R. Jarvis, "Range sensing for computer vision," in Three-Dimensional Object Recognition Systems (A. K. Jain and P. J. Flynn, eds.), vol. 1 of Advances in Image Communications, pp. 17--56, Amsterdam: Elsevier Science Publishers, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....patches of particular surface types. These highly structured patches provide useful cues for identifying objects or locating features of an object. 1 Introduction 3D sensors such as laser stripe scanners and temporally encoded structured light systems generate dense surface position samples [1]. This paper considers a fundamental step in processing this data, that of partitioning the points into sets which correspond to smooth surface patches. The boundaries of a smooth surface patch is formed from three di erent types of boundary segments: visibility boundaries; occlusion boundaries; ....

Ray Jarvis. Range sensing for computer vision. In Anil K. Jain and Patrick J. Flynn, editors, Three-Dimensional Object Recognition Systems, volume 1 of Advances in Image Communications, pages 17-56. Elsevier Science Publishers, Amsterdam, 1993.

....reconstruire le gomtrie 3D et la texture partir d une seule image, en utilisant la stroscopie par correlation. Mots cls . forme, lumiare structure, stroscopie, calibrage 3 i Introduction 1. 1 Motivation The 3D shape of a given surface can be digitized using either passive or active methods [8, 4]. Passive methods use one or several optical sensors (cameras) and work either by extracting depth information from a single image (as in range from focus or shape from shading) or from two or more images taken from different places, from which features are extracted and matched to get 3D shape ....

....Instead of using different methods for active (i.e. structured light) and passive (i.e. stereoscopic) systems, we propose to use a matching algorithm originally developed for passive stereoscopy in a structured light system. 1. 2 Related work Among the large collection of range sensing techniques [8], the methods based on projecting a known light pattern on the 3D scene have been the subject of various studies for the last twenty years. Recent reviews compare the different ways of coding and extracting 3D information from structured light [1, 4] The light projected onto the scene is usually ....

R. Jarvis. Range sensing for computer vision. In A.K. Jain and P.J. Flynn, editors, Three-Dimensional Objects Recognition Systems. Elsevier Science Publishers, 1993.

....et pour reconstruire le gomtrie 3D et la texture partir d une seule image, en utilisant la stroscopie par corrlation. Mots cls : forme, lumire structure, stroscopie, calibrage 1 Introduction 1. 1 Motivation The 3D shape of a given surface can be digitized using either passive or active methods [8, 4]. Passive methods use one or several optical sensors (cameras) and work either by extracting depth information from a single image (as in range from focus or shape from shading) or from two or more images taken from di erent places, from which features are extracted and matched to get 3D shape by ....

....Instead of using di erent methods for active (i.e. structured light) and passive (i.e. stereoscopic) systems, we propose to use a matching algorithm originally developed for passive stereoscopy in a structured light system. 1. 2 Related work Among the large collection of range sensing techniques [8], the methods based on projecting a known light pattern on the 3D scene have been the subject of various studies for the last twenty years. Recent reviews compare the di erent ways of coding and extracting 3D information from structured light [1, 4] The light projected onto the scene is usually ....

R. Jarvis. Range sensing for computer vision. In A.K. Jain and P.J. Flynn, editors, Three-Dimensional Objects Recognition Systems. Elsevier Science Publishers, 1993.

....its significance and the types of errors that are encountered. This, in turn, requires an understanding of some of the basic workings of laser rangefinders, which are described here. For an in depth discussion of other range imaging devices, their manufacturers and performance, see [Besl 1988] [Jarvis 1993]. Figure 3 2. Experimental setup showing triangulation based laser rangefinder attached to SCARA manipulator (at right) The turntable is used to rotate parts to present different orientations to the scanner. 31 3.2.1 Rangefinder Camera Characteristics The main issues that need to be ....

R. Jarvis. Range Sensing for Computer Vision. International Journal of Computer Vision, pp. 17--56, 1993.

....then be solved using standard techniques. The use of this non linear model reduces the error in the generated 3D data by over an order of magnitude. 2 Subject Terms Laser pro ler, calibration, nonlinear methods, least squares. 3 1 Introduction A laser stripe pro ler is one of many techniques [1] for generating dense 3D surface information. Such data has uses in computer vision, in computer graphics for generation of realistic object models, and in medical applications. A laser stripe pro ler consists of a laser source, camera, and linear motion platform, in an arrangement such as ....

