Search results for "Pattern recognition"
showing 10 items of 2301 documents
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…
Hidden Markov Random Field model and BFGS algorithm for Brain Image Segmentation
2016
Brain MR images segmentation has attracted a particular focus in medical imaging. The automatic image analysis and interpretation became a necessity. Segmentation is one of the key operations to provide a crucial decision support to physicians. Its goal is to simplify the representation of an image into items meaningful and easier to analyze. Hidden Markov Random Fields (HMRF) provide an elegant way to model the segmentation problem. This model leads to the minimization problem of a function. BFGS (Broyden-Fletcher-Goldfarb-Shanno algorithm) is one of the most powerful methods to solve unconstrained optimization problem. This paper presents how we combine HMRF and BFGS to achieve a good seg…
Non-negative matrix factorization Vs. FastICA on mismatch negativity of children
2009
In this presentation two event-related potentials, mismatch negativity (MMN) and P3a, are extracted from EEG by non-negative matrix factorization (NMF) simultaneously. Typically MMN recordings show a mixture of MMN, P3a, and responses to repeated standard stimuli. NMF may release the source independence assumption and data length limitations required by Fast independent component analysis (FastICA). Thus, in theory NMF could reach better separation of the responses. In the current experiment MMN was elicited by auditory duration deviations in 102 children. NMF was performed on the time-frequency representation of the raw data to estimate sources. Support to Absence Ratio (SAR) of the MMN co…
Visual Cortex Performs a Sort of Non-linear ICA
2010
Here, the standard V1 cortex model optimized to reproduce image distortion psychophysics is shown to have nice statistical properties, e.g. approximate factorization of the PDF of natural images. These results confirm the efficient encoding hypothesis that aims to explain the organization of biological sensors by information theory arguments.
A statistical model for magnitudes and angles of wavelet frame coefficients and its application to texture retrieval
2014
Abstract This paper presents a texture descriptor based on wavelet frame transforms. At each position in the image, and for each resolution level, we consider both vertical and horizontal wavelet detail coefficients as the components of a bivariate random vector. The magnitudes and angles of these vectors are computed. At each level the empirical histogram of magnitudes is modeled by a Generalized Gamma distribution, and the empirical histogram of angles is modeled by a different version of the von Mises distribution that accounts for histograms with 2 modes. Each texture is characterized by few parameters. A new distance is presented (based on the Kullback–Leibler divergence) that allows g…
Editorial for Special Issue “Fine Art Pattern Extraction and Recognition”
2021
Cultural heritage, especially the fine arts, plays an invaluable role in the cultural, historical, and economic growth of our societies [...]
Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnos…
2021
Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment …
Measurement of three-dimensional mirror parameters by polarization imaging applied to catadioptric camera calibration
2008
International audience; We present a new efficient method for calibration of cata- dioptric sensors. The method is based on an accurate measurement of the three-dimensional parameters of the mirror through polariza- tion imaging. While inserting a rotating polarizer between the cam- era and the mirror, the system is automatically calibrated without any calibration patterns. Moreover, this method permits most of the constraints related to the calibration of catadioptric systems to be relaxed. We show that, contrary to our system, the traditional meth- ods of calibration are very sensitive to misalignment of the camera axis and the symmetry axis of the mirror. From the measurement of three-di…