We propose a method for removing non-uniform motion blur from multiple blurry images. Traditional methods focus on estimating a single motion blur kernel for the entire image. In ...
In this paper we present a novel boosting algorithm for supervised learning that incorporates invariance to data transformations and has high generalization capabilities. While on...
We consider the task of creating a 3-d model of a large novel environment, given only a small number of images of the scene. This is a difficult problem, because if the images are...
In this paper, we introduce a new image descriptor for broad Image Categorization, the Progressive Randomization (PR), that uses perturbations on the values of the Least Significa...
Online camera recalibration is necessary for long-term deployment of computer vision systems. Existing algorithms assume that the source of recalibration information is a set of f...
Andrew W. Fitzgibbon, Duncan P. Robertson, Antonio...
We present a fast and accurate framework for registration of multi-modal volumetric images based on decoupled estimation of registration parameters utilizing spatial information i...
Parastoo Sadeghi, Ramtin Shams, Richard I. Hartley...
We present a practical, stratified autocalibration algorithm with theoretical guarantees of global optimality. Given a projective reconstruction, the first stage of the algorithm ...
Manual labeling of objects in videos is a tedious task. We present an approach which automatically propagates the labels from a single frame to the next ones. We tackle the challe...
Julien Fauqueur, Gabriel J. Brostow, Roberto Cipol...
This paper presents a fast, accurate, and novel method for the problem of estimating the number of humans and their positions from background differenced images obtained from a si...
Lan Dong, Vasu Parameswaran, Visvanathan Ramesh, I...