Ray Jarvis. Range sensing for computer vision. In Anil K. Jain and Patrick J. Flynn, editors, Three-Dimensional Object Recognition Systems, volume 1 of Advances in Image Communications, pages 17-56. Elsevier Science Publishers, Amsterdam, 1993.

....in the camera lens. The way in which stripe data is extracted from the camera images leads to a natural formulation of the calibration problem as a nonlinear least squares problem. This can then be solved using standard techniques. 1 Introduction A laser stripe profiler is one of many techniques [1] for generating dense 3D surface information. Such data has uses in computer vision, in computer graphics for generation of realistic object models, and in medical applications. A laser stripe profiler consists of a laser source, camera, and linear motion platform, in an arrangement such as ....

R. Jarvis, "Range sensing for computer vision," in Three-Dimensional Object Recognition Systems (A. K. Jain and P. J. Flynn, eds.), vol. 1 of Advances in Image Communications, pp. 17--56, Amsterdam: Elsevier Science Publishers, 1993.

....stripe data is extracted from the camera images leads to a natural formulation of the calibration problem as a nonlinear least squares problem. This can then be solved using standard techniques. Keywords: Range sensor, calibration 1 Introduction A laser stripe profiler is one of many techniques [4] for generating dense 3D surface information. Such data has uses in computer vision, in computer graphics for generation of realistic object models, and in medical applications. A laser stripe profiler consists of a laser source, camera, and linear motion platform, in an arrangement such as ....

Ray Jarvis. Range sensing for computer vision. In Anil K. Jain and Patrick J. Flynn, editors, Three-Dimensional Object Recognition Systems, volume 1 of Advances in Image Communications, pages 17--56. Elsevier Science Publishers, Amsterdam, 1993.

....in section 6. Section 7 concludes the paper. 2. 3D ACQUISITION 2.1. Motivations for structured light Structured light acquisition systems use the projection of a known pattern of light (in our case, parallel stripes ) to recover 3D coordinates. Compared to other 3D acquisition techniques [14], structured light benefits from some interesting properties. First, 3D acquisition can be very fast. A single image with stripes contains 3D information so that subject s motion is not a problem. Secondly, the additional cost only concerns the projector and its slide. Thirdly, the projector ....

R. Jarvis, "Range Sensing for Computer Vision", In ThreeDimensional Object Recognition Systems, Advances in Image Communication, Volume 1, A.K. Jain and P.J. Flynn (Editors), Elsevier Science Publisher 1993, pp 17-56.

....summarizes results of practical tests with the system. Section 7 concludes the paper. 2. MOTIVATIONS FOR STRUCTURED LIGHT Structured light acquisition systems use the projection of a known light pattern (in our case, parallel stripes ) to recover 3D coordinates. Among 3D acquisition solutions [4], the choice for structured light has been motivated by the following considerations. First, compared to a classical camera, the additional cost is limited to a projector and its slide. Secondly, a single image with the projected light pattern suffices to recover absolute 3D coordinates. This ....

R. Jarvis, "Range Sensing for Computer Vision", Three-Dimensional Object Recognition Systems, Advances in Image Communication, Volume 1, A.K. Jain and P.J. Flynn (Editors), Elsevier Science Publisher 1993, pp. 17-56.

....representations: a facial surface matching algorithm and a profile matching procedure after intrinsic normalization. Recognition results are presented in section 5. Section 6 concludes the paper. 2 3D acquisition 2. 1 Motivations for structured light Among the possible range acquisition systems [11,12], structured light has emerged as the solution for 3D acquisition in our context. It is based on the projection of a known light pattern (in our case parallel stripes ) The light pattern deformation, captured by a camera, contains the depth information of the scene. Four advantages motivate our ....

