T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local, a#nely invariant regions. In British Machine Vision Conference BMVC'2000.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....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 ....

....[6] Forming a complete bipartite graph on the two sets of DRs and searching for a globally consistent subset of correspondences is clearly out of question for computational reasons. Recently, a whole class of stereo matching and object recognition algorithms with common structure has emerged [23, 34, 1, 35, 4, 28, 18, 11]. These methods exploit local invariant descriptors to limit the number of tentative correspondences. Important design decisions at this stage include: 1. the choice of measurement regions, i.e. the parts of the image on which invariants are computed, 2. the method of selecting tentative ....

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Tinne Tuytelaars and Luc Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In Proc. 11th British Machine Vision Conference, 2000.

....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 ....

Tinne Tuytelaars and Luc Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In Proc. 11th British Machine Vision Conference, 2000. 10

....is solved in hypothesis verification framework. The unifying view, or perhaps the theory , does not lead to any revolutionary conclusions. In fact, approaches very similar the one presented in the paper have been reported in the literature, most notable the work of Tuytelaars and Van Gool[3], Tell and Carlsson [4] and Matas and Buriinek [5] However, by generalizing from these approaches and providing a model of their structure, we gained a valuable insight. Firstly, this makes analysis of the algorithms easier, highlighting what particular choices were made at what point and what is ....

Tuytelaars, T., Van Gool, L.: Wide Baseline Stereo Matching based on Local, Afflnely Invariant Regions. Proc. BMVC 2000, Bristol, UK (2000)

....group so called area based stereo. This division is more or less historical and does not reflect the modern view well. First of all, a pixel is also a feature. Second, there has been various fea7 tures proposed in recent work on wide baseline stereoscopic matching that have interesting properties [44, 68]. The features need not to have any typical geometric interpretation, they may be thought of as image primitives that possess the property of detection stability and have some (required) invariant properties. The historical development in matching elements we try to describe now. Marr Poggio ....

Tinne Tuytelaars and Luc Van Gool. Wide baseline stereo matching based on local, a#nely invariant regions. In Majid Mirmehdi and Barry Thomas, editors, Proceedings British Machine Vision Conference, volume 2, pages 412--425, University of Bristol, September 2000.

....gure ground separation) over a range of viewing and illumination conditions. In other words, the DR detection must be repeatable and stable w.r.t. viewpoint and illumintion changes. DRs are referred to in the literature as interest points [6] features [1] or invariant regions [15]. Note that we do not require DRs to have some transformation invariant property that is unique in the image. If a DR possessed such a property, nding its corresponding DR in an other image would be greatly simpli ed. To increase the likelihood of this happening, DRs can be equipped with a ....

....part of an object in both views and MRs are de ned in a transformation invariant manner they are quasi viewpoint invariant. Besides the simplest and most common case where the MR is the DR itself, a MR may be constructed for example as a convex hull of a DR, a tted ellipse (anelly invariant, [15]) a line segment between a pair of interest points [14] or any region de ned in a DR derived coordinates. Of course, invariant measurements from a single or even multiple MRs associated with a DR will not guarantee a unique match on e.g. repetitive patterns. However, often DR characterisation by ....

T. Tuytelaars and L. van Gool. Wide baseline stereo matching based on local, anely invariant regions. In Proc. of British Machine Vision Conference, pages 412-422, 2000.

....MRs associated with a DR will not guarantee a unique match on e.g. repetitive patterns. But often, the DR characterization by invariants computed on MRs might be unique or almost unique. The distinguished regions are referred to in the literature as interest points [15] or invariant regions [52]. The separation of the concepts of DR and MRs is important and not made explicit in the literature. For instance, Tuytelaars and van Gool wrote [52] Invariant regions are image patches that automatically deform with changing viewpoint as to keep on covering identical physical part of a scene. ....

....MRs might be unique or almost unique. The distinguished regions are referred to in the literature as interest points [15] or invariant regions [52] The separation of the concepts of DR and MRs is important and not made explicit in the literature. For instance, Tuytelaars and van Gool wrote [52]: Invariant regions are image patches that automatically deform with changing viewpoint as to keep on covering identical physical part of a scene. Such regions are then described by a set of invariant features. Definition 3 A Discriminative Region is any subset of an image defined by ....

