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Displaying 1-49 out of 49 total
Editor's Note
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih,Sing Bing Kang,Neil Lawrence,Jiri Matas,Max Welling
Issue Date:May 2012
pp. 833
No summary available.
 
Editor's Note
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih,Sing Bing Kang,Neil Lawrence,Jiri Matas,Max Welling
Issue Date:February 2012
pp. 209-210
No summary available.
 
Editor's Note
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih,Sing Bing Kang,Jiri Matas,Max Welling
Issue Date:December 2011
pp. 2337-2340
No summary available.
 
Editor's Note
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih,Sing Bing Kang,Jiri Matas,Max Welling
Issue Date:November 2011
pp. 2129-2130
No summary available.
 
Editorial
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih, Zoubin Ghahramani, Sing Bing Kang, Jiri Matas
Issue Date:September 2011
pp. 1697-1698
No summary available.
 
Editor's Note
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih, Zoubin Ghahramani, Sing Bing Kang, Jiri Matas
Issue Date:May 2011
pp. 865-866
No summary available.
 
State of the Journal
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih,Jiri Matas,Zoubin Ghahramani
Issue Date:January 2011
pp. 1-2
No summary available.
 
Editor's Note
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih, Jiri Matas, Zoubin Ghahramani
Issue Date:October 2010
pp. 1729
No summary available.
 
Editor's Note
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih, Jiri Matas, Zoubin Ghahramani
Issue Date:August 2010
pp. 1345-1346
No summary available.
 
Editor's Note
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih, Jiri Matas, Zoubin Ghahramani
Issue Date:May 2010
pp. 769
No summary available.
 
Introduction of New Associate Editors
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih, Jiri Matas, Zoubin Ghahramani
Issue Date:August 2009
pp. 1345-1346
No summary available.
 
Introduction of New Associate Editors
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih, Zoubin Ghahramani, Jiri Matas
Issue Date:June 2009
pp. 961-963
No summary available.
 
Guest Editors' Introduction to the Special Section on CVPR Papers
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Simon Baker, Jiri Matas, Ramin Zabih
Issue Date:October 2008
pp. 1681-1682
No summary available.
 
Long-Term Tracking through Failure Cases
Found in: 2013 IEEE International Conference on Computer Vision Workshops (ICCVW)
By Karel Lebeda,Simon Hadfield,Jiri Matas,Richard Bowden
Issue Date:December 2013
pp. 153-160
Long term tracking of an object, given only a single instance in an initial frame, remains an open problem. We propose a visual tracking algorithm, robust to many of the difficulties which often occur in real-world scenes. Correspondences of edge-based fea...
 
The Visual Object Tracking VOT2013 Challenge Results
Found in: 2013 IEEE International Conference on Computer Vision Workshops (ICCVW)
By Matej Kristan,Roman Pflugfelder,Ale Leonardis,Jiri Matas,Fatih Porikli,Luka Cehovin,Georg Nebehay,Gustavo Fernandez,Toma Vojir,Adam Gatt,Ahmad Khajenezhad,Ahmed Salahledin,Ali Soltani-Farani,Ali Zarezade,Alfredo Petrosino,Anthony Milton,Behzad Bozorgtabar,Bo Li,Chee Seng Chan,Cherkeng Heng,Dale Ward,David Kearney,Dorothy Monekosso,Hakki Can Karaimer,Hamid R. Rabiee,Jianke Zhu,Jin Gao,Jingjing Xiao,Junge Zhang,Junliang Xing,Kaiqi Huang,Karel Lebeda,Lijun Cao,Mario Edoardo Maresca,Mei Kuan Lim,Mohamed El Helw,Michael Felsberg,Paolo Remagnino,Richard Bowden,Roland Goecke,Rustam Stolkin,Samantha Yueying Lim,Sara Maher,Sebastien Poullot,Sebastien Wong,Shin'Ichi Satoh,Weihua Chen,Weiming Hu,Xiaoqin Zhang,Yang Li,Zhiheng Niu
Issue Date:December 2013
pp. 98-111
Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that th...
 
