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Publications associées (143)

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Learning to Segments Objects Candidates

Ronan Collobert, Pedro Henrique Oliveira Pinheiro

Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and (2) this set of candidate proposals is then passed to an object classifier. Such approaches have been shown they can be f ...

2015

Are All Pixels Equally Important? Towards Multi-Level Salient Object Detection

Gökhan Yildirim

When we look at our environment, we primarily pay attention to visually distinctive objects. We refer to these objects as visually important or salient. Our visual system dedicates most of its processing resources to analyzing these salient objects. An ana ...

EPFL2015

The impact of privacy protection filters on gender recognition

Touradj Ebrahimi

Deep learning-based algorithms have become increasingly efficient in recognition and detection tasks, especially when they are trained on large-scale datasets. Such recent success has led to a speculation that deep learning methods are comparable to or eve ...

2015

Efficient Sample Mining for Object Detection

François Fleuret, Olivier Canévet

We propose a novel approach to efficiently select informative samples for large-scale learning. Instead of directly feeding a learning algorithm with a very large amount of samples, as it is usually done to reach state-of-the-art performance, we have devel ...

2014

Sample Distillation for Object Detection and Image Classification

François Fleuret, Olivier Canévet, Leonidas Lefakis

2014

Receptive Fields Selection for Binary Feature Description

Pascal Fua, Zhiye Wang, Tomasz Piotr Trzcinski, Bin Fan

Feature description for local image patch is widely used in computer vision. While the conventional way to design local descriptor is based on expert experience and knowledge, learning based methods for designing local descriptor become more and more popul ...

Institute of Electrical and Electronics Engineers2014

Robust visual tracking using feature selection

Fabien Willemin

Visual tracking has become a very important component in computer vision, but achieving a robust, reliable and real time tracking remains a real challenge.In order to improve the actual state-of-the-art, we choose to study and improve one of the most perfo ...

2014

Adaptive Sampling for Large Scale Boosting

François Fleuret, Charles Dubout

Classical Boosting algorithms, such as AdaBoost, build a strong classifier without concern for the computational cost. Some applications, in particular in computer vision, may involve millions of training examples and very large feature spaces. In such con ...

2014

Risk Assessment of Atrial Fibrillation: a Failure Prediction Approach

David Atienza Alonso, Giovanni Ansaloni, Ruben Braojos Lopez, Daniel Camilo Rojas Quirós

We present a methodology for identifying patients who have experienced Paroxysmal Atrial Fibrillation (PAF) among a given subject population. Our work is intended as an initial step towards the design of an unobtrusive portable system for concurrent detect ...

Institute of Electrical and Electronics Engineers2014

Object Classification and Detection in High Dimensional Feature Space

Charles Dubout

Object classification and detection aim at recognizing and localizing objects in real-world images. They are fundamental computer vision problems and a prerequisite for full scene understanding. Their difficulty lies in the large number of possible object ...

Programme doctoral en Informatique, Communications et Information2013