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Efficient Sample Mining for Object Detection

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Detection of mini-UAVs in the presence of strong topographic relief - a multi-sensor perspective

Yusuf Leblebici

Based on the steadily growing use of mini-UAVs for numerous civilian and military applications, mini-UAVs have been recognized as an increasing potential threat. Therefore, counter-UAV solutions addressing the peculiarities of this class of UAVs have recen ...

Spie-Int Soc Optical Engineering2016

Dictionary learning for fast classification based on soft-thresholding

Pascal Frossard, Alhussein Fawzi

Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits t ...

Springer2015

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

Sample Distillation for Object Detection and Image Classification

François Fleuret, Olivier Canévet, Leonidas Lefakis

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

Adjustment of combination weights over adaptive diffusion networks

Ali H. Sayed

We show how the convergence time of an adaptive network can be estimated in a distributed manner by the agents. Using this procedure, we propose a distributed mechanism for the nodes to switch from using fixed doubly-stochastic combination weights to adapt ...

IEEE2014

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

Learning to Detect Objects with Minimal Supervision

Karim Ali

Many classes of objects can now be successfully detected with statistical machine learning techniques. Faces, cars and pedestrians, have all been detected with low error rates by learning their appearance in a highly generic manner from extensive training ...

EPFL2012

A Real-Time Deformable Detector

Pascal Fua, François Fleuret, Karim Ali, David Hasler

We propose a new learning strategy for object detection. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform base ...

Institute of Electrical and Electronics Engineers2012

Comparing machines and humans on a visual categorization test

François Fleuret, Charles Dubout, Ting Li

Automated scene interpretation has benefited from advances in machine learning, and restricted tasks, such as face detection, have been solved with sufficient accuracy for restricted settings. However, the performance of machines in providing rich semantic ...

2011

Tasting Families of Features for Image Classification

François Fleuret, Charles Dubout

Using multiple families of image features is a very efficient strategy to improve performance in object detection or recognition. However, such a strategy induces multiple challenges for machine learning methods, both from a computational and a statistical ...

2011