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Target Detection with Deep Learning in Polarimetric Imaging

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A MultiPath Network for Object Detection

Pedro Henrique Oliveira Pinheiro

The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical images, and requires more precise localization. To address these challenges, we ...

BMVA Press2016

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

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

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

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

A hierarchical detection framework for computational contact mechanics

Jean-François Molinari, Alejandro Marcos Aragon

A novel methodology consisting of three hierarchical levels is proposed for the detection phase of contact mechanics simulations. The top level of the hierarchy uses kinematic information from the objects involved in the simulation to determine approximate ...

Elsevier2014

Models of Reward-Modulated Spike-Timing-Dependent Plasticity

Nicolas Frémaux

How do animals learn to repeat behaviors that lead to the obtention of food or other “rewarding” objects? As a biologically plausible paradigm for learning in spiking neural networks, spike-timing dependent plasticity (STDP) has been shown to perform well ...

EPFL2013

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

Removing Shadows from Images Using Color and Near-infrared

Sabine Süsstrunk, Neda Salamati, Arthur Germain

Shadows often introduce errors in the performance of computer vision algorithms, such as object detection and tracking. This paper proposes a method to remove shadows from real images based on a probability shadow map. The probability shadow map identifies ...

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