Jarvis, R., Range Sensing for Computer Vision, in: Three-Dimensional Object Recognition Systems, K. Jain and P. J. Flynn, eds., Advances in Image Communication, vol 1, Elsevier Science Publisher, 1993, 17-56.

....of the system at Arrows MI2 ESAT KU Leuven is documented. 1 Introduction One shot range finders attract attention since 3 D scene analysis has become widely popular. Besides passive techniques relying on stereo algorithms various kinds of active 3 D measurement devices were developed. See [Jar93, Vuy87] for overview of the whole gamut of methods. Vuylsteke and Oosterlinck [VO90] have invented attractive idea of local redundant ray index encoding. Based on the idea working experimental setup was developed and tested with promising results. In short time some successful applications of the system ....

R. Jarvis. Range sensing for computer vision. In A.K. Jain and P.J. Flyn, editors, Three Dimensional Object Recognition Systems, volume 1 of Advances in Image Communication, pages 17--56. Elsvier Science Publishers B.V., 1993. 27

....due to sensor s construction. 1 Introduction Range finders utilizing active structured illumination attract attention since 3 D scene analysis has become widely popular. Besides passive techniques relying on stereo algorithms various kinds of active 3 D measurement devices were developed. See [1, 2] for the overview of the whole gamut of methods. This text documents the implementaion of the Laser Plane Range Finder (LPRF) at the Computer Vision Laboratory. The text is organized as follows. In Section 2, the principles of the sensor are explained. Concise description of the system is given ....

R. Jarvis. Range sensing for computer vision. In A.K. Jain and P.J. Flyn, editors, Three Dimensional Object Recognition Systems, volume 1 of Ad8 vances in Image Communication, pages 17--56. Elsvier Science Publishers B.V., 1993.

....from 4 sided patches or exactly four faces can meet at a vertex. 6 4. 1 Reconstruction of points A large number of depth perception methods, called Shape from X, have recently appeared as the outcome of the intensive research carried out in the area of active and passive range sensing [9, 10, 11, 12]. For our purposes, only relatively precise and dense measurement is benefitial and therefore we will shortly discuss the principles of Shape from Stereo and Shape from Structured Light as these are currently the most promising approaches. Stereo vision uses two perspective cameras placed at ....

R. Jarvis. Range sensing for computer vision. In A.K. Jain and P.J. Flyn, editors, Three Dimensional Object Recognition Systems, volume 1 of Advances in Image Communication, pages 17--56. Elsvier Science Publishers B.V., 1993.

.... sensors have been developed for acquiring 3D information about visible surfaces, ranging from passive sensors such as binocular (Brint and Brady 1989, Krotkov et al. 1990) and trinocular systems (Hansen et al. 1988) to active sensors such as structured light systems (Jarvis 1983, Besl 1990, Jarvis 1993) and laser scanners (Ferrie et al. 1990) This paper considers the calibration (i.e. determining the parameters of the transformation from raw sensor data into 3D coordinates) of the structured light system described below. Structured light systems have the advantage over passive system of ....

Jarvis, R. 1993. Range sensing for computer vision. In Jain, A. K.; Flynn, SLS Calibration \Delta References 16 P. J., editors, Three-Dimensional Object Recognition Systems, pp. 17--56. Elsevier Science Publishers.

....their spatial location is much more well defined. This leads to higher accuracy 3D data. An Alternative Interpretation of Structured Light System Data 1 1. Introduction One way in which structured light can be used to determine 3D positions in space is to project a plane of light into the scene (Jarvis 1993). The intersection of this plane with the visible surfaces in the scene forms an illuminated stripe on those surfaces. The illuminated stripe can easily be detected in the image plane. The position of the stripe in 3 space (i.e. the position of the illuminated surface points) can be determined by ....

Jarvis, R. 1993. Range sensing for computer vision. In Jain, A. K.; Flynn, P. J., editors, Three-Dimensional Object Recognition Systems, volume 1 of Advances in Image Communications, pp. 17--56. Elsevier Science Publishers, Amsterdam.