T. Tuytelaars and L. van Gool. Wide baseline stereo matching based on local, a#nely invariant regions. In Proc. of British Machine Vision Conference, pages 412--422, 2000.

....scale space. Mikolajczyk and Schmid [11] use a multi scale framework to detect points and then apply scale selection [8] to select characteristic points. These points are invariant to scale changes and allow matching and recognition in the presence of large scale factors. Tuytelaars and Van Gool [16] detect a#ne invariant regions based on image intensities. However, the number of such regions in an image is limited and depends on the content. They use colour descriptors computed for these regions for wide baseline matching. Wide baseline matching and recognition. The methods presented in the ....

....is a significant perspective transformation between the two images. A second example is presented in figure 6a. The images show a 3D scene taken from significantly di#erent viewpoints. This image pair presents a more significant change in viewpoint than the images in figure 7c which were used in [13, 16] as an example for matching. In the figure 6b, we show a pair of images for which our matching procedure fails. The failure is not due to our detector, as the manually selected corresponding points show. It is caused by our descriptors which are not su#ciently distinctive. Note that the corners of ....

T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local, a#nely invariant regions. In The Eleventh British Machine Vision Conference, University of Bristol, UK, pages 412--425, 2000.

....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 ....

T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local, afflnely invariant regions. In BMVC, 2000.

....The difficulty here is that between different shots of the same 3D scene, camera viewpoints and image scales may differ widely. This is illustrated in figure 1. For such cases a plethora of so called wide baseline methods have been developed, and this is still an area of active research [1, 7 9, 11 15, 17, 19, 20]. Here we demonstrate that 3D scene based shot matching can be achieved by applying wide baseline techniques to key frames. A film is partitioned into shots using standard methods (colour histograms and motion compensated cross correlation [5] Invariant descriptors are computed for individual ....

T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In Proc. BMVC., pages 412--425, 2000.

....with affine geometric and photometric invariance, using multiple scales in the interest point detection. Descriptors with affine invariance have also been developed for point pairs [21] and for regions by Tuytelaars and Van Gool based on corners and edges [23] or local intensity extrema [24]. In all the above cases it is necessary to compute the descriptor over the same surface region the region is determined from a single image, but it must be determined independent of viewpoint. The imaged region must therefore adapt its shape with viewpoint, and this is a large part of the ....

....cut framework could thus be extended to include this information. Some progress in viewpoint invariant segmentation has already been demonstrated by [1, 6] The inter image application of section 3 adds another semi local affine invariant descriptor to the raft of those already available [2, 7, 21, 23, 24] for wide baseline matching. The texture descriptor is especially suited in its sole use to scenes which contain multiple textures. Of course, for unknown scene types, all available descriptors should be applied and used in concorde. Acknowledgements We are grateful to RobotVis INRIA ....

T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In Proc. BMVC., pages 412--425, 2000.

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T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local, a#nely invariant regions. In British Machine Vision Conference BMVC'2000.

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Tuytelaars, T. and Gool, L. V., 2000. Wide baseline stereo matching based on local, affinely invariant regions. In: British Machine Vision Conference BMVC'2000.

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T. Tuytelaars and L. Van Gool. Wide Baseline Stereo Matching based on Local, A#nely Invariant Regions. In Proceedings of the 11th British Machine Vision Conference, pages 412--422, Bristol, UK, 2000.

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T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In British Machine Vision Conference BMVC'2000.

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T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In Proc. 11th British Machine Vision Conference, 2000.

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T. Tuytelaars and L.J. Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In British Machine Vision Conference, pages 412--425, 2000.

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T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In Proc. BMVC., pages 412--425, 2000.

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T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In Proc. BMVC., pages 412--425, 2000.

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T. Tuytelaars and L. Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. BMVC, pages 412--425, 2000.

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T. Tuytelaars and L. VanGool. Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions. In British Machine Vision Conference, pages 412--422, 2000.

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Tinne Tuytelaars and Luc Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In In Proceedings of British Machine Vision Conference, pages 412--422, 2000.

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Tinne Tuytelaars and Luc Van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In Proc. 11th British Machine Vision Conference, 2000. 10

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T. Tuytelaars and L. van Gool. Wide baseline stereo matching based on local, affinely invariant regions. In Proc. of British Machine Vision Conference, pages 412--422, 2000.

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