G. D. Hager and K. Toyama. X vision: A portable substrate for real-time vision applications. Computer Vision and Image Understanding: CVIU, 69(1):23--37, 1998.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....for the top corners of the sub assembly the recovered center and corner locations are marked. positions of the top two corners of the device. Given these estimates of corner locations, local image processing routines can track the corners at fieldrate (60 Hz) We utilize the XVision package [8] to perform these tracking tasks. The flexible nature of XVision allows programming constraints between the two corner trackers we used. This improves robustness of the system by allowing it to recover from transient occlusion events. For example, if one corner tracker fails, information from the ....

G. D. Hager and K. Toyama. X Vision: a portable substrate for real-time vision applications. Computer Vision and Image Understanding, 69(1):23--37, Jan. 1998.

....version of this framework originated in summer of 1993 and the first edge tracker was based on Greg Hager s dissertation work. The potential applications of this technology include surveillance, video conferencing, interactive video games, interactive animation, and vision based user interfaces [6]. The original Visual Tracking System provides an application independent set of tools for visual feature tracking optimized to be simple to configure at the user level, yet extremely fast to execute. The principle point of all possible applications developed from the Visual Tracking System is ....

....directly with his or her workstation in a nonintrusive, natural manner. Hence, as opposed to most multimedia applications where the visual signal is passively transmitted. By using the Visual Tracking System, it is possible to program interactive, feedback based applications with passive sensing [6]. Since the Visual Tracking System is an ideal choice of prototype for video conferencing, remote surveillance, it is not surprised that it is ported in this thesis work functioning as the multimedia application layer. It interacts with our proposed multicasting middle ware layer and transmission ....

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Gregory Hager and Kentaro Toyama. X vision: A portable substrate for real-time vision applications. Technical report, Department of Computer Science, Yale University, 1994.

....minimal requirement of semi automatic video surveillance systems is the capability of tracking multiple objects or groups of objects in the presence of background noise and lighting variations. Various tracking algorithms have been published that di er in their respective strengths and weaknesses [8, 11, 4, 6]. These systems show that robustness to illumination changes can be substantially bolstered if detailed information is available about the scene and the objects of interest, such as 3 D scene geometry, object sizes, velocities, shapes, modes of their interaction, etc. Obtaining robustness in the ....

G. D. Hager and K. Toyama. X Vision: A portable substrate for real-time vision applications. Computer Vision and Image Understanding, 69(1):23--37, January 1998.

....I 1 UI 11ii IN 11KNAIIUNAL UNIiKiNL:i UN UNIFU ILK I;IUN ANU I AI 11KN IiL:UUN111UN, FlAWAll, USA, PP. 287 292, VOL. 1, DECEMBER 2001. The 3D Line Motion Matrix and Alignment of Line Reconstructions Adrien Bartoli Peter Sturm INRIA Rh6ne Alpes, 655, av. de l Europe 38334 St. Ismier cedex, France. first. last inria.fr Abstract We study the problem of aligning two 3D line reconstructions expressed in Plicker line coordinates. We introduce the 6x6 3D line motion matrix that acts on Pliicker coordinates in projective, affine or Euclidean ....

....I 1 UI 11ii IN 11KNAIIUNAL UNIiKiNL:i UN UNIFU ILK I;IUN ANU I AI 11KN IiL:UUN111UN, FlAWAll, USA, PP. 287 292, VOL. 1, DECEMBER 2001. The 3D Line Motion Matrix and Alignment of Line Reconstructions Adrien Bartoli Peter Sturm INRIA Rh6ne Alpes, 655, av. de l Europe 38334 St. Ismier cedex, France. first. last inria.fr Abstract We study the problem of aligning two 3D line reconstructions expressed in Plicker line coordinates. We introduce the 6x6 3D line motion matrix that acts on Pliicker coordinates in projective, affine or Euclidean ....

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G. Hager and K. Toyama. X vision: A portable substrate for real-time vision applications. CVIU, 69(1):237, 1998.

....structure from motion only relies on 2D to 2D correspondences. These are more easily obtained since they depend on the image information only. There are two classes of (semi )automatic methods to find them: a discrete approach, known as matching [13] and a continuous approach, known as tracking [14]. A relevant class of structure from motion methods is known as the Eu clidean reconstruction [7, 28, 22, 29, 18, 6] It assumes that the camera is calibrated (i.e. the camera intrinsic parameters are known) From this knowledge, one can reconstruct the structure of the scene and the motion of ....

G. Hager and K. Toyama. X vision: A portable substrate for real- time vision applications. Computer Vision and Image Understanding, 69(1):23 37, 1998.

....minimal requirement of semi automatic video surveillance systems is the capability of tracking multiple objects or groups of objects in the presence of background noise and lighting variations. Various tracking algorithms have been published that di#er in their respective strengths and weaknesses [8, 11, 4, 6]. These systems show that robustness to illumination changes can be substantially bolstered if detailed information is available about the scene and the objects of interest, such as 3 D scene geometry, object sizes, velocities, shapes, modes of their interaction, etc. Obtaining robustness in the ....

G. D. Hager and K. Toyama. X Vision: A portable substrate for real-time vision applications. Computer Vision and Image Understanding, 69(1):23--37, January 1998.

....face changes. We have applied the proposed algorithm on each of these sequences. Experimental results show a clear improvement over frame based detection results. Related work Past work has concentrated either on detection [13, 15] or on exploiting the temporal aspect in the form of face tracking [2, 7]. Detection can be feature based [18] based on a statistical model [13, 15] on colour [16] geometric shape [3] or motion information [7, 17] Features may not convey complete information about the face. Model based approaches suffer from the drawbacks of the specific model skin colour is ....

