Development of Novel Feature For Iris Biometrics

IRIS is one of the supreme biometric trait available, whose accuracy surprises everyone, better then DNA. There are numbered of algorithms proposed for the efficient result but fails due to limitations. All traditional iris recognition systems are not meant especially for iris image. They are being derived from other trades hoping that it will work with iris also. The mainly known feature in iris biometric is J Daugman’s Gabor filter. He has used Wavelet equation an applied Integro-differentiation operator to obtain Gabor filter. In this thesis, we have proposed a new scheme in feature detection, particularly for iris Biometric. We have taken Wavelet as a base equation and apply complex-exponential in the presence of Gaussian envelop. Our approach starts with the efficient sector based normalization and noise removal techniques, in the pre-processing phase of iris biometric. Then Creating feature keypixel from that normalised image. The enhancement of the keypixel is done to increase accuracy rate

Feature Extraction Methods for Character Recognition

Not Include

Feature based dynamic intra-video indexing

A thesis submitted in partial fulfillment for the degree of Doctor of PhilosophyWith the advent of digital imagery and its wide spread application in all vistas of life, it has become an important component in the world of communication. Video content ranging from broadcast news, sports, personal videos, surveillance, movies and entertainment and similar domains is increasing exponentially in quantity and it is becoming a challenge to retrieve content of interest from the corpora. This has led to an increased interest amongst the researchers to investigate concepts of video structure analysis, feature extraction, content annotation, tagging, video indexing, querying and retrieval to fulfil the requirements. However, most of the previous work is confined within specific domain and constrained by the quality, processing and storage capabilities. This thesis presents a novel framework agglomerating the established approaches from feature extraction to browsing in one system of content based video retrieval. The proposed framework significantly fills the gap identified while satisfying the imposed constraints of processing, storage, quality and retrieval times. The output entails a framework, methodology and prototype application to allow the user to efficiently and effectively retrieved content of interest such as age, gender and activity by specifying the relevant query. Experiments have shown plausible results with an average precision and recall of 0.91 and 0.92 respectively for face detection using Haar wavelets based approach. Precision of age ranges from 0.82 to 0.91 and recall from 0.78 to 0.84. The recognition of gender gives better precision with males (0.89) compared to females while recall gives a higher value with females (0.92). Activity of the subject has been detected using Hough transform and classified using Hiddell Markov Model. A comprehensive dataset to support similar studies has also been developed as part of the research process. A Graphical User Interface (GUI) providing a friendly and intuitive interface has been integrated into the developed system to facilitate the retrieval process. The comparison results of the intraclass correlation coefficient (ICC) shows that the performance of the system closely resembles with that of the human annotator. The performance has been optimised for time and error rate

Reconnaissance Biométrique par Fusion Multimodale de Visages

Biometric systems are considered to be one of the most effective methods of protecting and securing private or public life against all types of theft. Facial recognition is one of the most widely used methods, not because it is the most efficient and reliable, but rather because it is natural and non-intrusive and relatively accepted compared to other biometrics such as fingerprint and iris. The goal of developing biometric applications, such as facial recognition, has recently become important in smart cities. Over the past decades, many techniques, the applications of which include videoconferencing systems, facial reconstruction, security, etc. proposed to recognize a face in a 2D or 3D image. Generally, the change in lighting, variations in pose and facial expressions make 2D facial recognition less than reliable. However, 3D models may be able to overcome these constraints, except that most 3D facial recognition methods still treat the human face as a rigid object. This means that these methods are not able to handle facial expressions. In this thesis, we propose a new approach for automatic face verification by encoding the local information of 2D and 3D facial images as a high order tensor. First, the histograms of two local multiscale descriptors (LPQ and BSIF) are used to characterize both 2D and 3D facial images. Next, a tensor-based facial representation is designed to combine all the features extracted from 2D and 3D faces. Moreover, to improve the discrimination of the proposed tensor face representation, we used two multilinear subspace methods (MWPCA and MDA combined with WCCN). In addition, the WCCN technique is applied to face tensors to reduce the effect of intra-class directions using a normalization transform, as well as to improve the discriminating power of MDA. Our experiments were carried out on the three largest databases: FRGC v2.0, Bosphorus and CASIA 3D under different facial expressions, variations in pose and occlusions. The experimental results have shown the superiority of the proposed approach in terms of verification rate compared to the recent state-of-the-art method

