Search results for "ECoG"
showing 10 items of 3774 documents
Towards interpretable classifiers with blind signal separation
2012
Blind signal separation (BSS) is a powerful tool to open-up complex signals into component sources that are often interpretable. However, BSS methods are generally unsupervised, therefore the assignment of class membership from the elements of the mixing matrix may be sub-optimal. This paper proposes a three-stage approach using Fisher information metric to define a natural metric for the data, from which a Euclidean approximation can then be used to drive BSS. Results with synthetic data models of real-world high-dimensional data show that the classification accuracy of the method is good for challenging problems, while retaining interpretability.
PD recognition by means of statistical and fractal parameters and a neural network
2000
A novel partial discharge (PD) defect identification method is described. Starting with PD data on different families of specimens, a suitable set of parameters are determined and then used as input variables to a neural network for the purpose of identifying the defects within the insulation. In this procedure the statistical Weibull analysis is performed on PD pulse amplitude histograms to obtain the scale parameter /spl alpha/ and the shape parameter /spl beta/. Thereafter, the two statistical operators (skewness and kurtosis) and two fractal parameters (fractal dimension and lacunarity) are evaluated from the PD phase on the discharge epoch histogram and from the 3 dimensional (pulse am…
Unsupervised clustering method for pattern recognition in IIF images
2017
Autoimmune diseases are a family of more than 80 chronic, and often disabling, illnesses that develop when underlying defects in the immune system lead the body to attack its own organs, tissues, and cells. Diagnosis of autoimmune pathologies is based on research and identification of antinuclear antibodies (ANA) through indirect immunofluorescence (IIF) method and is performed by analyzing patterns and fluorescence intensity. We propose here a method to automatically classify the centromere pattern based on the grouping of centromeres on the cells through a clustering K-means algorithm. The described method was tested on a public database (MIVIA). The results of the test showed an Accuracy…
An algorithm for earthquakes clustering based on maximum likelihood
2007
In this paper we propose a clustering technique set up to separate and find out the two main components of seismicity: the background seismicity and the triggered one. We suppose that a seismic catalogue is the realization of a non homogeneous space-time Poisson clustered process, with a different parametrization for the intensity function of the Poisson-type component and of the clustered (triggered) component. The method here proposed assigns each earthquake to the cluster of earthquakes, or to the set of independent events, according to the increment to the likelihood function, computed using the conditional intensity function estimated by maximum likelihood methods and iteratively chang…
Combining similarity measures in content-based image retrieval
2008
The purpose of content based image retrieval (CBIR) systems is to allow users to retrieve pictures from large image repositories. In a CBIR system, an image is usually represented as a set of low level descriptors from which a series of underlying similarity or distance functions are used to conveniently drive the different types of queries. Recent work deals with combination of distances or scores from different and usually independent representations in an attempt to induce high level semantics from the low level descriptors of the images. Choosing the best method to combine these results requires a careful analysis and, in most cases, the use of ad-hoc strategies. Combination based on or…
Semi-Supervised Remote Sensing Image Classification based on Clustering and the Mean Map Kernel
2008
This paper presents a semi-supervised classifier based on the combination of the expectation-maximization (EM) algorithm for Gaussian mixture models (GMM) and the mean map kernel. The proposed method uses the most reliable samples in terms of maximum likelihood to compute a kernel function that accurately reflects the similarity between clusters in the kernel space. The proposed method improves classification accuracy in situations where the available labeled information does not properly describe the classes in the test image.
Optical flow estimation from multichannel spherical image decomposition
2011
The problem of optical flow estimation is largely discussed in computer vision domain for perspective images. It was also proven that, in terms of optical flow analysis from these images, we have difficulty distinguishing between some motion fields obtained with little camera motion. The omnidirectional cameras provided images with large filed of view. These images contain global information about motion and allow to remove the ambiguity present in perspective case. Nevertheless, these images contain significant radial distortions that is necessary to take into account when treating these images to estimate the motion. In this paper, we shall describe new way to compute efficient optical fl…
Estimation of Electrical Pathways Finding Minimal Cost Paths from Electro-Anatomical Mapping of the Left Ventricle
2014
The electrical activation of the heart is a complex physiological process that is essential for the understanding of several cardiac dysfunctions, such as ventricular tachycardia VT. Nowadays, electro-anatomical mappings of patient-specific activation times on the left ventricle surface can be estimated, providing crucial information to the clinicians for guiding cardiac treatment. However, some electrical pathways of particular interest such as Purkinje or still viable conduction channels are difficult to interpret in these maps. We present here a novel method to find some of these electrical pathways using minimal cost paths computations on surface maps. Experiments to validate the propos…
An unsupervised region growing method for 3D image segmentation
1995
The paper deals with 3D shape decomposition problem, objects are modelled as finite unions of almost-convex primitives. A new region growing method is proposed to extract meaningful objects parts. Parts are individuated by performing a set-partitioning of surface dominating points. The partition step returns labelled seeds from which to start a region growing procedure that propagate labels onto object surface patches. A fuzzy concept of λ-convexity is introduced to test noised real images. Experimental results are given.
A robust approach to ERP denoising
2010
The purpose of presented study is to explore possibilities to increase the robustness and improve the performance of the spatial ERP denoising methods proposed in earlier research. The quality of the subspace separation solution may easily be degraded essentially, if the underlying assumptions become noticeably violated, which is a normal situation in practice. The distortions to the results of a separation are caused by non-zero sample signal-noise and noise-noise correlations, which are indistinguishable from the variances of the signal and noise in the framework of the second-order statistical information exploited by the discussed methods. Therefore, in the research reported in this art…