Tracking-Learning-Detection
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Zdenek Kalal,Krystian Mikolajczyk,Jiri Matas
Issue Date:July 2012
pp. 1409-1422
This paper investigates long-term tracking of unknown objects in a video stream. The object is defined by its location and extent in a single frame. In every frame that follows, the task is to determine the object's location and extent or indicate that the...
 
State of the Journal
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ramin Zabih,Sing Bing Kang,Jiri Matas,Max Welling;
Issue Date:January 2012
pp. 1-2
No summary available.
 
Forward-Backward Error: Automatic Detection of Tracking Failures
Found in: Pattern Recognition, International Conference on
By Zdenek Kalal, Krystian Mikolajczyk, Jiri Matas
Issue Date:August 2010
pp. 2756-2759
This paper proposes a novel method for tracking failure detection. The detection is based on the Forward-Backward error, i.e. the tracking is performed forward and backward in time and the discrepancies between these two trajectories are measured. We demon...
 
Image Matching and Retrieval by Repetitive Patterns
Found in: Pattern Recognition, International Conference on
By Petr Doubek, Jiri Matas, Michal Perdoch, Ondrej Chum
Issue Date:August 2010
pp. 3195-3198
Detection of repetitive patterns in images has been studied for a long time in computer vision. This paper discusses a method for representing a lattice or line pattern by shift-invariant descriptor of the repeating element. The descriptor overcomes shift ...
 
Construction of Precise Local Affine Frames
Found in: Pattern Recognition, International Conference on
By Andrej Mikulik, Jiri Matas, Michal Perdoch, Ondrej Chum
Issue Date:August 2010
pp. 3565-3569
We propose a novel method for the refinement of Maximally Stable Extremal Region (MSER) boundaries to sub-pixel precision by taking into account the intensity function in the 2x2 neighborhood of the contour points. The proposed method improves the repeatab...
 
Tracking the invisible: Learning where the object might be
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Helmut Grabner, Jiri Matas, Luc Van Gool, Philippe Cattin
Issue Date:June 2010
pp. 1285-1292
Objects are usually embedded into context. Visual context has been successfully used in object detection tasks, however, it is often ignored in object tracking. We propose a method to learn supporters which are, be it only temporally, useful for determinin...
 
P-N learning: Bootstrapping binary classifiers by structural constraints
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Zdenek Kalal, Jiri Matas, Krystian Mikolajczyk
Issue Date:June 2010
pp. 49-56
This paper shows that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if knowing the label of one example restricts the labeling of the others. We propose a novel...
 
Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Hongping Cai, Krystian Mikolajczyk, Jiri Matas
Issue Date:February 2011
pp. 338-352
In this paper, we present Linear Discriminant Projections (LDP) for reducing dimensionality and improving discriminability of local image descriptors. We place LDP into the context of state-of-the-art discriminant projections and analyze its properties. LD...
 
Improving Descriptors for Fast Tree Matching by Optimal Linear Projection
Found in: Computer Vision, IEEE International Conference on
By Krystian Mikolajczyk, Jiri Matas
Issue Date:October 2007
pp. 1-8
In this paper we propose to transform an image descriptor so that nearest neighbor (NN) search for correspondences becomes the optimal matching strategy under the assumption that inter-image deviations of corresponding descriptors have Gaussian distributio...
 
Stable Affine Frames on Isophotes
Found in: Computer Vision, IEEE International Conference on
By Michal Perdoch, Jiri Matas, Stepan Obdrzalek
Issue Date:October 2007
pp. 1-8
We propose a new affine-covariant feature, the Stable Affine Frame (SAF). SAFs lie on the boundary of extremal regions, i.e. on isophotes. Instead of requiring the whole isophote to be stable with respect to intensity perturbation as in maximally stable ex...
 
Linear Predictors for Fast Simultaneous Modeling and Tracking
Found in: Computer Vision, IEEE International Conference on
By Liam Ellis, Nicholas Dowson, Jiri Matas, Richard Bowden
Issue Date:October 2007
pp. 1-8
An approach for fast tracking of arbitrary image features with no prior model and no offline learning stage is presented. Fast tracking is achieved using banks of linear displacement predictors learnt online. A multi-modal appearance model is also learnt o...
 
Object-Detection with a Varying Number of Eigenspace Projections
Found in: Pattern Recognition, International Conference on
By Michael Reiter, Jiri Matas
Issue Date:August 1998
pp. 759
No summary available.
 
