the poses of all the cameras in a multi-camera system in which each camera is placed rigidly using only a few coplanar points simultaneously. Instead of solving the orientation and translation for the multi-camera system from the overlapping point correspondences among all the cameras directly, we employ homography, which can map image points with 3D coplanar-referenced points.In our method, we first establish the corresponding relations between each camera by their Euclidean geometries and optimize the homographies of the cameras; then, we solve the orientation and translation for the optimal homographies. The results from simulations and real case experiments show that our approach is accurate and robust for implementation in robotics applications. Finally, a practical implementation in a ping-pong robot is described in order to confirm the validity of our approach.

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“…1(d) have become popular for creating high resolution mosaics (McCutchen, 1991;Swaminathan and Nayar, 2000). Camera clusters have been used to recover ego-motion (Baker et al, 2001;Neumann et al, 2003). Planar mirrors create virtual camera clusters, which have been applied to stereo recovery (Gluckman and Nayar, 1999).…”

Section: Introductionmentioning

confidence: 99%

The Raxel Imaging Model and Ray-Based Calibration

Grossberg

Nayar

2005

International Journal of Computer Vision

112

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Abstract. An imaging model provides a mathematical description of correspondence between points in a scene and in an image. The dominant imaging model, perspective projection, has long been used to describe traditional cameras as well as the human eye. We propose an imaging model which is flexible enough to represent an arbitrary imaging system. For example using this model we can describe systems using fisheye lenses or compound insect eyes, which violate the assumptions of perspective projection. By relaxing the requirements of perspective projection, we give imaging system designers greater freedom to explore systems which meet other requirements such as compact size and wide field of view. We formulate our model by noting that all imaging systems perform a mapping from incoming scene rays to photosensitive elements on the image detector. This mapping can be conveniently described using a set of virtual sensing elements called raxels. Raxels include geometric, radiometric and optical properties. We present a novel ray based calibration method that uses structured light patterns to extract the raxel parameters of an arbitrary imaging system. Experimental results for perspective as well as non-perspective imaging systems are included.

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A spherical eye from multiple cameras (makes better models of the world)

Cited by 35 publications

References 6 publications

Determining Motion Directly from Normal Flows Upon the Use of a Spherical Eye Platform

Determining Motion Directly from Normal Flows Upon the Use of a Spherical Eye Platform

Robust and Accurate Multiple-Camera Pose Estimation toward Robotic Applications

The Raxel Imaging Model and Ray-Based Calibration

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