Oncel Tuzel

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Region Covariance: A Fast Descriptor for Detection and Classification

Oncel Tuzel, Fatih Murat Porikli, Peter Meer
ECCV
2006

We describe a new region descriptor and apply it to two problems, object detection and texture classification. The covariance of d-features, e.g., the three-dimensional color vector, the norm of… (More)

Entropy rate superpixel segmentation

Ming-Yu Liu, Oncel Tuzel, Srikumar Ramalingam, Rama Chellappa
CVPR
2011

We propose a new objective function for superpixel segmentation. This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term. The entropy rate… (More)

Pedestrian Detection via Classification on Riemannian Manifolds

Oncel Tuzel, Fatih Murat Porikli, Peter Meer
IEEE Transactions on Pattern Analysis and Machine…
2008

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… (More)

Covariance Tracking using Model Update Based on Lie Algebra

Fatih Murat Porikli, Oncel Tuzel, Peter Meer
IEEE Computer Society Conference on Computer…
2006

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… (More)

Learning from Simulated and Unsupervised Images through Adversarial Training

Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, Russell Y. Webb
IEEE Conference on Computer Vision and Pattern…
2017

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images… (More)

Coupled Generative Adversarial Networks

We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of corresponding… (More)

Human Detection via Classification on Riemannian Manifolds

Oncel Tuzel, Fatih Murat Porikli, Peter Meer
IEEE Conference on Computer Vision and Pattern…
2007

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… (More)

Fast directional chamfer matching

Ming-Yu Liu, Oncel Tuzel, Ashok Veeraraghavan, Rama Chellappa
IEEE Computer Society Conference on Computer…
2010

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… (More)

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

Yin Zhou, Oncel Tuzel
IEEE/CVF Conference on Computer Vision and…
2018

Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality. To interface a highly… (More)

Joint Geodesic Upsampling of Depth Images

Ming-Yu Liu, Oncel Tuzel, Yuichi Taguchi
IEEE Conference on Computer Vision and Pattern…
2013

We propose an algorithm utilizing geodesic distances to upsample a low resolution depth image using a registered high resolution color image. Specifically, it computes depth for each pixel in the… (More)

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