Novel block-based motion estimation and segmentation for video coding

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Nearest Neighbor Discriminant Analysis Based Face Recognition Using Ensembled Gabor Features

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Bilişim Enstitüsü, 2009Thesis (M.Sc.) -- İstanbul Technical University, Institute of Informatics, 2009Son yıllarda, ışık varyasyonlarına ve yüz ifade değişikliklerine karşı gürbüz olduğu üzere yüz tanıma alanında Gabor öznitelikleri tabanlı yüz temsil etme çok umut vaad edici sonuç vermiştir. Seçilen uzamsal frekans, uzamsal lokalizasyon ve yönelime göre yerel yapıyı hesaplaması, elle işaretlendirmeye ihtiyaç duymaması Gabor özniteliklerini efektif yapan özellikleridir. Bu tez çalışmasındaki katkı, Gabor süzgeçleri ve En Yakın Komşu Ayrışım Analizi'nin (EYKAA) güçlerini birleştirerek önemli ayrışım öznitelikleri ortaya çıkaran Gabor En Yakın Komşu Sınıflandırıcısı (GEYKS) genişletip Parçalı Gabor En Yakın Komşu Sınıflandırıcısı (PGEYKS) metodunu ortaya koymaktır. PGEYKS; alçaltılmış gabor öznitelikleri barındıran farklı segmanları kullanarak, her biri ayrı dizayn edilen birçok EYKAA tabanlı bileşen sınıflandırıcılarını bir araya getiren grup sınıflandırıcısıdır. Tüm gabor özniteliklerinin alçaltılmış boyutu tek bir EYKAA bileşeninden çıkarıldığı gibi, PGEYKS; ayrışım bilgi kaybını minimum yapıp 3S (yetersiz örnek miktarı) problemini önleyerek alçaltılmış gabor öznitelikleri içindeki ayrıştırabilirliği daha iyi kullanır. PGEYKS yönteminin tanıma başarımı karşılaştırmalı performans çalışması ile gösterilmiştir. Farklı ışıklandırma ve yüz ifadesi deişiklikleri barındıran 200 sınıflık FERET veritabanı alt kümesinde, 65 öznitelik için PGEYKS %100 başarım elde ederek atası olan GEYKS'nın aldığı %98 başarısını ve diğer GFS (Gabor Fisher Sınıflandırıcı) ve GTS (Gabor Temel Sınıflandırıcı) gibi standard methodlardan daha iyi sonuçlar vermiştir. Ayrıca YALE veritabanı üzerindeki testlerde PGEYKS her türlü (k, alpha) çiftleri için GEYKS'ten daha başarılıdır ve 14 öznitelik için step size = 5, k = 5, alpha = 3 parametlerinde %96 tanıma başarısına ulaşmıştır.In last decades, Gabor features based face representation performed very promising results in face recognition area as its robust to variations due to illumination and facial expression changes. The properties of Gabor are, which makes it effective, it computes the local structure corresponding to spatial frequency (scale), spatial localization, and orientation selectivity and no need for manual annotations. The contribution of this thesis, an Ensemble based Gabor Nearest Neighbor Classifier (EGNNC) method is proposed extending Gabor Nearest Neighbor Classifier (GNNC) where GNNC extracts important discriminant features both utilizing the power of Gabor filters and Nearest Neighbor Discriminant Analysis (NNDA). EGNNC is an ensemble classifier combining multiple NNDA based component classifiers designed respectively using different segments of the reduced Gabor feature. Since reduced dimension of the entire Gabor feature is extracted by one component NNDA classifier, EGNNC has better use of the discriminability implied in reduced Gabor features by the avoiding 3S (small sample size) problem as making minimum loss of discriminative information. The accuracy of the EGNNC is shown by comparative performance work. Using a 200 class subset of FERET database covering illumination and expression variations, EGNNC achieved 100% recognition rate, outperforming its ancestor GNNC perform 98 percent as well as standard methods such GFC and GPC for 65 features. Also for the YALE database, EGNNC outperformed GNNC on all (k, alpha) tuples and EGNNC reaches 96 percent accuracy in 14 feature dimension, along with parameters step size = 5, k = 5, alpha = 3.Yüksek LisansM.Sc

