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Featured researches published by Shai Bagon.


international conference on computer vision | 2009

Super-resolution from a single image

Daniel Glasner; Shai Bagon; Michal Irani

Methods for super-resolution can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misalignments), and (ii) Example-Based super-resolution (learning correspondence between low and high resolution image patches from a database). In this paper we propose a unified framework for combining these two families of methods. We further show how this combined approach can be applied to obtain super resolution from as little as a single image (with no database or prior examples). Our approach is based on the observation that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Recurrence of patches within the same image scale (at subpixel misalignments) gives rise to the classical super-resolution, whereas recurrence of patches across different scales of the same image gives rise to example-based super-resolution. Our approach attempts to recover at each pixel its best possible resolution increase based on its patch redundancy within and across scales.


international conference on computer vision | 2011

Decision tree fields

Sebastian Nowozin; Carsten Rother; Shai Bagon; Toby Sharp; Bangpeng Yao; Pushmeet Kohli

This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fields (CRF) which have been widely used in computer vision. In a typical CRF model the unary potentials are derived from sophisticated random forest or boosting based classifiers, however, the pairwise potentials are assumed to (1) have a simple parametric form with a pre-specified and fixed dependence on the image data, and (2) to be defined on the basis of a small and fixed neighborhood. In contrast, in DTF, local interactions between multiple variables are determined by means of decision trees evaluated on the image data, allowing the interactions to be adapted to the image content. This results in powerful graphical models which are able to represent complex label structure. Our key technical contribution is to show that the DTF model can be trained efficiently and jointly using a convex approximate likelihood function, enabling us to learn over a million free model parameters. We show experimentally that for applications which have a rich and complex label structure, our model achieves excellent results.


computer vision and pattern recognition | 2010

Detecting and sketching the common

Shai Bagon; Ori Brostovski; Meirav Galun; Michal Irani

Given very few images containing a common object of interest under severe variations in appearance, we detect the common object and provide a compact visual representation of that object, depicted by a binary sketch. Our algorithm is composed of two stages: (i) Detect a mutually common (yet non-trivial) ensemble of ‘self-similarity descriptors’ shared by all the input images. (ii) Having found such a mutually common ensemble, ‘invert’ it to generate a compact sketch which best represents this ensemble. This provides a simple and compact visual representation of the common object, while eliminating the background clutter of the query images. It can be obtained from very few query images. Such clean sketches may be useful for detection, retrieval, recognition, co-segmentation, and for artistic graphical purposes.


Archive | 2013

Decision Tree Fields: An Efficient Non-parametric Random Field Model for Image Labeling

Sebastian Nowozin; Carsten Rother; Shai Bagon; Toby Sharp; Bangpeng Yao; Pushmeet Kohli

This chapter introduces a new random field model for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes decision forests and conditional random fields (CRF) which have been widely used in computer vision.


european conference on computer vision | 2008

What Is a Good Image Segment? A Unified Approach to Segment Extraction

Shai Bagon; Oren Boiman; Michal Irani


arXiv: Computer Vision and Pattern Recognition | 2011

Large Scale Correlation Clustering Optimization

Shai Bagon; Meirav Galun


Archive | 2010

Super-resolution from a single signal

Michal Irani; Daniel Glasner; Oded Shahar; Shai Bagon


arXiv: Computer Vision and Pattern Recognition | 2012

A Multiscale Framework for Challenging Discrete Optimization

Shai Bagon; Meirav Galun


international conference on information science and applications | 2012

Boundary Driven Interactive Segmentation

Shai Bagon


arXiv: Computer Vision and Pattern Recognition | 2012

A Unified Multiscale Framework for Discrete Energy Minimization

Shai Bagon; Meirav Galun

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Meirav Galun

Weizmann Institute of Science

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Michal Irani

Weizmann Institute of Science

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Daniel Glasner

Weizmann Institute of Science

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Carsten Rother

Dresden University of Technology

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Oded Shahar

Weizmann Institute of Science

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Oren Boiman

Weizmann Institute of Science

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