On Combining Classifiers
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Josef Kittler, Mohamad Hatef, Robert P.W. Duin, Jiri Matas
Issue Date:March 1998
pp. 226-239
<p><b>Abstract</b>—We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all ...
 
Scene Text Localization and Recognition with Oriented Stroke Detection
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Luka Neumann,Jiri Matas
Issue Date:December 2013
pp. 97-104
An unconstrained end-to-end text localization and recognition method is presented. The method introduces a novel approach for character detection and recognition which combines the advantages of sliding-window and connected component methods. Characters ar...
 
USAC: A Universal Framework for Random Sample Consensus
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Rahul Raguram,Ondrej Chum,Marc Pollefeys,Jiri Matas,Jan-Michael Frahm
Issue Date:August 2013
pp. 2022-2038
A computational problem that arises frequently in computer vision is that of estimating the parameters of a model from data that have been contaminated by noise and outliers. More generally, any practical system that seeks to estimate quantities from noisy...
 
On Combining Multiple Segmentations in Scene Text Recognition
Found in: 2013 12th International Conference on Document Analysis and Recognition (ICDAR)
By Luka Neumann,Jiri Matas
Issue Date:August 2013
pp. 523-527
An end-to-end real-time scene text localization and recognition method is presented. The three main novel features are: (i) keeping multiple segmentations of each character until the very last stage of the processing when the context of each character in a...
 
Text Localization in Real-World Images Using Efficiently Pruned Exhaustive Search
Found in: Document Analysis and Recognition, International Conference on
By Luk´š Neumann,Jirí Matas
Issue Date:September 2011
pp. 687-691
An efficient method for text localization and recognition in real-world images is proposed. Thanks to effective pruning, it is able to exhaustively search the space of all character sequences in real time (200ms on a 640x480 image). The method exploits hig...
 
Unsupervised discovery of co-occurrence in sparse high dimensional data
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Ondrej Chum, Jiri Matas
Issue Date:June 2010
pp. 3416-3423
An efficient min-Hash based algorithm for discovery of dependencies in sparse high-dimensional data is presented. The dependencies are represented by sets of features co-occurring with high probability and are called co-ocsets. Sparse high dimensional desc...
 
Efficient Sequential Correspondence Selection by Cosegmentation
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Jan Čech, Jiří Matas, Michal Perdoch
Issue Date:September 2010
pp. 1568-1581
In many retrieval, object recognition, and wide-baseline stereo methods, correspondences of interest points (distinguished regions) are commonly established by matching compact descriptors such as SIFTs. We show that a subsequent cosegmentation process cou...
 
Large-Scale Discovery of Spatially Related Images
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ondř;ej Chum, Jiří Matas
Issue Date:February 2010
pp. 371-377
We propose a randomized data mining method that finds clusters of spatially overlapping images. The core of the method relies on the min-Hash algorithm for fast detection of pairs of images with spatial overlap, the so-called cluster seeds. The seeds are t...
 
Dense linear-time correspondences for tracking
Found in: Computer Vision and Pattern Recognition Workshop
By Stepan Obdrzalek, Michal Perd'och, Jiri Matas
Issue Date:June 2008
pp. 1-8
A novel method is proposed for the problem of frame-to-frame correspondence search in video sequences. The method, based on hashing of low-dimensional image descriptors, establishes dense correspondences and allows large motions. All image pixels are consi...
 
Efficient sequential correspondence selection by cosegmentation
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Jan C. Cech, Jiri Matas, Michal Perd'och
Issue Date:June 2008
pp. 1-8
In many retrieval, object recognition and wide baseline stereo methods, correspondences of interest points are established possibly sublinearly by matching a compact descriptor such as SIFT. We show that a subsequent cosegmentation process coupled with a q...
 
Tracking by an Optimal Sequence of Linear Predictors
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Karel Zimmermann, Jiří Matas, Thomáš Svoboda
Issue Date:April 2009
pp. 677-692
We propose a learning approach to tracking explicitly minimizing the computational complexity of the tracking process subject to user-defined probability of failure (loss-of-lock) and precision. The tracker is formed by a Number of Sequences of Learned Lin...
 