Apprentissage d'espaces sémantiques

Dans cette dissertation, nous présentons plusieurs techniques d’apprentissage d’espaces sémantiques pour plusieurs domaines, par exemple des mots et des images, mais aussi à l’intersection de différents domaines. Un espace de représentation est appelé sémantique si des entités jugées similaires par un être humain, ont leur similarité préservée dans cet espace. La première publication présente un enchaînement de méthodes d’apprentissage incluant plusieurs techniques d’apprentissage non supervisé qui nous a permis de remporter la compétition “Unsupervised and Transfer Learning Challenge” en 2011. Le deuxième article présente une manière d’extraire de l’information à partir d’un contexte structuré (177 détecteurs d’objets à différentes positions et échelles). On montrera que l’utilisation de la structure des données combinée à un apprentissage non supervisé permet de réduire la dimensionnalité de 97% tout en améliorant les performances de reconnaissance de scènes de +5% à +11% selon l’ensemble de données. Dans le troisième travail, on s’intéresse à la structure apprise par les réseaux de neurones profonds utilisés dans les deux précédentes publications. Plusieurs hypothèses sont présentées et testées expérimentalement montrant que l’espace appris a de meilleures propriétés de mixage (facilitant l’exploration de différentes classes durant le processus d’échantillonnage). Pour la quatrième publication, on s’intéresse à résoudre un problème d’analyse syntaxique et sémantique avec des réseaux de neurones récurrents appris sur des fenêtres de contexte de mots. Dans notre cinquième travail, nous proposons une façon d’effectuer de la recherche d’image ”augmentée” en apprenant un espace sémantique joint où une recherche d’image contenant un objet retournerait aussi des images des parties de l’objet, par exemple une recherche retournant des images de ”voiture” retournerait aussi des images de ”pare-brises”, ”coffres”, ”roues” en plus des images initiales.In this work, we focus on learning semantic spaces for multiple domains, but also at the intersection of different domains. The semantic space is where the learned representation lives. This space is called semantic if similar entities from a human perspective have their similarity preserved in this space. We use different machine learning algorithms to learn representations with interesting intrinsic properties. The first article presents a pipeline including many different unsupervised learning techniques used to win the Unsupervised and Transfer Learning Challenge in 2011. In the second article, we present a pipeline taking advantage of the structure of the data for a scene classification problem. This approach allows us to drastically reduce the dimensionality while improving significantly on the scene recognition accuracy. The third article focuses on the space structure learned by deep representations. We show that performing the sampling procedure from deeper levels of representation space explores more of the different classes. In the fourth article, we tackle a semantic parsing problem with several Recurrent Neural Network architectures taking as input context windows of word embeddings. In the fifth article, an investigation on learning a single semantic space at the intersection of words and images is presented. We propose a way to perform ”augmented search” where a search on an image containing an object would also return images of the object’s parts

Proceedings, MSVSCC 2015

The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters. Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair John ShullGraduate Student, MSVE Capstone Conference Student Chai

Research reports: 1987 NASA/ASEE Summer Faculty Fellowship Program

For the 23rd consecutive year, a NASA/ASEE Summer Faculty Fellowship Program was conducted at the Marshall Space Flight Center (MSFC). The program was conducted by the University of Alabama in Huntsville and MSFC during the period 1 June to 7 August 1987. Operated under the auspices of the American Society for Engineering Education, the MSFC program, as well as those at other NASA Centers, was sponsored by the Office of University Affairs, NASA Headquarters, Washington, D.C. The basic objectives of the program are: (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participant's institutions; and (4) to contribute to the research objectives of the NASA Centers. This document is a compilation of Fellow's reports on their research during the Summer of 1987

Bharata’s Natyashastra-based Theatre Analysis Model: An experiment on British South Asian and contemporary Indian theatre in English

This thesis tests a newly developed model based on the Natyashastra, an Indian treatise on performing arts, and uses this for theatrical analysis in the contexts of British Asian theatre productions and contemporary Indian theatre in English. The study offers a tool that can provide an alternative model of analysis. By extending the existing analytical models, we can ask questions concerning the actors’ emotional manifestation and their mental state while acting. This thesis attempts to interpret the actors’ gestures and provides a structure to analyse them. In order to do that, this project uses the Natyashastra and rasa/bhava concepts as performance analysis tools, which might provide an alternate perspective to theatre analysis. The thesis reviews existing models of theatrical analysis and argues for an alternative model in Chapter One. It examines the analysis of theatre productions by scholars of British Asian theatre and contemporary Indian theatre in English in Chapter Two. Here, I review the ways in which scholars of British South Asian theatre have examined theatrical productions so far. Chapter Three tests the proposed model on four theatre productions, illustrating the ways in which theatre productions could be analysed, and identifies the model’s limitations and advantages. Chapter Four discusses findings in the light of the results analysed in Chapter Three; it also outlines some questions which needs further investigation. By doing so, this thesis contributes to the field of performance analysis and theatre studies by developing strong links between the manifestation of the actors’ bodymind, the directors' reception after their first reading of a play’s text, and playwrights’ initial emotions within the text, through production analysis

URI Undergraduate and Graduate Course Catalog 2019-2020

This is a downloadable PDF version of the University of Rhode Island course catalog.https://digitalcommons.uri.edu/course-catalogs/1071/thumbnail.jp