J. Weber and J. Malik. Rigid body segmentation and shape description from dense optical flow under weak perspective. In Proc. International Conference on Computer Vision, pages 251--256, 1995.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....The contours are used to track the objects. Once the boundaries are obtained from the user, a boundary tracking algorithm is proposed to follow the boundaries in case of motion [39] Feature based methods first extract features from pixels or select pixels having distinct features from others [25, 64, 52, 69, 62, 15, 27, 102]. The feature models for edges, corners, and vertices are presented in [15, 27] A corner is represented by the amplitude of the wedge, its aperture angle and a parameter to measure the smoothness of the wedge. Vertices are defined as a superposition of corner models. The extracted information is ....

J. Weber and J. Malik. Rigid body segmentation and shape description from dense optical flow under weak perspective. In Proceedings of the 5th International Conference on Computer Vision, 1995.

....been adopted for solving these problems. In [1] an occlusion adaptive mesh is used for tracking moving features over several frames. In other approaches, features are extracted from a set of frames and afterwards they are tracked over the sequence. Kalman filters have been used for tracking in [2, 3, 4]. Objects are segmented based on clustering in [3, 5] Simultaneous optical flow estimation and moving object segmentation has been employed in [6] In this approach the moving scene is modeled based on the Median Radial Basis Function (MRBF) network [8] Each output unit of Adrian G. Borg was ....

J. Weber, J. Malik, "Rigid body segmentation and shape description from dense optical flow under weak perspective," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19, no. 2, pp. 139-143, Feb. 1997.

....for 3D structure were indicated in [11, 13] for two weak perspective views there is a one parameter family of solutions, and for three views the solutions are unique up to re ection. In addition, there is a large literature exploring the computation of 3D structure from weakperspective views [1, 8, 9, 14, 15, 18, 22, 23, 28, 29]. However from the approach taken in those papers it seems not easy to develop recognition polynomials for the weak perspective detection of rigidity. Kontsevich [12] developed a mathematical approach to the weak perspective detection of rigidity, using some of the same geometric ideas in [11] ....

Weber, J. and J. Malik: 1997, `Rigid body segmentation and shape description from dense optical ow under weak perspective'. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 139143.

....These approaches are briefly explained here. In the first approach, a dense optic flow field is recovered first and the segmentation is performed by fitting a model (often affine) to the computed flow field (Adiv, 1985; Wang Adelson, 1994; Kollnig et al. 1994; Huang et al. 1995; Weber Malik, 1997 and Bab Hadiashar Suter, 1996 and 1998) Since reliable computation of optic flow by itself is a daunting task (and often requires expensive computations) these methods will likely to be limited to off line applications for a number of years to come. Moreover, separating the two process causes ....

Weber J., Malik J., 1997 "Rigid Body Segmentation and Shape Description from Optical Flow Under weak Perspective" IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, 19(2), 139-143.

.... Schunk, 1981; Black and Anandan, 1993; Ju et al. 1996) as a way both to estimate dense motion fields over the entire visible region of an image sequence, e.g. Black and Anandan, 1993; Ju et al. 1996) and to segment areas of consistent flow into discrete objects, e.g. Black and Jepson, 1996; Weber and Malik, 1995). In order to solve the optic flow constraint equation it is necessary to either apply regularisation, assuming change in motion is smooth over an image region, or parameterise the motion in an entire region using a low dimensional model, for example an affine model. Black et al. have developed a ....

Weber, J. and Malik, J. (1995). Rigid body segmentation and shape description from dense optical flow under weak perspective. In Proc. 5th Int. Conf. on Computer Vision, 251--256, Cambridge, MA.

....have been adopted for solving these problems. In [1] an occlusion adaptive mesh is used for tracking moving features over several frames. In other approaches, features are extracted from a set of frames and afterwards they are tracked over the sequence. Kalman lters have been used for tracking in [2, 3, 4]. Objects are segmented based on clustering in [3, 5] Simultaneous optical ow estimation and moving object segmentation has been employed in [6] In this approach the moving scene is modeled based on the Median Radial Basis Function (MRBF) network [8] Each output unit of Adrian G. Bor s was ....

J. Weber, J. Malik, \Rigid body segmentation and shape description from dense optical ow under weak perspective," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19, no. 2, pp. 139-143, Feb. 1997.

....image ow arises from both object motion and camera motion. The work reported so far can be class ed into two categories. The rst category relies on continuity and discontinuity in the 2D ow eld [1, 10] The second category relies on examining if ows share or violate the epipolar constraints [11, 17]. We take an approach which explicitly uses the epipolar constraint for segmentation, that is, whether epipolar equations are shared or not is explicitly examined. Once the epipolar equations are determined, they are used to match edge points, reducing the search from 2 dimensional to ....

