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Unsupervised Stereo Matching Using Confidential Correspondence Consistency

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Unsupervised Learning of Long-Term Motion Dynamics for Videos

Alexandre Massoud Alahi

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learni ...

2017

Using Deep Learning for Image-Based Plant Disease Detection

Marcel Salathé, Sharada Prasanna Mohanty

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances ...

Frontiers Media Sa2016

Learning to Match Aerial Images with Deep Attentive Architectures

Pascal Fua, Eduard Trulls Fortuny, Hani Altwaijry

Image matching is a fundamental problem in Computer Vision. In the context of feature-based matching, SIFT and its variants have long excelled in a wide array of applications. However, for ultra-wide baselines, as in the case of aerial images captured unde ...

2016

A Deeper Look at Dataset Bias

Barbara Caputo, Tatiana Tommasi, Novi Patricia

The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset. At the same time, with the rapid deve ...

Springer Int Publishing Ag2015

Learning Separable Filters

Pascal Fua, Vincent Lepetit, Bugra Tekin, Roberto Rigamonti, Amos Sironi

Learning filters to produce sparse image representations in terms of overcomplete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usually both numerous and non-separable, mak ...

Ieee Computer Soc2015

Non-Linear Domain Adaptation with Boosting

Pascal Fua, Carlos Joaquin Becker, Christos Marios Christoudias

A common assumption in machine vision is that the training and test samples are drawn from the same distribution. However, there are many problems when this assumption is grossly violated, as in bio-medical applications where different acquisitions can gen ...

2013

Compressed look-up-table based real-time rectification hardware

Alexandre Schmid, Yusuf Leblebici, Abdulkadir Akin, Ipek Baz, Luis Manuel Gaemperle

Stereo image rectification is a pre-processing step of disparity estimation intended to remove image distortions and to enable stereo matching along an epipolar line. A real-time disparity estimation system needs to perform real-time rectification which re ...

2013

Deep Learning via Semi-Supervised Embedding

Ronan Collobert

We show how nonlinear embedding algorithms popular for use with "shallow" semi-supervised learning techniques such as kernel methods can be easily applied to deep multi-layer architectures, either as a regularizer at the output layer, or on each layer of t ...

Springer2012

Max-pooling convolutional neural networks for vision-based hand gesture recognition

Automatic recognition of gestures using computer vision is important for many real-world applications such as sign language recognition and human-robot interaction (HRI). Our goal is a real-time hand gesture-based HRI interface for mobile robots. We use a ...

2011

FlowBoost - Appearance Learning from Sparsely Annotated Video

François Fleuret, Karim Ali, David Hasler

We propose a new learning method which exploits temporal consistency to successfully learn a complex appearance model from a sparsely labeled training video. Our approach consists in iteratively improving an appearance based model built with a Boosting pro ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011