Tant sur le plan historique que sur le plan individuel, l’écriture connait une progression régulière : elle part du dessin et aboutit aux lettres. Les écritures alphabétiques permettent de transcrire chaque élément sonore de la langue, donnant ainsi l’impression de pouvoir reproduire la parole. La parole possède des liens profonds avec l’écriture, mais pas forcément là où on les attend. L’écriture ne reproduit pas la parole, en revanche, la parole regorge d’artifices langagiers qui tenteront de trouver leur place à l’écrit. Le présent travail permet de pointer l’apparition tardive des divers procédés de segmentation de l’écrit. Désormais, ni le blanc inter-lexical, ni les signes de ponctuation ne sont des éléments consubstantiels à l’apparition de l’écriture alphabétique, mais des ajouts ultérieurs. / On an historical level as well as on the individual one, the writing evolution knows a constant progress starting on drawing and ending up in letters. The alphabetic writing system allows the transcription of every sonorous element of the language, thus giving the impression of being able to reproduce the speech. Speech and writing have profound links, but not necessarily where expected. In fact, writing does not reproduce speech. On the other hand, speech is full of linguistic artifices that will attempt to find a written form. The present work shows the late appearance of the numerous processes of writing segmentation. Henceforth, neither inter-lexical blanks nor punctuation marks are consubstantial to the alphabetic writing appearance, but later additions

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Skeletonization and segmentation are two important techniques for object representation and analysis. Skeletonization algorithm extracts the centre-lines of an object and uses them to efficiently represent the object. It has many applications in various areas, such as computer-aided design, computer-aided engineering, and virtual reality. Segmentation algorithm locates the target object or Region Of Interest (ROI) from images. It has been widely applied to medical image analysis and many other areas. This thesis presents two studies in skeletonization and two studies in segmentation that advanced the state-of-the-art research. The first skeletonization study suggests an improvement of an existing algorithm for connectivity preservation, which is one of the fundamental requirements for skeletonization algorithms. The second skeletonization study proposes a method to generate curve skeletons with unit-width, which is required by many applications. The first segmentation study presents a new approach named Flexible Vector Flow (FVF) to address a few problems of other active contour models such as insufficient capture range and poor convergence for concavities. This approach was applied to brain tumor segmentation in two dimensional (2D) space. The second segmentation study extends the 2D FVF algorithm to three-dimension (3D) and utilizes it to automatically segment brain tumors in 3D.

Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: We develop and evaluate several algorithms that segment a speech signal into subword units without using phone or orthographic transcripts. These segmentation algorithms rely on a scoring function, termed the local score, that is applied at the feature level and indicates where the characteristics of the audio signal change. The predominant approach in the literature to segmentation is to apply a threshold to the local score, and local maxima (peaks) that are above the threshold result in the hypothesis of a segment boundary. Scoring mechanisms of a select number of such algorithms are investigated, and it is found that these local scores frequently exhibit clusters of peaks near phoneme transitions that cause spurious segment boundaries. As a consequence, very short segments are sometimes postulated by the algorithms. To counteract this, ad-hoc remedies are proposed in the literature. We propose a dynamic programming (DP) framework for speech segmentation that employs a probabilistic segment length model in conjunction with the local scores. DP o ers an elegant way to deal with peak clusters by choosing only the most probable segment length and local score combinations as boundary positions. It is shown to o er a clear performance improvement over selected methods from the literature serving as benchmarks. Multilayer perceptrons (MLPs) can be trained to generate local scores by using groups of feature vectors centred around phoneme boundaries and midway between phoneme boundaries in suitable training data. The MLPs are trained to produce a high output value at a boundary, and a low value at continuity. It was found that the more accurate local scores generated by the MLP, which rarely exhibit clusters of peaks, made the additional application of DP less e ective than before. However, a hybrid approach in which DP is used only to resolve smaller, more ambiguous peaks in the local score was found to o er a substantial improvement on all prior methods. Finally, restricted Boltzmann machines (RBMs) were applied as features detectors. This provided a means of building multi-layer networks that are capable of detecting highly abstract features. It is found that when local score are estimated by such deep networks, additional performance gains are achieved. / AFRIKAANSE OPSOMMING: Ons ontwikkel en evalueer verskeie algoritmes wat 'n spraaksein in sub-woord eenhede segmenteer sonder om gebruik te maak van ortogra ese of fonetiese transkripsies. Dié algoritmes maak gebruik van 'n funksie, genaamd die lokale tellingsfunksie, wat 'n waarde produseer omtrent die lokale verandering in 'n spraaksein. In die literatuur is daar gevind dat die hoofbenadering tot segmentasie gebaseer is op 'n grenswaarde, waarbo alle lokale maksima (pieke) in die lokale telling lei tot 'n skeiding tussen segmente. 'n Selektiewe groep segmentasie algoritmes is ondersoek en dit is gevind dat lokale tellings geneig is om groeperings van pieke te hê naby aan die skeidings tussen foneme. As gevolg hiervan, word baie kort segmente geselekteer deur die algoritmes. Om dit teen te werk, word ad-hoc metodes voorgestel in die literatuur. Ons stel 'n alternatiewe metode voor wat gebaseer is op dinamiese programmering (DP), wat 'n statistiese verspreiding van lengtes van segmente inkorporeer by segmentasie. DP bied 'n elegante manier om groeperings van pieke te hanteer, deurdat net kombinasies van hoë lokale tellings en segmentwaarskynlikheid, met betrekking tot die lengte van die segment, tot 'n skeiding lei. Daar word gewys dat DP 'n duidelike verbetering in segmentasie akkuraatheid toon bo 'n paar gekose algoritmes uit die literatuur. Meervoudige lae perseptrone (MLPe) kan opgelei word om 'n lokale telling te genereer deur gebruik te maak van groepe eienskapsvektore gesentreerd rondom en tussen foneem skeidings in geskikte opleidingsdata. Die MLPe word opgelei om 'n groot waarde te genereer as 'n foneem skeiding voorkom en 'n klein waarde andersins. Dit is gevind dat die meer akkurate lokale tellings wat deur die MLPe gegenereer word minder groeperings van pieke het, wat dan die addisionele toepassing van die DP minder e ektief maak. 'n Hibriede toepassing, waar DP net tussen kleiner en minder duidelike pieke in die lokale telling kies, lei egter tot 'n groot verbetering bo-op alle vorige metodes. As 'n nale stap het ons beperkte Boltzmann masjiene (BBMe) gebruik om patrone in data te identi- seer. Sodoende, verskaf BBMe 'n manier om meervoudige lae netwerke op te bou waar die boonste lae baie komplekse patrone in die data identi seer. Die toepassing van dié dieper netwerke tot die generasie van 'n lokale telling het tot verdere verbeteringe in segmentasie-akkuraatheid gelei. / National Research Foundation (NRF)

