Publications related to Semantic coding by supervised dimensionality reduction

Multiple Hypothesis Semantic Mapping for Robust Data Association

In this letter, we present a semantic mapping approach with multiple hypothesis tracking for data association. As semantic information has the potential to overcome ambiguity in measurements and place recognition, it forms an eminent modality for autonomou ...

2019

Comic Digitization through the Extraction of Semantic Content and Style Analysis

Comic book digitization would play a pivotal role in exploring new avenues on how digital comics can be consumed. As of present, the systems capable of doing such a task are limited in capability to achieve complete digitization. This task of digitization ...

IEEE2019

Variational Methods for Human Modeling

Timur Bagautdinov

A large part of computer vision research is devoted to building models and algorithms aimed at understanding human appearance and behaviour from images and videos. Ultimately, we want to build automated systems that are at least as capable as people when i ...

EPFL2018

Towards a supervised classification of neocortical interneuron morphologies

Sean Lewis Hill, Ruth Benavides Piccione, Bojan Mihaljevic

BackgroundThe challenge of classifying cortical interneurons is yet to be solved. Data-driven classification into established morphological types may provide insight and practical value.ResultsWe trained models using 217 high-quality morphologies of rat so ...

BMC2018

A Fourier dimensionality reduction model for big data interferometric imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Vijay Kartik

Data dimensionality reduction in radio interferometry can provide savings of computational resources for image reconstruction through reduced memory footprints and lighter computations per iteration, which is important for the scalability of imaging method ...

Oxford Univ Press2017

Face Recognition in Real-world Images

Sabine Süsstrunk, Radhakrishna Achanta, Xavier Philippe Marie Fontaine

Face recognition systems are designed to handle well-aligned images captured under controlled situations. However real-world images present varying orientations, expressions, and illumination conditions. Traditional face recognition algorithms perform poor ...

Ieee2017

Fast Robust PCA on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Nauman Shahid, Vasilis Kalofolias

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

PCA using graph total variation

Pierre Vandergheynst, Nathanaël Perraudin, Benjamin Ricaud, Nauman Shahid, Vasilis Kalofolias

Mining useful clusters from high dimensional data has received sig- nificant attention of the signal processing and machine learning com- munity in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome th ...

Ieee2016

Learning from sparse codes

Pascal Frossard, Sofia Karygianni

In this paper we address the problem of learning image structures directly from sparse codes. We first model images as linear combinations of molecules, which are themselves groups of atoms from a redundant dictionary. We then formulate a new structure lea ...

Ieee2016

Estimation Error of the Constrained Lasso

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

2016

Semantic coding by supervised dimensionality reduction

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Multiple Hypothesis Semantic Mapping for Robust Data Association

Comic Digitization through the Extraction of Semantic Content and Style Analysis

Variational Methods for Human Modeling

Towards a supervised classification of neocortical interneuron morphologies

A Fourier dimensionality reduction model for big data interferometric imaging

Face Recognition in Real-world Images

Fast Robust PCA on Graphs

PCA using graph total variation

Learning from sparse codes

Estimation Error of the Constrained Lasso

Variational Methods for Human Modeling

PCA using graph total variation

Face Recognition in Real-world Images

A Fourier dimensionality reduction model for big data interferometric imaging

Fast Robust PCA on Graphs

Learning from sparse codes

Multiple Hypothesis Semantic Mapping for Robust Data Association

Towards a supervised classification of neocortical interneuron morphologies

Comic Digitization through the Extraction of Semantic Content and Style Analysis

Estimation Error of the Constrained Lasso