12 research outputs found
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
Unsupervised delineation of the vessel tree in retinal fundus images
Retinal imaging has gained particular popularity as it provides an opportunity to diagnose various
medical pathologies in a non-invasive way. One of the basic and very important steps in the analysis of such
images is the delineation of the vessel tree from the background. Such segmentation facilitates the investigation
of the morphological characteristics of the vessel tree and the analysis of any lesions in the background, which
are both indicators for various pathologies. We propose a novel method called B-COSFIRE for the delineation
of the vessel tree. It is based on the classic COSFIRE approach, which is a trainable nonlinear filtering method.
The responses of a B-COSFIRE filter is achieved by combining the responses of difference-of-Gaussians filters
whose areas of support are determined in an automatic configuration step. We configure two types of
B-COSFIRE filters, one that responds selectively along vessels and another that is selective to vessel endings.
The segmentation of the vessel tree is achieved by summing up the response maps of both types of filters followed
by thresholding.We demonstrate high effectiveness of the proposed approach by performing experiments
on four public data sets, namely DRIVE, STARE, CHASE DB1 and HRF. The delineation approach that we
propose also has lower time complexity than existing methods.peer-reviewe
BCI Signal Classification using a Riemannian-based kernel
Abstract. The use of spatial covariance matrix as feature is investigated for motor imagery EEG-based classification. A new kernel is derived by establishing a connection with the Riemannian geometry of symmetric positive definite matrices. Different kernels are tested, in combination with support vector machines, on a past BCI competition dataset. We demonstrate that this new approach outperforms significantly state of the art results without the need for spatial filtering.
Security protocols for iot access networks
Nowadays, we are immersed in a digital world with a huge number of sensors, and devices, connected following a great variety of typologies. Internet Protocol (IP) v6 and the standardization of the novel Internet of Things (IoT) protocols enable new services and applications. Moreover, the heterogeneity of IP and non-IP devices requires novel security techniques, allowing non-IP devices to connect over a short range with a mediator gateway, and then forming a capillary access network. Providing security and privacy is hard in the conventional Internet, and is even more challenging in the IoT because of global connectivity and heterogeneous and resource-constrained devices. In this chapter, we present the background on security algorithms for both uni- and bidirectional terminals, in the context of IoT scenarios. We review the current security and privacy solutions in the IoT, and discuss research challenges for novel IoT security and privacy solutions. Particularly, we deal with security algorithms based on a local key renewal, performed considering only the local clock time. Finally, conclusive remarks and future trends are outlined at the end of the chapter
Spectral Analysis of physiological parameters for emotion detection
This paper intends to be a literature review in the field of emotions detection using spectral analysis of neurological signals. It also shows the great boom in Brain Computer Interfaces (BCI) applications. Explains the research methodology used for this type of projects, and finally it highlights the results of several studies that have been done in this are