F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In 8th International Conference of Computer Vision, pages 636--643, Vancouver, Canada, July 2001. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
The Correspondence Problem in Perspective Images - Chum (2003) (Correct)
....a significant proportion of outliers. The RANSAC algorithm introduced by Fishler and Bolles in 1981 [5] is possibly the most widely used robust estimator in the field of computer vision. RANSAC has been applied in the context of short baseline stereo [30, 33] wide baseline stereo matching [23, 35, 25, 15], motion segmentation [30] mosaicing [17] detection of geometric primitives [3] robust eigenimage matching [10] and elsewhere. Overview of the algorithm is given in Section 2. In Section 3 we show that under a broad range of conditions, RANSAC efficiency is significantly improved if its ....
....method of selecting tentative correspondences given the invariant description and 3. the choice of invariants. Typically, distinguished regions or their scaled version serve as measurement 26 regions and tentative correspondences are established by comparing invariants using Mahalanobis distance [25, 35, 26]. As a second novelty of the presented approach, a robust similarity measure for establishing tentative correspondences is proposed to replace the Mahalanobis distance. The robustness of the proposed similarity measure allows us to use invariants from a collection of measurement regions, even some ....
[Article contains additional citation context not shown here]
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proc. 8th ICCV, Vancouver, Canada, July 2001. 39
Randomized RANSAC - Ond (2002) (1 citation) (Correct)
....a significant proportion of outliers. The RANSAC algorithm introduced by Fishler and Bolles in 1981 [2] is possibly the most widely used robust estimator in the field of computer vision. RANSAC has been applied in the context of short baseline stereo [11, 13] wide baseline stereo matching [8, 14, 10], motion segmentation [11] mosaicing [6] detection of geometric primitives [1] robust eigenimage matching [4] and elsewhere. The structure of the RANSAC algorithm is simple but powerful. Repeatedly, subsets are randomly selected from the input data and model parameters fitting the sample are ....
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proc. 8th International Conference on Computer Vision, Vancouver, Canada, July 2001.
Center For - Machine Perception Czech (2001) (Correct)
....A. In Section 5 details of a novel matching algorithm (from the above mentioned class) are given. A new robust approach is used for tentative correspondence computation. A robust similarity measure for comparison of local invariants replaces the common method based on Mahalanobis distance [14, 20, 15] which can be justified theoretically only under conditions that are almost certainly not met in the wide baseline matching problem [5] The robustness of proposed similarity measure allows us to use invariants from a collection of measurement regions, even some that are much larger than the ....
....regions. The potential for combination of multiple types of distinguished regions is demonstrated on perhaps the most difficult pair from the VALBONNE set. The last experiment can be viewed as a benchmark; results on the VALBONNE set has been presented in a number of papers on the topic [14, 12]. Presented experiments are summarised and the contributions of the paper are reviewed in Section 7. 2 Correspondence from Distinguished Regions In the introduction, the concept of a distinguished region (DR) was described rather vaguely. In this section, we first present a formal definition of ....
[Article contains additional citation context not shown here]
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Eighth Int. Conference on Computer Vision (Vancouver, Canada), 2001. 21
Randomized RANSAC with T d,d test - Chum Matas Center (Correct)
....a significant proportion of outliers. The RANSAC algorithm introduced by Fishler and Bolles in 1981 [2] is possibly the most widely used robust estimator in the field of computer vision. RANSAC has been applied in the context of short baseline stereo [11, 13] wide baseline stereo matching [8, 14, 10], motion segmentation [11] mosaicing [6] detection of geometric primitives [1] robust eigenimage matching [4] and elsewhere. The structure of the RANSAC algorithm is simple but powerful. Repeatedly, subsets are randomly selected from the input data and model parameters fitting the sample are ....
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proc. 8th International Conference on Computer Vision, Vancouver, Canada, July 2001.
Rotational Invariants for Wide-baseline Stereo - Matas, Bílek, Chum (2002) (Correct)
....Invariant Descriptors. The most simple situation arises if a local affine frame is defined on the DR. Photometrically normalised pixel values from a normalised patch characterise the DR invariantly. More commonly, only a point or a point and a scale factor are known, and rotation invariants [9, 8] or affine invariants [15, 2] must be used. Tentative Correspondences. At this stage, we have a set of DRs for each image and a potentially large number of invariant descriptors associated with each DR. Selecting mutually nearest pairs in Mahalanobis distance is the most common method [8, 15, 9] ....
....[9, 8] or affine invariants [15, 2] must be used. Tentative Correspondences. At this stage, we have a set of DRs for each image and a potentially large number of invariant descriptors associated with each DR. Selecting mutually nearest pairs in Mahalanobis distance is the most common method [8, 15, 9]. Note that the objective of this stage is not to keep the maximum possible number of good correspondences, but rather to maximise the fraction of good correspondences. The fraction determines the speed of epipolar geometry estimation by the RANSAC procedure [13] Epipolar Geometry estimation is ....
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Eighth Int. Conference on Computer Vision (Vancouver, Canada), 2001.
Rotational Invariants for Wide-baseline Stereo - Matas, Bílek, Chum (2002) (Correct)
....Research and Grant Agency of the Czech Republic GACR 102 01 0971. invariant way. The fully affine invariant regions were introduced recently, exploiting local texture characteristics [1] or local configuration of multiple image edges or interest points [5, 16] Schaffalitzky and Zisserman [7] presented a method for automatic determination of local neighborhood shape, but only for image areas where stationary texture occurs. In this paper, we rely on the so called Maximum Stable Extremal Regions and Separated Elementary Cycles of the Edge Graph introduced in [4] which were shown to ....
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proc. 8th International Conference on Computer Vision, Vancouver, Canada, July 2001.
