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Introduction to Programming in Java

Related publications (223)

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Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

Sciences et Techniques de l’Ingénieur (STI)2017

Vision-based detection of aircrafts and UAVs

Artem Rozantsev

Unmanned Aerial Vehicles are becoming increasingly popular for a broad variety of tasks ranging from aerial imagery to objects delivery. With the expansion of the areas, where drones can be efficiently used, the collision risk with other flying objects inc ...

EPFL2017

Learning Aerial Image Segmentation from Online Maps

Martin Jaggi, Jan Dirk Wegner, Aurélien Lucchi, Thomas Hofmann

This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) ...

Institute of Electrical and Electronics Engineers2017

Graph-based Isometry Invariant Representation Learning

Pascal Frossard, Renata Khasanova

Learning transformation invariant representations of visual data is an important problem in computer vision. Deep convolutional networks have demonstrated remarkable results for image and video classification tasks. However, they have achieved only limited ...

2017

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

Pierre Vandergheynst, Xavier Bresson, Michaël Defferrard, Youngjoo Seo

This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary grap ...

2017

Convolutional Neural Networks for Inverse Problems in Imaging—A Review

Michaël Unser, Kyong Hwan Jin, Michael Thompson McCann

In this article, we review recent uses of convolutional neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding performance on object clas ...

IEEE2017

Template-based Monocular 3-D Shape Reconstruction And Tracking Using Laplacian Meshes

Tien Dat Ngo

This thesis addresses the problem of recovering the 3-D shape of a deformable object in single images, or image sequences acquired by a monocular video camera, given that a 3-D template shape and a template image of the object are available. While being a ...

EPFL2016

Word Sequence Modeling using Deep Learning

Joël Yvon Roland Legrand

For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...

EPFL2016

Towards End-to-End Speech Recognition

Dimitri Palaz

Standard automatic speech recognition (ASR) systems follow a divide and conquer approach to convert speech into text. Alternately, the end goal is achieved by a combination of sub-tasks, namely, feature extraction, acoustic modeling and sequence decoding, ...

EPFL2016

Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Touradj Ebrahimi, Lin Yuan, Ashutosh Singla

Recent past has seen a lot of developments in the field of image-based dietary assessment. Food image classification and recognition are crucial steps for dietary assessment. In the last couple of years, advancements in the deep learning and convolutional ...

Assoc Computing Machinery2016