The Experts below are selected from a list of 234 Experts worldwide ranked by ideXlab platform
Takeo Kanade - One of the best experts on this subject based on the ideXlab platform.
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Shape and motion from image streams : a factorization method.
2018Co-Authors: Carlo Tomasi, Takeo KanadeAbstract:Abstract: "Inferring the depth and shape of remote objects and the complete camera motion from a stream of images is possible, but is an ill-conditioned problem when the objects are distant with respect to their size. To overcome this difficulty, we have developed a factorization method to decompose an image stream directly into object shape and camera motion, without computing depth as an intermediate step. The factorization method is explored in a series of technical reports, going from basic principles through implementation. This is the first report in the series, and presents basic concepts in the case of planar motion, in which images are single scanlines.In this situation, an image stream can be represented by the F [cross] P matrix of the image coordinates of P points tracked through F frames. We show that under Orthographic Projection this measurement matrix is of rank 3. Using this observation, we develop an algorithm to recover shape and camera motion, based on the singular value decomposition of the measurement matrix. Noise is defeated by applying a well-conditioned computation to the highly redundant input represented by an image stream. No assumptions are made about smoothness or regularity of the camera motion, and even sudden jumps in the camera velocity are faithfully reproduced in the computed output.
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a paraperspective factorization method for shape and motion recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997Co-Authors: Conrad J Poelman, Takeo KanadeAbstract:The factorization method, first developed by Tomasi and Kanade (1992), recovers both the shape of an object and its motion from a sequence of images, using many images and tracking many feature points to obtain highly redundant feature position information. The method robustly processes the feature trajectory information using singular value decomposition (SVD), taking advantage of the linear algebraic properties of Orthographic Projection. However, an Orthographic formulation limits the range of motions the method can accommodate. Paraperspective Projection, first introduced by Ohta et al. (1981), is a Projection model that closely approximates perspective Projection by modeling several effects not modeled under Orthographic Projection, while retaining linear algebraic properties. Our paraperspective factorization method can be applied to a much wider range of motion scenarios, including image sequences containing motion toward the camera and aerial image sequences of terrain taken from a low-altitude airplane.
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a paraperspective factorization method for shape and motion recovery
European Conference on Computer Vision, 1994Co-Authors: Conrad J Poelman, Takeo KanadeAbstract:The factorization method, first developed by Tomasi and Kanade, recovers both the shape of an object and its motion from a sequence of images, using many images and tracking many feature points to obtain highly redundant feature position information. The method robustly processes the feature trajectory information using singular value decomposition (SVD), taking advantage of the linear algebraic properties of Orthographic Projection. However, an Orthographic formulation limits the range of motions the method can accommodate. Paraperspective Projection, first introduced by Ohta, is a Projection model that closely approximates perspective Projection by modelling several effects not modelled under Orthographic Projection, while retaining linear algebraic properties. We have developed a paraperspective factorization method that can be applied to a much wider range of motion scenarios, such as image sequences containing significant translational motion toward the camera or across the image. We present the results of several experiments which illustrate the method's performance in a wide range of situations, including an aerial image sequence of terrain taken from a low-altitude airplane.
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shape and motion from image streams under orthography a factorization method
International Journal of Computer Vision, 1992Co-Authors: Carlo Tomasi, Takeo KanadeAbstract:Inferring scene geometry and camera motion from a stream of images is possible in principle, but is an ill-conditioned problem when the objects are distant with respect to their size. We have developed a factorization method that can overcome this difficulty by recovering shape and motion under orthography without computing depth as an intermediate step. An image stream can be represented by the 2FxP measurement matrix of the image coordinates of P points tracked through F frames. We show that under Orthographic Projection this matrix is of rank 3. Based on this observation, the factorization method uses the singular-value decomposition technique to factor the measurement matrix into two matrices which represent object shape and camera rotation respectively. Two of the three translation components are computed in a preprocessing stage. The method can also handle and obtain a full solution from a partially filled-in measurement matrix that may result from occlusions or tracking failures. The method gives accurate results, and does not introduce smoothing in either shape or motion. We demonstrate this with a series of experiments on laboratory and outdoor image streams, with and without occlusions.
Jaehyeung Park - One of the best experts on this subject based on the ideXlab platform.
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fresnel and fourier hologram generation using Orthographic Projection images
Optics Express, 2009Co-Authors: Jaehyeung Park, Minsu Kim, Ganbat Baasantseren, Nam KimAbstract:A novel technique for synthesizing a hologram of three-dimensional objects from multiple Orthographic Projection view images is proposed. The three-dimensional objects are captured under incoherent white illumination and their Orthographic Projection view images are obtained. The Orthographic Projection view images are multiplied by the corresponding phase terms and integrated to form a Fourier or Fresnel hologram. Using simple manipulation of the Orthographic Projection view images, it is also possible to shift the three-dimensional objects by an arbitrary amount along the three axes in the reconstruction space or invert their depths with respect to the given depth plane. The principle is verified experimentally.
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view image generation in perspective and Orthographic Projection geometry based on integral imaging
Optics Express, 2008Co-Authors: Jaehyeung Park, Ganbat Baasantseren, Gilbae Park, Jinmo KangAbstract:A novel technique generating arbitrary view images in perspective and Orthographic geometry based on integral imaging is proposed. After capturing three-dimensional object using a lens array, disparity estimation is performed for the pixels at the selected position of each elemental image. According to the estimated disparity, appropriate parts of elemental images are mapped to synthesize new view images in perspective or Orthographic geometry. As a result, the proposed method is capable of generating new view images at arbitrary positions with high resolution and wide field of view.
