Invariant descriptors for 3D object recognition and pose

Forsyth, David; Mundy, Joseph L.; Zisserman, Andrew; Coelho, Clarimar José; Heller, Aaron; Rothwell, Charlie

doi:10.1109/34.99233

Cited by 366 publications

(172 citation statements)

References 20 publications

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“…Early work that used conics for computer vision applications was reported in [2][3][4]. Circular markers have been extensively used in tracking applications due to their robustness properties [6,7].…”

Section: Previous Workmentioning

confidence: 99%

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Camera Calibration Using Two Concentric Circles

Abad

Camahort

Vivó

2004

Lecture Notes in Computer Science

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Abstract. We present a simple calibration method for computing the extrinsic parameters (pose) and intrinsic parameters (focal length and principal point) of a camera by imaging a pattern of known geometry. Usually, the patterns used in calibration algorithms are complex to build (three orthogonal planes) or need a lot of features (checkerboard-like pattern). We propose using just two concentric circles that, when projected onto the image, become two ellipses. With a simple mark close to the outer circle, our algorithm can recover the full pose of the camera. Under the perfect pinhole camera assumption, the pose and the focal length can be recovered from just one image. If the principal point of the camera has to be computed as well, two images are required. We present several results, using both synthetic and real images, that show the robustness of our method.

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Section: Previous Workmentioning

confidence: 99%

“…The projected center of the circles has to be computed in order to recover the normal to the supporting plane. The direction of the Z w axis in the camera coordinate system (or R 3 ) can be computed as follows [2,3]:…”

Section: Recovering the Circles' Projected Centermentioning

confidence: 99%

Camera Calibration Using Two Concentric Circles

Abad

Camahort

Vivó

2004

Lecture Notes in Computer Science

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“…The problem has been extensively investigated and there are many papers concentrating on 3D location of circular objects [5,15,19,16]. We adopted the monocular camera-positioning algorithm proposed in [16,19].…”

Section: The "One-circle" Algorithmmentioning

confidence: 99%

Eye gaze estimation from a single image of one eye

Wang

Sung

Venkateswarlu

2003

Proceedings Ninth IEEE International Conference on Computer Vision

119

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“…The 3D transform algorithm implements an adaption of Forsyth's ellipse back projection algorithm (Forsyth et al, 1991) to recover a general 3D transformation from object co-ordinates to camera co-ordinates. This algorithm is computationally complex but produces accurate 3D information for the tag's position and pose.…”

Section: Circletagmentioning

confidence: 99%

Analysing fundamental properties of marker-based vision system designs

Rice

Harle

Beresford

2006

Pervasive and Mobile Computing

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This paper investigates fundamental properties of Marker-based Vision (MBV) systems. We present a theoretical analysis of the performance of basic tag designs which is extended through simulation to investigate the effects of different processing algorithms. Real-world data are processed and related to the simulated results. Image processing is performed using Cantag, an open source software toolkit for building Marker-based Vision (MBV) systems that can identify and accurately locate printed markers in three dimensions. Cantag supports multiple fiducial shapes, payload types, data sizes and image processing algorithms in one framework. This paper explores the design space of tags within the Cantag system, and describes the design parameters and performance characteristics which an application writer can use to select the best tag system for any given scenario.

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Invariant descriptors for 3D object recognition and pose

Cited by 366 publications

References 20 publications

Camera Calibration Using Two Concentric Circles

Camera Calibration Using Two Concentric Circles

Eye gaze estimation from a single image of one eye

Analysing fundamental properties of marker-based vision system designs

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