A neuro-genetic hybrid approach to automatic identification of plant leaves

Plants are essential for the existence of most living things on this planet. Plants are used for providing food, shelter, and medicine. The ability to identify plants is very important for several applications, including conservation of endangered plant species, rehabilitation of lands after mining activities and differentiating crop plants from weeds. In recent times, many researchers have made attempts to develop automated plant species recognition systems. However, the current computer-based plants recognition systems have limitations as some plants are naturally complex, thus it is difficult to extract and represent their features. Further, natural differences of features within the same plant and similarities between plants of different species cause problems in classification. This thesis developed a novel hybrid intelligent system based on a neuro-genetic model for automatic recognition of plants using leaf image analysis based on novel approach of combining several image descriptors with Cellular Neural Networks (CNN), Genetic Algorithm (GA), and Probabilistic Neural Networks (PNN) to address classification challenges in plant computer-based plant species identification using the images of plant leaves. A GA-based feature selection module was developed to select the best of these leaf features. Particle Swam Optimization (PSO) and Principal Component Analysis (PCA) were also used sideways for comparison and to provide rigorous feature selection and analysis. Statistical analysis using ANOVA and correlation techniques confirmed the effectiveness of the GA-based and PSO-based techniques as there were no redundant features, since the subset of features selected by both techniques correlated well. The number of principal components (PC) from the past were selected by conventional method associated with PCA. However, in this study, GA was used to select a minimum number of PC from the original PC space. This reduced computational cost with respect to time and increased the accuracy of the classifier used. The algebraic nature of the GA’s fitness function ensures good performance of the GA. Furthermore, GA was also used to optimize the parameters of a CNN (CNN for image segmentation) and then uniquely combined with PNN to improve and stabilize the performance of the classification system. The CNN (being an ordinary differential equation (ODE)) was solved using Runge-Kutta 4th order algorithm in order to minimize descritisation errors associated with edge detection. This study involved the extraction of 112 features from the images of plant species found in the Flavia dataset (publically available) using MATLAB programming environment. These features include Zernike Moments (20 ZMs), Fourier Descriptors (21 FDs), Legendre Moments (20 LMs), Hu 7 Moments (7 Hu7Ms), Texture Properties (22 TP) , Geometrical Properties (10 GP), and Colour features (12 CF). With the use of GA, only 14 features were finally selected for optimal accuracy. The PNN was genetically optimized to ensure optimal accuracy since it is not the best practise to fix the tunning parameters for the PNN arbitrarily. Two separate GA algorithms were implemented to optimize the PNN, that is, the GA provided by MATLAB Optimization Toolbox (GA1) and a separately implemented GA (GA2). The best chromosome (PNN spread) for GA1 was 0.035 with associated classification accuracy of 91.3740% while a spread value of 0.06 was obtained from GA2 giving rise to improved classification accuracy of 92.62%. The PNN-based classifier used in this study was benchmarked against other classifiers such as Multi-layer perceptron (MLP), K Nearest Neigbhour (kNN), Naive Bayes Classifier (NBC), Radial Basis Function (RBF), Ensemble classifiers (Adaboost). The best candidate among these classifiers was the genetically optimized PNN. Some computational theoretic properties on PNN are also presented

Perception de la géométrie de l'environnement pour la navigation autonome

Le but de de la recherche en robotique mobile est de donner aux robots la capacité d'accomplir des missions dans un environnement qui n'est pas parfaitement connu. Mission, qui consiste en l'exécution d'un certain nombre d'actions élémentaires (déplacement, manipulation d'objets...) et qui nécessite une localisation précise, ainsi que la construction d'un bon modèle géométrique de l'environnement, a partir de l'exploitation de ses propres capteurs, des capteurs externes, de l'information provenant d'autres robots et de modèle existant, par exemple d'un système d'information géographique. L'information commune est la géométrie de l'environnement. La première partie du manuscrit couvre les différents méthodes d'extraction de l'information géométrique. La seconde partie présente la création d'un modèle géométrique en utilisant un graphe, ainsi qu'une méthode pour extraire de l'information du graphe et permettre au robot de se localiser dans l'environnement.The goal of the mobile robotic research is to give robots the capability to accomplish missions in an environment that might be unknown. To accomplish his mission, the robot need to execute a given set of elementary actions (movement, manipulation of objects...) which require an accurate localisation of the robot, as well as a the construction of good geometric model of the environment. Thus, a robot will need to take the most out of his own sensors, of external sensors, of information coming from an other robot and of existing model coming from a Geographic Information System. The common information is the geometry of the environment. The first part of the presentation will be about the different methods to extract geometric information. The second part will be about the creation of the geometric model using a graph structure, along with a method to retrieve information in the graph to allow the robot to localise itself in the environment

Text recognition and 2D/3D object tracking

Orientadores: Jorge Stolfi, Neucimar Jerônimo LeiteTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Nesta tese abordamos três problemas de visão computacional: (1) detecção e reconhecimento de objetos de texto planos em imagens de cenas reais; (2) rastreamento destes objetos de texto em vídeos digitais; e (3) o rastreamento de um objeto tridimensional rígido arbitrário com marcas conhecidas em um vídeo digital. Nós desenvolvemos, para cada um dos problemas, algoritmos inovadores, que são pelo menos tão precisos e robustos quanto outros algoritmos estado-da-arte. Especificamente, para reconhecimento de texto nós desenvolvemos (e validamos extensivamente) um novo descritor de imagem baseado em HOG especializado para escrita romana, que denominamos T-HOG, e mostramos sua contribuição como um filtro em um detector de texto (SNOOPERTEXT). Nós também melhoramos o algoritmo SNOOPERTEXT através do uso da técnica multiescala para tratar caracteres de tamanhos bastante variados e limitar a sensibilidade do algoritmo a vários artefatos. Para rastreamento de texto, nós descrevemos quatro estratégias básicas para combinar a detecção e o rastreamento de texto, e desenvolvemos também um rastreador específico baseado em filtro de partículas que explora o uso do reconhecedor T-HOG. Para o rastreamento de objetos rígidos, nós desenvolvemos um novo algoritmo preciso e robusto (AFFTRACK) que combina rastreamento de características por KLT com uma calibração de câmera melhorada. Nós testamos extensivamente nossos algoritmos com diversas bases de dados descritas na literatura. Nós também desenvolvemos algumas bases de dados (publicamente disponíveis) para a validação de algoritmos de detecção e rastreamento de texto e de rastreamento de objetos rígidos em vídeosAbstract: In this thesis we address three computer vision problems: (1) the detection and recognition of flat text objects in images of real scenes; (2) the tracking of such text objects in a digital video; and (3) the tracking an arbitrary three-dimensional rigid object with known markings in a digital video. For each problem we developed innovative algorithms, which are at least as accurate and robust as other state-of-the-art algorithms. Specifically, for text classification we developed (and extensively evaluated) a new HOG-based descriptor specialized for Roman script, which we call T-HOG, and showed its value as a post-filter for an existing text detector (SNOOPERTEXT). We also improved the SNOOPERTEXT algorithm by using the multi-scale technique to handle widely different letter sizes while limiting the sensitivity of the algorithm to various artifacts. For text tracking, we describe four basic ways of combining a text detector and a text tracker, and we developed a specific tracker based on a particle-filter which exploits the T-HOG recognizer. For rigid object tracking we developed a new accurate and robust algorithm (AFFTRACK) that combines the KLT feature tracker with an improved camera calibration procedure. We extensively tested our algorithms on several benchmarks well-known in the literature. We also created benchmarks (publicly available) for the evaluation of text detection and tracking and rigid object tracking algorithmsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

Modélisation et représentation dans l'espace des phénomènes photoniques inélastiques en biophotonique

Ce présent mémoire s’intéresse à la modélisation mathématique pour aborder la spatialité de signaux de spectroscopie Raman et de fluorescence dans des problématiques d’assistance au diagnostic et d’aide à l’instrumentation. Dans un premier temps, ce mémoire expose une technique de simulation adaptée à un large spectre d’interaction photon-matière basée sur la résolution par tracé de chemin Monté-Carlo pour des domaines discrets. L’algorithme développé, le parcours caché des photons, supporte notamment les phénomènes linéaires, soit l’absorption et l’émission spontanée, les diffusions élastiques et inélastiques (Raman), les réflexions, les réfractions et la fluorescence. Le modèle a été conçu dans l’objectif d’être adapté à la complexité des milieux biologiques, soit la complexité des interactions et des géométries. La représentation discrète de l’espace est réalisée par Marching Cube et l’ensemble des phénomènes est simulé simultanément, pour plusieurs longueurs d’onde discrètes, afin de supporter les interactions entre les phénomènes (diaphonie) et de produire une solution physiquement exacte. La solution a été implémentée dans un format de calcul générique sur un processeur graphique par adaptation du pipeline 3D. L’algorithme présenté aborde aussi des méthodes pour limiter l’utilisation de la mémoire afin de présenter une solution non prohibitive aux phénomènes Raman et de fluorescence à plusieurs longueurs d’onde. De plus, la solution proposée intègre une caméra, une visualisation de la fluence et une visualisation 3D des photons afin d’être adaptée au domaine de la biophysique. Finalement, les algorithmes développés sont validés par la prédiction de résultats déterminés selon une base théorique et expérimentale. Le simulateur propose une méthode théorique pour calibrer les instruments de mesure optiques et pour évaluer la portée d'un signal. Dans un second temps, ce mémoire propose des méthodes de réduction de dimensionnalité pour optimiser la reconnaissance automatisée de volumes de données rattachés à des modalités optiques dans un contexte biomédical. Deux modalités optiques sont plus spécialement visées, soit la microscopie Raman et la tomographie en cohérence optique. Dans le premier cas, un outil effectuant des analyses chimiométriques a été mis au point pour reproduire les images de coloration histologique avec la microscopie traditionnelle. L’algorithme a été proposé pour des échantillons fixés sur des lames d’aluminium.----------Abstract This master’s thesis focuses on mathematical modelling to address the spatiality of Raman spectroscopy and fluorescence signals to assist instrumentation and diagnostics. Firstly, this thesis presents a simulation technique adapted to a broad spectrum of photon-matter interaction based on the Monte Carlo path tracing resolution for discrete domains. The developed algorithm, the hidden path of photons, notably supports linear phenomena, namely absorption and spontaneous emission, elastic and inelastic scattering (Raman), reflections, refractions and fluorescence. The model was designed with the objective of being adapted to the complexity of biological environments, of interactions and of geometries. The discrete representation of space is performed by Marching Cube and the set of phenomena is simulated simultaneously, for several discrete wavelengths, in order to support the interactions between the phenomena (crosstalk) and to produce a physically exact solution. The solution has been implemented in a general-purpose processing on graphics processing units format by adaptation of the 3D pipeline. The presented algorithm also addresses methods to limit the use of memory in order to present a non-prohibitive solution to Raman diffusion and fluorescence at several wavelengths. In addition, the proposed solution integrates a camera, a visualization of fluence and a 3D visualization of photons to be adapted to the field of biophysics. Finally, the algorithms developed are validated by the prediction of known results on a theoretical and empirical basis. The simulator represents a theoretical method for calibrating optical measuring instruments and determining the spatial range of a signal. Secondly, this thesis proposes dimensionality reduction methods to optimize the automated recognition of data volumes related to optical modalities in biomedical contexts. Two optical modalities are more specifically targeted, namely Raman microscopy and optical coherence tomography. In the first case, a tool performing chemometrics analysis was developed to reproduce histologic staining images with traditional microscopy. The algorithm has been proposed for samples fixed on aluminium microscope slides. By evaluating the contribution of the measured signal on an empty slide, algorithm seeks to evaluate the drop in concentration of the compounds of interest, making analogy to the gradual transparency in histology, thus offering a more faithful representation

Intrinsic Properties and Fabric Anisotropy of Sands

The intrinsic properties and fabric anisotropy of sands significantly affect their macroscopic engineering behavior including packing densities, compressibility and strength. However, due to difficulties in reliably and rapidly determining them, intrinsic properties such as gradation, particle roundness and sphericity as well as the related fabric anisotropy of soils have not received their deserved attention and usage in practice. This dissertation introduces research that has facilitated rapid and precise quantification of soil properties and fabric anisotropy using various newly developed image analysis techniques. Extensive laboratory tests were performed on sands of various gradations, roundnesses, sphericities and geologic origins to develop relationships between their intrinsic properties and macroscopic mechanical behavior. A gradation-shape-fabric based Distinct Element Modeling technique was developed to simulate the properties and fabric anisotropy of soils. Besides geotechnical engineering, the technique can be used by engineers and scientists in various disciplines including material science, geology, mining, powder sciences, pavement engineering and agriculture to simulate more realistic material particle geometries and microstructures.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138645/1/junxing_1.pd