Syfte - Att undersöka hur en kundsegmenteringsprocess kan utformas samt vilka variabler man bör iaktta för att kunna erbjuda en lämplig kundservicenivå. För att uppnå detta syfte skall följande frågeställningar besvaras: 1. Vilka variabler bör ingå i en kundsegmentering? 2. Hur kan en kundsegmenteringsprocess utformas? Metod - En generell kundsegmenteringsprocess utformades. Processen har utvecklats genom kvalitativ forskning baserad på litteraturstudier samt intervjuer i en fallstudie. Under litteraturstudien granskades teorier i ämnet för att besvara frågeställningarna. Detta jämfördes senare med empirin som samlats under fallstudien. Resultat - Den utformade processen innehåller sju dimensioner med tillhörande variabler. Under studien har variablerna utvärderats för att ta reda på hur de påverkar situationen. Endast de variabler som hade ett stort inflytande på situationen togs med i processen. Studien visade att det finns olika strategier för att utföra en kundsegmentering. Vid utformning av en kundsegmenteringsprocess är det viktigt att veta vilka variabler som passar organisationens bransch samt hur de påverkar resultatet. Omfång och Avgränsningar - Rapporten är begränsad till att utforma ett förslag på en kundsegmenteringsprocess. Processen kommer därför inte att tillämpas på fallföretaget under fallstudien. Processen kan fortfarande generaliseras och användas av företag med liknade egenskaper. Ytterligare forskning skulle kunna sträva efter att inkludera andra variabler som passar in på fler branscher. Implikationer - Den utformade processen hjälper till vid beslutssituationer avseende kundsegmentering. Genom att balansera de variabler som föreslagits möjliggör dem en grund för olika kundserviceerbjudanden. Dessa variabler beaktar den eftersträvade generaliseringen. Bidrag och Rekommendationer - Kundsegmenteringsprocessen som presenteras i denna rapport är, såvitt författarna vet, den första i sitt slag med sin layout. Variablerna kan även användas i andra segmenteringsprocesser vilket visar en hög grad av generalisering. Vad som är unikt med den designade processen i denna rapport är att den innehåller en mix av två väl beprövade teorier inom kundsegmentering nämligen, Kotler’s (2009) Bottom-Up-Approach och Weinstein’s (2004) B2B Market Segmentation. / Purpose – To examine how the process of customer segmentation can be designed, and which variables to consider to offer an appropriate customer service. To achieve this purpose the following questions will be answered: 1. Which variables should be included in customer segmentation? 2. How can a customer segmentation process be designed? Method – A general process was designed. The process has been developed through qualitative research based on literature review and interviews conducted in a case study. During the literature review the authors sought for theories on the subject in order to answer the research questions. This was later compared to the empirical evidence collected from the case study. Findings – The designed process contains seven dimensions with related variables. During the study the variables were evaluated concerning their impact on the situation. Only variables that had a high influence on the situation were implemented in the process. The study showed that that there are various approaches towards performing customer segmentation. When designing a customer segmentation process, it is of high importance to know which variables suit the organizations line of business and how they affect the outcome. Research limitations – The thesis is restricted into designing a customer segmentation process, the process will therefore not be applied on the case company during the case study. The process can still be generalized and usable for companies with similar distribution setup. Further research could strive to include other variables. Implications – The designed process assists in the decision-making situation regarding customer segmentation. By balancing the variables it enables a basis for customer service offering. These variables take the requested generalization in consideration. Originality/value – The customer segmentation process presented in this thesis is, as far as the authors know, the first in its kind with its layout. The variables could be used in other segmentation processes as well which show a high grade of generalization. What is unique with the designed process in this thesis is that it contains a mixture of two well proven customer segmentation theories namely, Kotler’s (2009) Bottom-Up-Approach and Weinstein’s (2004) B2B Market Segmentation.

