Multiple human tracking in RGB-depth data: A survey

© The Institution of Engineering and Technology. Multiple human tracking (MHT) is a fundamental task in many computer vision applications. Appearance-based approaches, primarily formulated on RGB data, are constrained and affected by problems arising from occlusions and/or illumination variations. In recent years, the arrival of cheap RGB-depth devices has led to many new approaches to MHT, and many of these integrate colour and depth cues to improve each and every stage of the process. In this survey, the authors present the common processing pipeline of these methods and review their methodology based (a) on how they implement this pipeline and (b) on what role depth plays within each stage of it. They identify and introduce existing, publicly available, benchmark datasets and software resources that fuse colour and depth data for MHT. Finally, they present a brief comparative evaluation of the performance of those works that have applied their methods to these datasets

Manifold Modeling of the Beating Heart Motion

Modeling the heart motion has important applications for diagnosis and intervention. We present a new method for modeling the deformation of the myocardium in the cardiac cycle. Our approach is based on manifold learning to build a representation of shape variation across time. We experiment with various manifold types to identify the best manifold method, and with real patient data extracted from cine MRIs. We obtain a representation, common to all subjects, that can discriminate cardiac cycle phases and heart function types

Topology of molecular machines of the endoplasmic reticulum: a compilation of proteomics and cytological data

The endoplasmic reticulum (ER) is a key organelle of the secretion pathway involved in the synthesis of both proteins and lipids destined for multiple sites within and without the cell. The ER functions to both co- and post-translationally modify newly synthesized proteins and lipids and sort them for housekeeping within the ER and for transport to their sites of function away from the ER. In addition, the ER is involved in the metabolism and degradation of specific xenobiotics and endogenous biosynthetic products. A variety of proteomics studies have been reported on different subcompartments of the ER providing an ER protein dictionary with new data being made available on many protein complexes of relevance to the biology of the ER including the ribosome, the translocon, coatomer proteins, cytoskeletal proteins, folding proteins, the antigen-processing machinery, signaling proteins and proteins involved in membrane traffic. This review examines proteomics and cytological data in support of the presence of specific molecular machines at specific sites or subcompartments of the ER

Genre classification using chords and stochastic language models

Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naiumlve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different leve! ls of the genre hierarchy for the techniques employed are presented and discussed.This work is supported by the Spanish CICyT PROSEMUS project (TIN2006-14932-C02), the research programme Consolider Ingenio 2010 (MIPRCV, CSD2007-00018) and the Pascal Network of Excellence