Sparse Texture Representation Using Affine-Invariant.. - Lazebnik, Schmid, Ponce (2003) (3 citations) (Correct)
....invariant with respect to 2D similarity and affine transformations, much less to 3D transformations caused by movement of the camera and non rigid deformations of the textured surface. However, invariance to such transformations is desirable for many applications, including wide baseline matching [12, 16, 19], texture based retrieval [15, 18] segmentation of natural scenes [9] and recognition of materials [20] In this paper, we set out to design a texture representation that is invariant to any geometric transformations that can be locally approximated by an affine model. Local affine invariants ....
....of the image. c) Salient regions found by methods of Section 2.1. d) Texture elements obtained by clustering the regions from (c) Methods for affine invariant texture analysis have not yet been studied widely in the literature. One approach has been proposed by Schaffalitzky and Zisserman [16] for the application of wide baseline matching. This approach relies on second moment statistics of homogeneous texture regions and uses the affine adaptation process [6] which also forms an important component of our approach. However, the method of [16] relies on a non invariant segmentation ....
[Article contains additional citation context not shown here]
F. Schaffalitzky and A. Zisserman, "Viewpoint Invariant Texture Matching and Wide Baseline Stereo", Proc. ICCV, 2001.
Correspondence-free Synchronization and - Lior Wolf And (2002) (Correct)
....Many traditional algorithms for reconstructing a 3D scene from two or more cameras require the establishment of correspondences between the images. This becomes challenging in some cases, for example when the cameras have different zoom factors, or large vergence (wide baseline stereo) [12, 9]. Using a moving video camera rather than a set of static cameras helps in overcoming some of the correspondence problems, but may decrease the stability and accuracy of the reconstruction. Moreover, the reconstruction from a moving camera becomes harder if not impossible when the scene is not ....
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In ICCV, Vancouver, BC, pages II: 636-643, 2001.
Viewpoint Invariant Descriptors For Image Matching - Andrew Zisserman And (2001) Self-citation (Schaffalitzky Zisserman) (Correct)
No context found.
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proc. ICCV, Jul 2001.
Automated Scene Matching in Movies - Schaffalitzky, Zisserman (2002) (1 citation) Self-citation (Schaffalitzky Zisserman) (Correct)
No context found.
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proc. ICCV, Jul 2001.
Classifying Images of Materials: Achieving Viewpoint and.. - Varma, Zisserman (2002) (8 citations) Self-citation (Zisserman) (Correct)
....only an intuitive, and not a sound mathematical, understanding. Therefore, classifying materials by their textural appearance in single images photographed under unknown viewing and illumination conditions is still quite an outstanding problem, though significant progress has been made recently [2, 3, 9, 12, 13, 17, 20]. In particular, Leung and Malik [13] made an important innovation in giving an operational definition of a texton. They defined a 2D texton as a cluster centre in filter response space. This not only enabled textons to be generated automatically from an image, but also opened up the possibility ....
....according to increasing viewing angle and this is represented on the X axis. The Y axis is the 2 distance between the model image and the given image. The pose normalized images consistently have a reduced 2 distance which translates into better classification. 3. 2 Pose normalization In [17] it was demonstrated that, provided a texture has sufficient directional variation, it can be pose normalized by maximizing weak isotropy of the second moment gradient matrix (a method originally suggested in [14] The method is applicable in the absence of solid texture effects. Here we ....
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proc. ICCV, Jul 2001.
Fusing Points and Lines for High Performance Tracking - Edward Rosten And (2005) (1 citation) (Correct)
No context found.
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In 8th International Conference of Computer Vision, pages 636--643, Vancouver, Canada, July 2001.
Robust and Fully Automated Image Registration Using.. - Bauer, Bischof.. (Correct)
No context found.
Schaffalitzky, F. and Zisserman, A., 2001. Viewpoint invariant texture matching and wide baseline stereo. In: Proc. 8th International Conference on Computer Vision, Vancouver, Canada.
Discriminative Techniques for the Recognition of Complex-Shaped .. - Carmichael (2003) (Correct)
No context found.
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proceedings International Conference On Computer Vision, pages 636--643, July 2001.
Joint Orientation of Epipoles - Chum, Werner, Pajdla (Correct)
No context found.
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proc. 8th ICCV, Vancouver, Canada, July 2001.
Affine-Invariant Local Descriptors and Neighborhood.. - Lazebnik, Schmid, Ponce (2003) (5 citations) (Correct)
No context found.
F. Schaffalitzky and A. Zisserman, "Viewpoint Invariant Texture Matching and Wide Baseline Stereo", Proc. ICCV, 2001, vol. 2, pp. 636-643.
Toward True 3D Object Recognition - Ponce, Lazebnik, Rothganger, Schmid (Correct)
No context found.
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proc. Int. Conf. Comp. Vision, Vancouver, Canada, 2001.
Velocity-Adaptation of Spatio-Temporal Receptive Fields for.. - Laptev, Lindeberg (2002) (Correct)
No context found.
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. Proc. ICCV'01, 636--643, 2001.
Center For - Machine Perception Czech (2001) (Correct)
No context found.
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proc. 8th International Conference on Computer Vision, Vancouver, Canada, July 2001.
Affine-Invariant Local Descriptors and Neighborhood.. - Lazebnik, Schmid, Ponce (2003) (5 citations) (Correct)
No context found.
F. Schaffalitzky and A. Zisserman, "Viewpoint Invariant Texture Matching and Wide Baseline Stereo", Proc. ICCV, 2001.
Local Affine Frames for Wide-Baseline Stereo - Matas, Obdrzalek, Chum (2002) (1 citation) (Correct)
No context found.
F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching and wide baseline stereo. In Proc. 8th International Conference on Computer Vision, Vancouver, Canada, July 2001.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC