An application of ARX stochastic models to iris recognition

We present a new approach for iris recognition based on stochastic autoregressive models with exogenous input (ARX). Iris recognition is a method to identify persons, based on the analysis of the eye iris. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. The main contribution in this work is given in the step of characterization of iris features by using ARX models. In our work every iris in database is represented by an ARX model learned from data. In the comparison and matching step, data taken from iris sample are substituted into every ARX model and residuals are generated. A decision of accept or reject is taken based on residuals and on a threshold calculated experimentally. We conduct experiments with two different databases. Under certain conditions, we found a rate of successful identifications in the order of 99.7 % for one database and 100 % for the other.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

An application of ARX stochastic models to iris recognition

We present a new approach for iris recognition based on stochastic autoregressive models with exogenous input (ARX). Iris recognition is a method to identify persons, based on the analysis of the eye iris. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. The main contribution in this work is given in the step of characterization of iris features by using ARX models. In our work every iris in database is represented by an ARX model learned from data. In the comparison and matching step, data taken from iris sample are substituted into every ARX model and residuals are generated. A decision of accept or reject is taken based on residuals and on a threshold calculated experimentally. We conduct experiments with two different databases. Under certain conditions, we found a rate of successful identifications in the order of 99.7 % for one database and 100 % for the other.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

Kalman Filter Bibliography: Agriculture, Biology, and Medicine

27 pages, 1 article*Kalman Filter Bibliography: Agriculture, Biology, and Medicine* (Federer, Walter T.; Murty, B. R.) 27 page

Modeling small objects under uncertainties : novel algorithms and applications.

Active Shape Models (ASM), Active Appearance Models (AAM) and Active Tensor Models (ATM) are common approaches to model elastic (deformable) objects. These models require an ensemble of shapes and textures, annotated by human experts, in order identify the model order and parameters. A candidate object may be represented by a weighted sum of basis generated by an optimization process. These methods have been very effective for modeling deformable objects in biomedical imaging, biometrics, computer vision and graphics. They have been tried mainly on objects with known features that are amenable to manual (expert) annotation. They have not been examined on objects with severe ambiguities to be uniquely characterized by experts. This dissertation presents a unified approach for modeling, detecting, segmenting and categorizing small objects under uncertainty, with focus on lung nodules that may appear in low dose CT (LDCT) scans of the human chest. The AAM, ASM and the ATM approaches are used for the first time on this application. A new formulation to object detection by template matching, as an energy optimization, is introduced. Nine similarity measures of matching have been quantitatively evaluated for detecting nodules less than 1 em in diameter. Statistical methods that combine intensity, shape and spatial interaction are examined for segmentation of small size objects. Extensions of the intensity model using the linear combination of Gaussians (LCG) approach are introduced, in order to estimate the number of modes in the LCG equation. The classical maximum a posteriori (MAP) segmentation approach has been adapted to handle segmentation of small size lung nodules that are randomly located in the lung tissue. A novel empirical approach has been devised to simultaneously detect and segment the lung nodules in LDCT scans. The level sets methods approach was also applied for lung nodule segmentation. A new formulation for the energy function controlling the level set propagation has been introduced taking into account the specific properties of the nodules. Finally, a novel approach for classification of the segmented nodules into categories has been introduced. Geometric object descriptors such as the SIFT, AS 1FT, SURF and LBP have been used for feature extraction and matching of small size lung nodules; the LBP has been found to be the most robust. Categorization implies classification of detected and segmented objects into classes or types. The object descriptors have been deployed in the detection step for false positive reduction, and in the categorization stage to assign a class and type for the nodules. The AAMI ASMI A TM models have been used for the categorization stage. The front-end processes of lung nodule modeling, detection, segmentation and classification/categorization are model-based and data-driven. This dissertation is the first attempt in the literature at creating an entirely model-based approach for lung nodule analysis

Analyse d'images Terahertz

Cette thèse à publications présente toutes nos contributions qui se rapportent à la segmentation d’images Térahertz. La thèse comprend quatre chapitres. Les deux premiers chapitres introduisent deux nouvelles approches de segmentation basées sur des techniques d'échantillonnage. Dans la première approche, nous formulons la technique de classification K-means dans le cadre de l'échantillon d'ensembles ordonnés pour surmonter le problème d'initialisation des centres. Le deuxième chapitre aborde la sélection des données à travers la pondération de caractéristiques et l'échantillonnage aléatoire simple pour la classification des pixels en vue d'une segmentation des images Térahertz. Une estimation automatique de la taille de l'échantillon aléatoire et du nombre de caractéristiques sélectionnées sont également proposés. Les deux chapitres suivants introduisent une autre famille de techniques de classification des séries chronologiques basées sur la régression et qui sont adaptées aux séries chronologiques. Nous supposons que les valeurs associées à chaque pixel d'une image Térahertz sont échantillonnées à partir d'un modèle autorégressif. La segmentation de l'image est alors vue comme un problème de classification de séries chronologiques. Ainsi, dans le troisième chapitre, la classification est formulée comme un problème d'optimisation non-linéaire. L'ordre du modèle et le nombre de classes sont estimés en utilisant un critère généralisé d'information. Finalement, le quatrième chapitre est une généralisation des résultats obtenus dans le troisième chapitre. Au lieu de considérer un problème de moindres carrés, nous proposons une approche de classification probabiliste basée sur le mélange de modèles autorégressifs. Les paramètres de l'approche proposée sont automatiquement estimés en utilisant un critère généralisé d'information

Convolutional Neural Network for Seismic Phase Classification, Performance Demonstration over a Local Seismic Network

Over the past two decades, the amount of available seismic data has increased significantly, fueling the need for automatic processing to use the vast amount of information contained in such data sets. Detecting seismicity in temporary aftershock networks is one important example that has become a huge challenge because of the high seismicity rate and dense station coverage. Additionally, the need for highly accurate earthquake locations to distinguish between different competing physical processes during the postseismic period demands even more accurate arrival‐time estimates of seismic phase. Here, we present a convolutional neural network (CNN) for classifying seismic phase onsets for local seismic networks. The CNN is trained on a small dataset for deep‐learning purposes (411 events) detected throughout northern Chile, typical for a temporary aftershock network. In the absence of extensive training data, we demonstrate that a CNN‐based automatic phase picker can still improve performance in classifying seismic phases, which matches or exceeds that of historic methods. The trained network is tested against an optimized short‐term average/long‐term average (STA/LTA) based method (Rietbrock et al., 2012) in classifying phase onsets for a separate dataset of 3878 events throughout the same region. Based on station travel‐time residuals, the CNN outperforms the STA/LTA approach and achieves location residual distribution close to the ones obtained by manual inspection