Much has been written about the discipline of market segmentation as both a marketing competency and ultimately a valuable component of overarching business strategy. Organisations have demonstrated the practical benefits of harnessing segmentation in various market contexts and shown the theoretical constructs of the discipline to be sustainably sound in their capacity to guide businesses towards strategic portfolio optimisation. Despite this rich history however, recent academic investigation has highlighted that deep complexity plagues the effectiveness with which segmentation is harnessed with significant impact on business outcomes.This study was developed from a curiosity to explore some of the identified gaps with specific reference to how these manifest within the South African operating environment. Detailed review of literary perspective on the matter highlighted topical aspects that were deemed meaningful to use as a roadmap to guide the study investigations. Research data was collated from seasoned South African marketing practitioners and used to evaluate their practical experiences of defining and implementing market segmentation against established academic perspective. The study was purely qualitative with data being collected through 10 in-depth interviews that were conducted with target respondents from 10 different organisations across six industry sectors.The findings were analysed using a recently released version of leading qualitative data analysis software enabling the identification of key themes and the construction of resulting association maps. The ensuing network maps ultimately enabled the construction of a consolidated organisational interaction map that typifies the stated experiences of South African marketers in their attempts to leverage and optimize strategic value from market segmentation for their organisations.<p/> / Dissertation (MBA)--University of Pretoria, 2012. / Gordon Institute of Business Science (GIBS) / unrestricted

