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Threshold Selection for Unsupervised Detection, with an Application to Microphone Arrays

Publications associées (33)

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Efﬁcient Feature Embeddings for Student Classiﬁcation with Variational Auto-encoders

Gathering labeled data in educational data mining (EDM) is a time and cost intensive task. However, the amount of available training data directly influences the quality of predictive models. Unlabeled data, on the other hand, is readily available in high ...

EDM2017

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

Combining Unsupervised Feature Learning and Riesz Wavelets for Histopathology Image Representation: Application to Identifying Anaplastic Medulloblastoma

Adrien Raphaël Depeursinge

Medulloblastoma (MB) is a type of brain cancer that represent roughly 25% of all brain tumors in children. In the anaplastic medulloblastoma subtype, it is important to identify the degree of irregularity and lack of organizations of cells as this correlat ...

Springer Int Publishing Ag2015

Springer2015

The main challenge of new information technologies is to retrieve intelligible information from the large volume of digital data gathered every day. Among the variety of existing data sources, the satellites continuously observing the surface of the Earth ...

EPFL2014

Unsupervised adaptation for acceleration-based activity recognition: Robustness to sensor displacement and rotation

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Hamidreza Bayati

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors affect the data distribution between two different experimental sessions. One of these factors is the pot ...

Springer Verlag2013

Unsupervised change detection with kernels

Devis Tuia

In this letter, an unsupervised kernel-based approach to change detection is introduced. Nonlinear clustering is utilized to partition in two a selected subset of pixels representing both changed and unchanged areas. Once the optimal clustering is obtained ...

Ieee-Inst Electrical Electronics Engineers Inc2012

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

A comparison of different automated methods for the detection of white matter lesions in MRI data

Ahmed Abdulkadir

White matter hyperintensities (WMH) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMH labour intensive and prone to subjective bias. T ...

Elsevier2011

Adaptive Strategy for the Statistical Analysis of Connectomes

Jean-Philippe Thiran, Stephan Morgenthaler, Reto Meuli, Patric Hagmann, Djalel Eddine Meskaldji, Leila Cammoun

We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by conside ...

Public Library of Science2011