Adaptive Parameter Optimization for Real-time Tracking
Found in: Computer Vision, IEEE International Conference on
By Karel Zimmermann, Tomas Svoboda, Jiri Matas
Issue Date:October 2007
pp. 1-8
Adaptation of a tracking procedure combined in a common way with a Kalman filter is formulated as an constrained optimization problem, where a trade-off between precision and loss-of-lock probability is explicitly taken into account. While the tracker is l...
 
Optimal Randomized RANSAC
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ondřej Chum, Jiří Matas
Issue Date:August 2008
pp. 1472-1482
A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-specified probability. The solution is found in time that is (i) close to the shortest possible and (ii) superior...
 
Epipolar Geometry from Two Correspondences
Found in: Pattern Recognition, International Conference on
By Michal Perdoch, Jiri Matas, Ondrej Chum
Issue Date:August 2006
pp. 215-219
<p>A novel algorithm for robust RANSAC-like estimation of epipolar geometry (of uncalibrated camera pair) from two correspondences of local affine frames (LAFs) is presented. Each LAF is constructed from three points independently detected on a maxim...
 
Multiview 3D Tracking with an Incrementally Constructed 3D Model
Found in: 3D Data Processing Visualization and Transmission, International Symposium on
By Karel Zimmermann, Tomas Svoboda, Jiri Matas
Issue Date:June 2006
pp. 488-495
We propose a multiview tracking method for rigid objects. Assuming that a part of the object is visible in at least two cameras, a partial 3D model is reconstructed in terms of a collection of small 3D planar patches of arbitrary topology. The 3D represent...
 
Geometric Hashing with Local Affine Frames
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Ondrej Chum, Jiri Matas
Issue Date:June 2006
pp. 879-884
We propose a novel representation of local image structure and a matching scheme that are insensitive to a wide range of appearance changes. The representation is a collection of local affine frames that are constructed on outer boundaries of maximally sta...
 
Randomized RANSAC with Sequential Probability Ratio Test
Found in: Computer Vision, IEEE International Conference on
By Jiří Matas, Ondřej Chum
Issue Date:October 2005
pp. 1727-1732
A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-controllable probability n. A provably optimal model verification strategy is designed for the situation when the...
 
WaldBoost — Learning for Time Constrained Sequential Detection
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Jan Šochman, Jiří Matas
Issue Date:June 2005
pp. 150-156
In many computer vision classification problems, both the error and time characterizes the quality of a decision. We show that such problems can be formalized in the framework of sequential decision-making. If the false positive and false negative error ra...
 
Inter-Stage Feature Propagation in Cascade Building with AdaBoost
Found in: Pattern Recognition, International Conference on
By Jan Sochman, Jirí Matas
Issue Date:August 2004
pp. 236-239
A modification of the cascaded detector with the Ada-Boost trained stage classifiers is proposed and brought to bear on the face detection problem. The cascaded detector is a sequential classifier with the ability of early rejection of easy samples. Each d...
 
Epipolar Geometry Estimation via RANSAC Benefits from the Oriented Epipolar Constraint
Found in: Pattern Recognition, International Conference on
By Ondrej Chum, Tomás Werner, Jirí Matas
Issue Date:August 2004
pp. 112-115
The efficiency of epipolar geometry estimation by RANSAC is improved by exploiting the oriented epipolar constraint. Performance evaluation shows that the enhancement brings up to a two-fold speed-up. The orientation test is simple to implement, is univers...
 
AdaBoost with Totally Corrective Updates for Fast Face Detection
Found in: Automatic Face and Gesture Recognition, IEEE International Conference on
By Jan Sochman, Jirí Matas
Issue Date:May 2004
pp. 445
An extension of the AdaBoost learning algorithm is proposed and brought to bear on the face detection problem. In each weak classifier selection cycle, the novel totally corrective algorithm reduces aggressively the upper bound on the training error by cor...
 
Local Affine Frames for Wide-Baseline Stereo
Found in: Pattern Recognition, International Conference on
By Jiří Matas, Štěpán Obdržálek, Ondřej Chum
Issue Date:August 2002
pp. 40363
A novel procedure for establishing wide-baseline correspondence is introduced. Tentative correspondences are established by matching photometrically normalised colour measurements represented in a local affine frame. The affine frames are obtained by a num...
 
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