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Displaying 1-13 out of 13 total
Fast directional chamfer matching
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Ming-Yu Liu, Oncel Tuzel, Ashok Veeraraghavan, Rama Chellappa
Issue Date:June 2010
pp. 1696-1703
We study the object localization problem in images given a single hand-drawn example or a gallery of shapes as the object model. Although many shape matching algorithms have been proposed for the problem over the decades, chamfer matching remains to be the...
 
Specular surface reconstruction from sparse reflection correspondences
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Aswin C. Sankaranarayanan, Ashok Veeraraghavan, Oncel Tuzel, Amit Agrawal
Issue Date:June 2010
pp. 1245-1252
We present a practical approach for surface reconstruction of smooth mirror-like objects using sparse reflection correspondences (RCs). Assuming finite object motion with a fixed camera and un-calibrated environment, we derive the relationship between RC a...
 
Learning on lie groups for invariant detection and tracking
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Oncel Tuzel, Fatih Porikli, Peter Meer
Issue Date:June 2008
pp. 1-8
This paper presents a novel learning based tracking model combined with object detection. The existing techniques proceed by linearizing the motion, which makes an implicit Euclidean space assumption. Most of the transformations used in computer vision hav...
 
Pedestrian Detection via Classification on Riemannian Manifolds
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Oncel Tuzel, Fatih Porikli, Peter Meer
Issue Date:October 2008
pp. 1713-1727
We present a new algorithm to detect pedestrian in still images utilizing covariance matrices as object descriptors. Since the descriptors do not form a vector space, well known machine learning techniques are not well suited to learn the classifiers. The ...
 
Human Detection via Classification on Riemannian Manifolds
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Oncel Tuzel, Fatih Porikli, Peter Meer
Issue Date:June 2007
pp. 1-8
We present a new algorithm to detect humans in still images utilizing covariance matrices as object descriptors. Since these descriptors do not lie on a vector space, well known machine learning techniques are not adequate to learn the classifiers. The spa...
 
Covariance Tracking using Model Update Based on Lie Algebra
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Fatih Porikli, Oncel Tuzel, Peter Meer
Issue Date:June 2006
pp. 728-735
We propose a simple and elegant algorithm to track nonrigid objects using a covariance based object description and a Lie algebra based update mechanism. We represent an object window as the covariance matrix of features, therefore we manage to capture the...
 
Simultaneous Multiple 3D Motion Estimation via Mode Finding on Lie Groups
Found in: Computer Vision, IEEE International Conference on
By Oncel Tuzel, Raghav Subbarao, Peter Meer
Issue Date:October 2005
pp. 18-25
We propose a new method to estimate multiple rigid motions from noisy 3D point correspondences in the presence of outliers. The method does not require prior specification of number of motion groups and estimates all the motion parameters simultaneously. W...
 
A Bayesian Approach to Background Modeling
Found in: Computer Vision and Pattern Recognition Workshop
By Oncel Tuzel, Fatih Porikli, Peter Meer
Issue Date:June 2005
pp. 58
<p>Learning background statistics is an essential task for several visual surveillance applications such as incident detection and traf.c management. In this paper, we propose a new method for modeling background statistics of a dynamic scene. Each p...
 
Vision-Based Single-Stroke Character Recognition for Wearable Computing
Found in: IEEE Intelligent Systems
By Ömer Faruk Özer, Oguz Özün, C. Öncel Tüzel, Volkan Atalay, A. Enis Çetin
Issue Date:May 2001
pp. 33-37
The authors describe a new approach for data entry in wearable computing applications. A head-mounted digital camera records characters drawn by hand gestures or by a pointer on the forearm of the user. Similar to the Graffiti alphabet, the user draws each...
 
Detecting 3D geometric boundaries of indoor scenes under varying lighting
Found in: 2014 IEEE Winter Conference on Applications of Computer Vision (WACV)
By Jie Ni,Tim K. Marks,Oncel Tuzel,Fatih Porikli
Issue Date:March 2014
pp. 1-8
The goal of this research is to identify 3D geometric boundaries in a set of 2D photographs of a static indoor scene under unknown, changing lighting conditions. A 3D geometric boundary is a contour located at a 3D depth discontinuity or a discontinuity in...
   
Entropy-Rate Clustering: Cluster Analysis via Maximizing a Submodular Function Subject to a Matroid Constraint
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Ming-Yu Liu,Oncel Tuzel,Srikumar Ramalingam,Rama Chellappa
Issue Date:January 2014
pp. 99-112
We propose a new objective function for clustering. This objective function consists of two components: the entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the bala...
 
Semi-Supervised Kernel Mean Shift Clustering
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Saket Anand,Sushil Mittal,Oncel Tuzel,Peter Meer
Issue Date:November 2013
pp. 1
Mean shift clustering is a powerful nonparametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. However, being completely unsupervised, its performance suffers when the original ...
 
Joint Geodesic Upsampling of Depth Images
Found in: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Ming-Yu Liu,Oncel Tuzel,Yuichi Taguchi
Issue Date:June 2013
pp. 169-176
We propose an algorithm utilizing geodesic distances to up sample a low resolution depth image using a registered high resolution color image. Specifically, it computes depth for each pixel in the high resolution image using geodesic paths to the pixels wh...
 
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