Publication

Unsupervised adaptation to on-body sensor displacement in acceleration-based activity recognition

Publications associées (36)

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search

Sparse Binary Features for Image Classification

Johann Paratte

In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes imp ...

2013

Dynamic Classifier Fusion for Multi-sensory Activity Recognition

Hesam Sagha

Nowadays, many systems rely on fusing different sources of information to recognize human activities and gestures, speech, or brain activities for applications in areas such as clinical practice, and health care and Human Computer Interaction (HCI). Typica ...

EPFL2013

On-line anomaly detection and resilience in classifier ensembles

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

Detection of anomalies is a broad field of study, which is applied in different areas such as data monitoring, navigation, and pattern recognition. In this paper we propose two measures to detect anomalous behaviors in an ensemble of classifiers by monitor ...

Elsevier2013

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

Are Sparse Representations Really Relevant for Image Classification?

Vincent Lepetit, Roberto Rigamonti, Matthew Brown

Recent years have seen an increasing interest in sparse representations for image classiﬁcation and object recognition, probably motivated by evidence from the analysis of the primate visual cortex. It is still unclear, however, whether or not sparsity hel ...

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

Dynamic Quantification of Activity Recognition Capabilities in Opportunistic Systems

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Hesam Sagha

Opportunistic activity and context recognition systems draw from the characteristic to use sensing devices according to a recognition goal at runtime that just happen to be available instead of pre-defining them at the design time of the system. Whenever a ...

2011

Face Detection using Ferns

Sébastien Marcel, Venkatesh Bala Subburaman

This paper discusses the use of ferns (a set of binary features) for face detection. The binary feature used here is the sign of pixel intensity difference. Ferns were first introduced for keypoint recognition and showed good performance, and improving the ...

Idiap2011

Subspaces Indexing Model on Grassmann Manifold for Image Search

Xinchao Wang

Conventional linear subspace learning methods like principal component analysis (PCA), linear discriminant analysis (LDA) derive subspaces from the whole data set. These approaches have limitations in the sense that they are linear while the data distribut ...

Institute of Electrical and Electronics Engineers2011

Learning Pose Invariant and Covariant Classifiers from Image Sequences

Mustafa Özuysal

Object tracking and detection over a wide range of viewpoints is a long-standing problem in Computer Vision. Despite significant advance in wide-baseline sparse interest point matching and development of robust dense feature models, it remains a largely op ...

EPFL2010