....with estimating these from the 3D data acquired by a sensor such as a structured light system. Eight different estimation methods are described and a comparison of their performance is presented. 1 Introduction Sensors such as temporally encoded structured light systems [1] and laser scanners [2] can generate dense 3D position samples from the surfaces of objects. The basic properties of the underlying surface at a data point are described by the osculating paraboloid. This is the local quadratic approximation to the surface which has second order contact. Moreover, it is possible to ....

Ray Jarvis. Range sensing for computer vision. In A. K. Jain and P. J. Flynn, editors, Three-Dimensional Object Recognition Systems, pages 17--56. Elsevier Science Publishers, 1993.

....of a variety of methods for acquiring 3D information about the world. This paper describes a method for obtaining accurate 3D measurements using a structured light system (SLS) Structured light is a general concept and there are a number of ways of exploiting it to obtain 3D information e.g. [1, 2, 3, 4, 5, 6, 7]. As such, a brief description of the specific SLS considered in this paper follows. Refer to Figure 1. The SLS consists of a projector together with a camera. The projector projects a coded stripe pattern on the scene and the camera captures an image. Hence for each visible point in the world ....

Ray Jarvis. Range sensing for computer vision. In Anil K. Jain and Patrick J. Flynn, editors, Three-Dimensional Object Recognition Systems, volume 1 of Advances in Image Communications, pages 17--56. Elsevier Science Publishers, Amsterdam, 1993.

....into patches of particular surface types. These highly structured patches provide useful cues for identifying objects or locating features of an object. 1 Introduction 3D sensors such as laser stripe scanners and temporally encoded structured light systems generate dense surface position samples [1]. This paper considers a fundamental step in processing this data, that of partitioning the points into sets which correspond to smooth surface patches. The boundaries of a smooth surface patch is formed from three different types of boundary segments: visibility boundaries; occlusion boundaries; ....

Ray Jarvis. Range sensing for computer vision. In A. K. Jain and P. J. Flynn, editors, Three-Dimensional Object Recognition Systems, pages 17--56. Elsevier Science Publishers, 1993.

....for locating image features. Experimental evaluation shows that it is important to use substripe estimation and incorporate lens distortion in the projector model. 1 Introduction Structured light is a general concept and there are a number of ways of exploiting it to obtain 3D information e.g. [1]. The structured light system (SLS) considered in this paper consists of a projector and a camera. The projector projects a sequence of band patterns which together identify one of 256 stripe values at each visible point in the world. Given the parameters of the SLS, obtained during system ....

Ray Jarvis. Range sensing for computer vision. In A. K. Jain and P. J. Flynn, editors, Three-Dimensional Object Recognition Systems, pages 17--56. Elsevier Science Publishers, 1993.

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R. Jarvis, "Range sensing for computer vision," in Three-Dimensional Object Recognition Systems (A. K. Jain and P. J. Flynn, eds.), vol. 1 of Advances in Image Communications, pp. 17--56, Amsterdam: Elsevier Science Publishers, 1993.

No context found.

R. Jarvis, "Range sensing for computer vision," in Three-Dimensional Object Recognition Systems (A. K. Jain and P. J. Flynn, eds.), vol. 1 of Advances in Image Communications, pp. 17--56, Amsterdam: Elsevier Science Publishers, 1993.

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R. Jarvis, Range sensing for computer vision, Advances in Image Communications. Elsevier Science Publishers, Amsterdam (1993) 17--56.

No context found.

R. Jarvis, "Range sensing for computer vision," in Three-Dimensional Object Recognition Systems (A. K. Jain and P. J. Flynn, eds.), vol. 1 of Advances in Image Communications, pp. 17--56, Amsterdam: Elsevier Science Publishers, 1993.

No context found.

Ray Jarvis. Range sensing for computer vision. In Anil K. Jain and Patrick J. Flynn, editors, Three-Dimensional Object Recognition Systems, volume 1 of Advances in Image Communications, pages 17-56. Elsevier Science Publishers, Amsterdam, 1993.

No context found.

R. Jarvis, "Range sensing for computer vision," in Three-Dimensional Object Recognition Systems (A. K. Jain and P. J. Flynn, eds.), vol. 1 of Advances in Image Communications, pp. 17--56, Amsterdam: Elsevier Science Publishers, 1993.

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