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Graph-Based Classification of Omnidirectional Images

Publications associées (35)

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Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Bugra Tekin

Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growin ...

EPFL2018

Spectral MAB for Unknown Graph Processes

Pascal Frossard, Laura Toni

In this work, we study graph-based multi-arms bandit (MAB) problems aimed at optimizing actions on irregular and high-dimensional graphs. More formally, we consider a decision-maker that takes sequential actions over time and observes the experienced rewar ...

IEEE Computer Society2018

Robust and Efficient Data Clustering with Signal Processing on Graphs

Lionel Jérémie Martin

Data is pervasive in today's world and has actually been for quite some time. With the increasing volume of data to process, there is a need for faster and at least as accurate techniques than what we already have. In particular, the last decade recorded t ...

EPFL2018

Everything You Always Wanted to Know about Multicore Graph Processing but Were Afraid to Ask

Willy Zwaenepoel, Baptiste Joseph Eustache Lepers, Jasmina Malicevic

Graph processing systems are used in a wide variety of fields, ranging from biology to social networks, and a large number of such systems have been described in the recent literature. We perform a systematic comparison of various techniques proposed to sp ...

USENIX Association2017

Graph-based structures in data science

Nathanaël Perraudin

State-of-the-art data analysis tools have to deal with high-dimensional data. Fortunately, the inherent dimensionality of data is often much smaller, as it has an internal structure limiting its degrees of freedom. In most cases, this structure can be appr ...

EPFL2017

Learning Laplacian Matrix in Smooth Graph Signal Representations

Pascal Frossard, Pierre Vandergheynst, Xiaowen Dong

The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing. However, a meaningful graph is not alwa ...

Institute of Electrical and Electronics Engineers2016

Graph Signal Processing

Ntorina Thanou

Over the past few decades we have been experiencing an explosion of information generated by large networks of sensors and other data sources. Much of this data is intrinsically structured, such as traffic evolution in a transportation network, temperature ...

EPFL2016

Graph-Based vs Depth-Based Data Representation for Multiview Images

Pascal Frossard, Thomas Maugey

In this paper, we propose a representation and coding method for multiview images. As an alternative to depth-based schemes, we propose a representation that captures the geometry and the dependencies between pixels in different views in the form of connec ...

2013

Graph-based representation and coding of multiview geometry

Pascal Frossard, Thomas Maugey

We propose a new approach for describing the geometry information of multiview image representations. Rather than transmitting the raw geometry of the scene, under the form of depth information, we build a graph that represents the connections between corr ...

2013

THE NUMBER OF EDGES IN k-QUASI-PLANAR GRAPHS

János Pach

A graph drawn in the plane is called k-quasi-planar if it does not contain k pair-wise crossing edges. It has been conjectured for a long time that for every fixed k, the maximum number of edges of a k-quasi-planar graph with n vertices is O(n). The best k ...

Siam Publications2013