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50 3 arbitrary view generation in perspective and Orthographic Projection geometry using lens array
SID Symposium Digest of Technical Papers, 2008Co-Authors: Jaehyeung Park, Ganbat Baasantseren, Nam Kim, Gilbae Park, Jinmo Kang, Byoungho LeeAbstract:A novel view image generation algorithm using lens array is proposed. Arbitrary view image in perspective Projection geometry is generated through the central pixel disparity estimation and elemental image mapping with wide field of view and high resolution. View image in Orthographic Projection geometry is also synthesized with high resolution from the elemental images using directional pixel disparity estimation and elemental image mapping.
Robert T Pack - One of the best experts on this subject based on the ideXlab platform.
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network orientation using the scaled Orthographic Projection for parameter initialization
Photogrammetric Engineering and Remote Sensing, 2012Co-Authors: Keith F Blonquist, Robert T PackAbstract:Bundle adjustment is a well known and reliable method used in photogrammetry for image network orientation which requires relatively accurate initial approximations of image orientations and point coordinates. The initialization problem has proved difficult and a variety of initialization techniques have been proposed. We present a new method that takes advantage of the scaled Orthographic Projection to compute direct linear solutions that approximate the image network geometry. The algorithm includes two relative orientation methods adapted from previous work, and incorporates a recently developed Orthographic bundle adjustment method. Following initial network orientation, image coordinates are corrected for perspective to obtain an intermediate solution which is converted into perspective Projection parameters and a final bundle adjustment is performed. The method has been tested using several image sets and has proven to be effective at various fields of view, with a variety of imaging network geometries, and with different object point geometries.
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a bundle adjustment approach with inner constraints for the scaled Orthographic Projection
Isprs Journal of Photogrammetry and Remote Sensing, 2011Co-Authors: Keith F Blonquist, Robert T PackAbstract:Abstract Bundle adjustment is a method for simultaneously calculating both the interior and exterior orientation parameters of a set of images, and the object-space coordinates of the observed points. In the case of long focal length lenses and narrow field-of-view (FOV) imaging situations, collinearity based (perspective Projection) algorithms may result in linear dependencies between parameters that cause solution instability. The use of a scaled Orthographic Projection model based on linear algebraic formulations was therefore adopted to reduce this risk. Using quaternions, a new mathematical model is derived that includes the partial derivatives as well as the inner constraint equations for a scaled Orthographic bundle adjustment. The model was then tested using two image sets of a single, small vessel (about 6 m length) with a cube target of known dimensions at two distinct ranges; perspective solutions were also calculated for comparison. RMS residual errors of 0.74–0.78 pixels associated with the new method compare favorably to a residual error range of 0.59–0.74 pixels using a perspective bundle adjustment of the same target points. Relative precisions (as a ratio of target size) of between 1:1650 and 1:750 have been achieved at ranges of 375 m and 850 m, respectively, given comparisons with the known cube dimensions. A third image dataset consisting of a network of 16 images was solved with a 1:2200 relative precision showing the new method can successfully handle high redundancy. For the experiments that were conducted, the new method was found to produce less precise results than the perspective bundle solution for a FOV of 0.50–0.65° where the object fills 5–8% of the image. However, it was found to match the precision of the perspective model (with an uncalibrated camera) for a FOV of 0.20–0.30° where the object of interest fills only 1–2% of the full image.
Narendra Ahuja - One of the best experts on this subject based on the ideXlab platform.
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mirror uncertainty and uniqueness conditions for determining shape and motion from Orthographic Projection
International Journal of Computer Vision, 1994Co-Authors: Narendra AhujaAbstract:This paper presents new forms of necessary and sufficient conditions for determining shape and motion to within a mirror uncertainty from monocular Orthographic Projections of any number of point trajectories over any number of views. The new forms of conditions use image data only and can therefore be employed in any practical algorithms for shape and motion estimation. We prove that the mirror uncertainty for the three view problem also exists for a long sequence: if shapeS is a solution, so is its mirror imageS′ which is symmetric toS about the image plane. The necessary and sufficient conditions for determining the two sets of solutions are associated with the rank of the measurement matrixW.
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motion estimation under Orthographic Projection
International Conference on Robotics and Automation, 1991Co-Authors: Narendra AhujaAbstract:Some new results for the problem of motion estimation under Orthographic Projection are presented. Some basic results obtained by previous researchers are refined and more detailed and precise results are provided. It is shown that, in the two-view problem, when the rotation is around the optical axis, the motion (but not the structure) is uniquely determined. In the three-view problem, only under certain conditions are the motion and structure uniquely determined. For any motion problem, if two-view matching cannot determine the motion, only under certain conditions can three-view or multiview matching help. >
Han Wang - One of the best experts on this subject based on the ideXlab platform.
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computational approaches for solving the bas relief ambiguity under Orthographic Projection
Pattern Recognition Letters, 1995Co-Authors: Han WangAbstract:Abstract Bas-relief ambiguity exists in point correspondences of two views. We present two algorithms that compute the rotation angle from two and three views. In the two view case with known rotation axis, a statistically unbiased least square approach is derived to compute the rotation angle uniquely. In three views, a numerical method is used to recover the rotation axis based on the assumption of equal rotations.
