Inverse perspective transform using zero-curvature contour points: application to the localization of some generalized cylinders from a single view

Richetin, M.; Dhome, Michel; Lapresté, Jean-Thierry; Rives, G.

doi:10.1109/34.67647

Cited by 32 publications

(7 citation statements)

References 10 publications

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“…The understanding of the relations between image contours geometry and the shape of the observed object and the viewing parameters is still a challenging problem and it is essential that special shapes are not represented by freeform surfaces without regard to their special properties, but treated in a way more appropriate to their simple nature. Explicit relations from occluding contours to the model of a curved threedimensional object have been presented for objects with geometrical properties such as generalized cylinders or surfaces of revolution (Dhome, 1992;Ferri, 1993;Kriegman, 1990 ;Ponce, 1989). More recent works are based on the image contour of a cylinder crosssection when it is visible.…”

Section: Pose Estimation From Cylindersmentioning

confidence: 99%

“…In order to guarantee a fast convergence and a good stability of the VVS, it is useful to initialize the algorithm close enough to the real pose. For this purpose, we use the modifed version of the Haralick's method and the DeMenthon iterative method (DeMenthon, 1995) for points, that of Dhome (Dhome, 1992) to get the 4 solutions for the pose of a circle and the pose determination of the axis of a circular cylinder described at the paragraph 3.5. With this initial pose parameters estimates for the attitude of the camera with respect to the object of interest (a laparoscopic surgical instrument, see Figure 11), the following control law is applied to the virtual camera (20) until the control vector becomes smaller than a specified value.…”

Section: Pose Estimation With Multiple Types Of Geometrical Featuresmentioning

confidence: 99%

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An Introduction to Model-Based Pose Estimation and 3-D Tracking Techniques

Doignon¹

2007

Scene Reconstruction Pose Estimation and Tracking

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The aim of this chapter is to present a general overview of the feature-based 3-D pose estimation and tracking techniques. Principles, classical techniques and recent advances are presented and discussed in the context of a monocular camera. The objective is to focus on techniques employed within both the visual servoing and registration fields for the wideclass of rigid objects. The main assumption to this problem rely on the availability of a 3-D model of the object to track. Introduction: Model-based tracking and pose estimationThe recovery of the 3-D geometric information from 2-D images is a fundamental problem in computer vision. When only one view is available, the appearance or the relative arrangement of the object features of interest should be modelled in a symbolic description so as to be compared with the image descriptors thanks to a similarity criterion. Geometricbased approaches restrict the search for correspondence to a sparse set of geometrical features. They use numerical and symbolic properties of entities available. To automatically compute a rigid-body transformation (the pose), it is necessary to match a 3-D model features with part of the visible 2-D image features, a process referred to as the correspondence problem, and for the past four decades, the model-based pose estimation of objects with a simple geometry has been intensively studied. The major goal is tracking at camera frame rate the pose parameters in the world space. Therefore, features such as points, lines, ellipses are not only extracted from 2-D images, but the 3-D model and the pose of the object has to be also exploited. Related work on model-based 3-D trackingThe modelbased 3-D tracking is closely related to the pose estimation problem. It can cope with abrupt motions and it is generally more efficient to deal with partial occlusions of the object of interest than 2-D tracking. However, it needs the correspondence problem to be solve at least once. The definition of object tracking algorithms in image sequences is an important issue for research and applications related to robot vision. A robust extraction and realtime spatiotemporal tracking of measurements is one of the keys to a successful visual servoingbased tracking, in particular for positionbased visual servoing approaches 360(PBVS) (Hutchinson, 1996), where the tracking error is computed in the task space and thereafter used by the robot control system. The reference work on visionbased navigation is the dynamic tracking approach introduced by Dickmanns and Graefe (Dickmanns, 1988). In this work, the steering of cars, vehicle docking and aircraft landing are performed by dynamic modelling and edge feature extraction and processing. Feature extraction and matching play an important role, and restriction of matching is commonly done by windowing technique. Harris and Stennet (Harris, 1990), Papanikolopoulos et al. (Papanikolopoulos, 1993) and Hager and Toyama (Hager, 1998) with the XVision system restrict the search space of matching to a neighbourhood of th...

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Section: Pose Estimation From Cylindersmentioning

confidence: 99%

Section: Pose Estimation With Multiple Types Of Geometrical Featuresmentioning

confidence: 99%

An Introduction to Model-Based Pose Estimation and 3-D Tracking Techniques

Doignon¹

2007

Scene Reconstruction Pose Estimation and Tracking

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“…Previously proposed mesh construction schemes 6,9,11,20,43] have not been demonstrated upon this type of image. Closed quadric representations 15,28,33,37,38,42,45], including superquadrics and generalized cylinders, are not formulated to model individual surfaces, and so are not applicable to background surface modeling. To construct a space envelope, it does not matter how many objects are in view, nor how much of any object is seen, because the concept of \an object" is not used.…”

Section: Related Workmentioning

confidence: 99%

The Space Envelope: A Representation for 3D Scenes

Hoover¹,

Goldgof²,

Bowyer³

1998

Computer Vision and Image Understanding

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“…In the literature, the estimation of the primitive surface parameters is often done by surface fitting [ 1,7,8,19] or curve fitting of elliptical curves marked on the surface [5,10,13]. This type of measurement methods is generally slow and noise sensitive.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision for Geometric Model Construction and Pose Determination of 3-D Curved Objects

Chia¹

1997

Journal of the Chinese Institute of Engineers

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In this paper a new vision based method for 3-D geometric model construction and pose determination of a curved object is proposed.The key concept in the proposed method is to derive the 3-D object information using some planar patch passing through the object stripe junctions or silhouette edges. This patch will be referred to as a virtual plane. We can derive the virtual plane to obtain the 3-D object stripe junctions or the 3-D silhouette edges of some specific primitive curved objects. Based on the derived 3-D object information, we propose two methods to estimate the shape parameters of primitive objects. The shape parameters completely specify the geometric model of the primitive object. The shape parameters also reflect the pose information (location and orientation) of the object. Experimental results indicate that our method for determining the 3-D surface points and shape parameters of primitive objects are accurate and fast .

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Inverse perspective transform using zero-curvature contour points: application to the localization of some generalized cylinders from a single view

Cited by 32 publications

References 10 publications

An Introduction to Model-Based Pose Estimation and 3-D Tracking Techniques

An Introduction to Model-Based Pose Estimation and 3-D Tracking Techniques

The Space Envelope: A Representation for 3D Scenes

Computer Vision for Geometric Model Construction and Pose Determination of 3-D Curved Objects

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