....characterized by the 1D disparity. In the strict sense, the motion disparity here is not the same as stereo disparity. 3 Determining Epipolar Equations for Multiple Rigid Motions by Clustering The recovery of epipolar geometry for multiple motions through matching feature points is a vital step [11, 13, 17]. To determine an epipolar equation, at least 4 pairs of matched points are needed. Since we do not know a priori which points belong to which motion, clustering techniques are required. Given n(n 4) pairs of point matches p i = u i ; v i ; u 0 i ; v 0 i T ; i = 1; n, f = f ....

J. Weber and J. Malik. Rigid body segmentation and shape description from dense optical ow under weak perspective. In Proc. Fifth Int'l Conf. Comput. Vision, pages 251-256, 1995.

....result does not appear to be stated in the literature. Moreover, we explicitly derive general expressions for the constants a; b; c; d; e in terms of the intrinsic and extrinsic parameters (the former including calibration parameters and the latter including motion parameters) Weber and Malik [WM97] appear to be aware of the special case of discrete motion calibrated weak perspective. In that same paper, they use a optic flow measurements as if they were discrete displacements to employ their constraint for motion segmentation and for structure from motion. Our results show that, if the ....

....the uncalibrated affine camera. In section 2 we derive the constraint for the calibrated orthographic camera in the discrete and continuous case. In section 3 we derive the constraint for the (calibrated) weak perspective camera and continuous motion. For the discrete case, we refer the reader to [WM97] and the cited references therein, or our later general affine exposition. Similarly, in section 4, since the algebra involved is very tedious, we simply give an expression for the calibrated camera projection process and then refer to the general result. Finally, in section 5 we present the main ....

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J. Weber and J. Malik. Rigid body segmentation and shape description from dense optical flow under weak perspective. IEEE Trans. on Pattern Analysis and Machine Intelligence, 19(2):139--143, February 1997.

.... of image features in an image from image features in two other images [2, 54, 9] positions) 19] positions,orientations,curvatures) ffl Synthesis of an image from several images [35] ffl Convex hull computation and plane positionning [62] ffl Segmentation of rigid independent motions [56, 72, 78], ffl Stereo analysis: rectification of images [27, 15] and stereo matching with uncalibrated cameras [61] feature based) 15] area based) 63, 11] taking orientation into account) ffl Self calibration of a moving camera [51, 16, 44, 25] The Fundamental matrix represents indeed the minimal ....

J. Weber and J. Malik. Rigid body segmentation and shape description from dense optical flow under weak perspective. Dep. of EECS, University of California at Berkeley, 1994.

....type which build on multiple images, i.e. stereo pairs or motion sequences. Multiple images without a doubt make it infinitely easier to properly segment images (how many animals understand the content of photographs ) Notable models of this type are due to Belhumeur [2] and to Weber and Malik [11]. It is my belief that a robust solution to the general low level vision problem can be found using this approach. The main obstacle is to find more effective and faster ways of estimating the w minimizing E(w) than those presently available. Although this energy approach seems on the surface to ....

J.Weber and J.Malik (1993), Rigid body segmentation and shape description from optical flow, EE/CS preprint, UC Berkeley.

....segmentation. These approaches are briefly explained here. In the first approach, a dense optic flow field is recovered first and the segmentation is performed by fitting a model (often affine) to the computed flow field (Adiv, 1985; Wang Adelson, 1994; Kollnig et al. 1994; Huang et al. 1995; Weber Malik, 1997 and Bab Hadiashar Suter, 1996 and 1998) Since reliable computation of optic flow by itself is a daunting task (and often requires expensive computations) these methods will likely to be limited to off line applications for a number of years to come. Two recent examples of this type of ....

....1998) Since reliable computation of optic flow by itself is a daunting task (and often requires expensive computations) these methods will likely to be limited to off line applications for a number of years to come. Two recent examples of this type of technique are Wang and Adelson (1994) and Weber and Malik (1997). Wang and Adelson (1994) used the Least Median of Squares (LMS) regression technique to recover the parameters of the affine motion model. A K Means clustering algorithm is then implemented to merge the models representing coherent motion in different regions. Both of the above techniques (LMS ....

[Article contains additional citation context not shown here]

Weber J., Malik J., 1997 "Rigid Body Segmentation and Shape Description from Optical Flow Under weak Perspective" IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, 19(2), 139-143.

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J. Weber and J. Malik. Rigid body segmentation and shape description from dense optical flow under weak perspective. In Proc. International Conference on Computer Vision, pages 251--256, 1995.

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J. Weber and J. Malik. Rigid Body Segmentation and Shape Description from Dense Optical Flow Under Weak Perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:139143, Feb 1997.

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Weber, J. and Malik J., Rigid Body Segmentation and Shape Description from Dense Optical Flow Under Weak Perspective, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 2, pp. 139-143, February, 1997.

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