....detection results. Related work Past work has concentrated either on detection [13, 15] or on exploiting the temporal aspect in the form of face tracking [2, 7] Detection can be feature based [18] based on a statistical model [13, 15] on colour [16] geometric shape [3] or motion information [7, 17]. Features may not convey complete information about the face. Model based approaches suffer from the drawbacks of the specific model skin colour is susceptible to changes in lighting conditions and motion information may be distracted by alternate motion in the video. In order to incorporate ....

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G. Hager and K. Toyama. X vision : A portable substrate for real-time vision applications. CVIU, 69(1):23--37, 1998.

....with a catalogue of domain speci c recognizable landmarks (e.g. lane boundaries, ceiling light, bar codes, door edges, etc. Recently, it has become possible to track at frame rates a modest number of features (on the order of a dozen) on conventional personal computers using for example X vision [2]. Yet in a scene or for an object of interest, there may be hundreds or thousands of feature points which could serve as landmarks. In fact, part of the process of most feature based structurefrom motion algorithms is to establish correspondence amongst a large number of features. For real time ....

....of an image is searched for the location which minimizes the sum of squared di erences (SSD) between the template and the image intensities about the location. Due to 3 D viewpoint changes however, the image pattern will di er from the template, and this is sometimes modeled as an ane image warp [2]. Here, our goal is to select distinctive templates (landmarks) from one image which can be readily recognized in a second image acquired from a di erent viewpoint. Let the irradiance (intensity) across the image plane be denoted by I(x; y) where x and y are the image coordinates. Since we are ....

G. Hager and K. Toyama. X vision: A portable substrate for real-time vision applications. Computer Vision & Image Understanding, 69(1):23-37, 1998.

....determine the 3D position of fingers and contacts from the image plane directly. The methods described here can be easily extended to a stereo perspective camera model that can provide full 3 D depth recovery as well [16] The vision system we are using is a modification of Hager s X Vision system [5]. Each tracker has a state vector consisting of position and orientation information which is updated after each iteration of the tracking loop. Once a line or region tracker is initialized on an edge or window within the image it will track the feature at frame rates. In our experiments we used ....

G. D. Hager and K. Toyama. X vision: A portable substrate for real-time vision applications. Technical report, Department of Computer Science, Yale University, 1995.

....reliability of the estimate. The use of Kalman filtering to assist in tracking for has been previously reported McLauchlan et.al. 8] We apply also separate Kalman filters to each tracking feature. However, in our approach also features assist each other in the tracking of features. Hager et.al. [9] proposed a similar idea of using geometric constraints and feature states for robust tracking. The Hager et.al. technique uses a binary switching between winning features and loosing features to decide who gives direction for other features to track. Our approach uses all the features for ....

G.D. Hager, K. Toyama, X Vision: A Portable Substrate for Real-Time Vision Applications, Description of the X Vision class library and applications, Technical Report, Yale University, 1995.

....tracking moving objects at considerably faster than frame rate. 1 Introduction Relative motion of objects in the environment with respect to a vision system causes visual motion. Analysis of such visual motion in sequences of images has been addressed by many researchers over a number of years [20, 18, 12, 19]. The techniques that have been used include optical flow [9, 13, 1, 2] feature point correspondence [8, 5] edge or contour correspondence [16, 7, 21, 23, 6] active contour models [15, 3, 4] and model based tracking [10, 17] Recently there has been an increase in robotic applications that ....

.... optical flow [9, 13, 1, 2] feature point correspondence [8, 5] edge or contour correspondence [16, 7, 21, 23, 6] active contour models [15, 3, 4] and model based tracking [10, 17] Recently there has been an increase in robotic applications that require visual tracking of one sort or another [12]. This visual tracking is usually done in the context of a particular overall task. The techniques frequently have a limited applicability beyond the task that they have been designed for, producing a multiplicity of ad hoc techniques. One of the techniques that has commonly been used as a basis ....

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G. D. Hager and K. Toyama. X Vision: A portable substrate for real-time vision applications. Technical Report TR-1078, Department of Computer Science, Yale University, New Haven, CT, 1995.

....real time. Current techniques exploited in industrial environment run on marked and simple objects. Our goal is to design a method to extract relevant image features without such constraints. Several authors have proposed ways to solve tracking of features in image sequences with monocular vision [1, 7, 8, 10, 14] or stereo vision [3] We have developed an original method for 2D tracking of complex objects which can be approximately modeled by a polyhedral shape. The efficiency of this method is demonstrated through a visual servoing homing task which consists in positioning a camera mounted on the end ....

G.D. Hager and K. Toyama. X Vision : A portable substrate for real-time vision applications. Computer Vision and Image Understanding, vol.69, no.1, pp. 23-37, Jan. 1998.

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G. Hager and K. Toyama. X Vision: A Portable Substrate for Real-Time Vision Applications, Computer Vision and Image Understanding, 69:1, pp.23-37, Jan. 1998.

No context found.

G. D. Hager and K. Toyama. X vision: A portable substrate for real-time vision applications. Computer Vision and Image Understanding: CVIU, 69(1):23--37, 1998.

No context found.

G. Hager and K. Toyama. X vision : A portable substrate for real-time vision applications. CVIU, 69(1):23--37, 1998.

No context found.

G.D. Hager and K. Toyama. X vision: A portable substrate for real-time vision applications. Technical report, Yale University, Dept. of Computer Science, 1995.

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