La segmentation est l'un des principaux thèmes du domaine de l'analyse d'images. Segmenter une image consiste à trouver une partition constituée de régions, c'est-à-dire d'ensembles de pixels connexes homogènes selon un critère choisi. L'objectif de la segmentation consiste à obtenir des régions correspondant aux objets ou aux parties des objets qui sont présents dans l'image et dont la nature dépend de l'application visée. Même s'il peut être très fastidieux, un tel découpage de l'image peut être facilement obtenu par un être humain. Il n'en est pas de même quand il s'agit de créer un programme informatique dont l'objectif est de segmenter les images de manière entièrement automatique. La segmentation interactive est une approche semi-automatique où l'utilisateur guide la segmentation d'une image en donnant des indications. Les méthodes qui s'inscrivent dans cette approche se divisent en deux catégories en fonction de ce qui est recherché : les contours ou les régions. Les méthodes qui recherchent des contours permettent d'extraire un unique objet correspondant à une région sans trou. L'utilisateur vient guider la méthode en lui indiquant quelques points sur le contour de l'objet. L'algorithme se charge de relier chacun des points par une courbe qui respecte les caractéristiques de l'image (les pixels de part et d'autre de la courbe sont aussi dissemblables que possible), les indications données par l'utilisateur (la courbe passe par chacun des points désignés) et quelques propriétés intrinsèques (les courbes régulières sont favorisées). Les méthodes qui recherchent les régions groupent les pixels de l'image en des ensembles, de manière à maximiser la similarité en leur sein et la dissemblance entre les différents ensembles. Chaque ensemble correspond à une ou plusieurs composantes connexes et peut contenir des trous. L'utilisateur guide la méthode en traçant des traits de couleur qui désignent quelques pixels appartenant à chacun des ensembles. Si la majorité des méthodes ont été conçues pour extraire un objet principal du fond, les travaux menés durant la dernière décennie ont permis de proposer des méthodes dites multiclasses, capables de produire une partition de l'image en un nombre arbitraire d'ensembles. La contribution principale de ce travail de recherche est la conception d'une nouvelle méthode de segmentation interactive multiclasse par recherche des régions. Elle repose sur la modélisation du problème comme la minimisation d'une fonction de coût pouvant être représentée par un graphe de facteurs. Elle intègre une méthode de classification par apprentissage supervisé assurant l'adéquation entre la segmentation produite et les indications données par l'utilisateur, l'utilisation d'un nouveau terme de régularisation et la réalisation d'un prétraitement consistant à regrouper les pixels en petites régions cohérentes : les superpixels. L'utilisation d'une méthode de sur-segmentation produisant des superpixels est une étape clé de la méthode que nous proposons : elle réduit considérablement la complexité algorithmique et permet de traiter des images contenant plusieurs millions de pixels, tout en garantissant un temps interactif. La seconde contribution de ce travail est une évaluation des algorithmes permettant de grouper les pixels en superpixels, à partir d'un nouvel ensemble de données de référence que nous mettons à disposition et dont la particularité est de contenir des images de tailles différentes : de quelques milliers à plusieurs millions de pixels. Cette étude nous a également permis de concevoir et d'évaluer une nouvelle méthode de production de superpixels. / Image segmentation is one of the main research topics in image analysis. It is the task of researching a partition into regions, i.e., into sets of connected pixels, meeting a given uniformity criterion. The goal of image segmentation is to find regions corresponding to the objects or the object parts appearing in the image. The choice of what objects are relevant depends on the application context. Manually locating these objects is a tedious but quite simple task. Designing an automatic algorithm able to achieve the same result is, on the contrary, a difficult problem. Interactive segmentation methods are semi-automatic approaches where a user guide the search of a specific segmentation of an image by giving some indications. There are two kinds of methods : boundary-based and region-based interactive segmentation methods. Boundary-based methods extract a single object corresponding to a unique region without any holes. The user guides the method by selecting some boundary points of the object. The algorithm search for a curve linking all the points given by the user, following the boundary of the object and having some intrinsic properties (regular curves are encouraged). Region-based methods group the pixels of an image into sets, by maximizing the similarity of pixels inside each set and the dissimilarity between pixels belonging to different sets. Each set can be composed of one or several connected components and can contain holes. The user guides the method by drawing colored strokes, giving, for each set, some pixels belonging to it. If the majority of region-based methods extract a single object from the background, some algorithms, proposed during the last decade, are able to solve multi-class interactive segmentation problems, i.e., to extract more than two sets of pixels. The main contribution of this work is the design of a new multi-class interactive segmentation method. This algorithm is based on the minimization of a cost function that can be represented by a factor graph. It integrates a supervised learning classification method checking that the produced segmentation is consistent with the indications given by the user, a new regularization term, and a preprocessing step grouping pixels into small homogeneous regions called superpixels. The use of an over-segmentation method to produce these superpixels is a key step in the proposed interactive segmentation method : it significantly reduces the computational complexity and handles the segmentation of images containing several millions of pixels, by keeping the execution time small enough to ensure comfortable use of the method. The second contribution of our work is an evaluation of over-segmentation algorithms. We provide a new dataset, with images of different sizes with a majority of big images. This review has also allowed us to design a new over-segmentation algorithm and to evaluate it.

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Nous proposons une nouvelle formulation de minimisation de fonctions d’énergies pour la traitement de la vision sur toute la segmentation d'image. Le problème est modélisé comme étant un jeu stratégique non coopératif, et le processus d'optimisation est interprété comme étant la recherche de l'équilibre de nash. Ce problème reste un problème combinatoire sous cette forme d'où nous avons opté à le résoudre en utilisant un algorithme de Séparation-Évaluation. Pour illustrer la performance de la nouvelle approche, nous l'avons appliqué sur des fonctions de régularisation convexe ainsi que non convexe / We propose a new formulation of the energy minimisation paradigm for image segmentation. The segmentation problem is modeled as a non-cooperative strategic game, and the optimization process is interpreted as the search of a Nash equilibrium. The problem is expressed as a combinatorial problem, for which an efficient Branch and Bound algorithm is proposed to solve the problem exactly. To illustrate the performance of the proposed framework, it is applied on convex regularization model, as well as a non-convex regularized segmentation models

People often have difficulties finding specific information in video because of its linear and unstructured nature. Segmenting long videos into small clips by topics and providing browsing and search functionalities is beneficial for information searching. However, manual segmentation is labor intensive and existing automated segmentation methods are not effective for plenty of amateur made and unedited lecture videos. The objectives of this dissertation are to develop 1) automated segmentation algorithms to extract the topic structure of a lecture video, and 2) retrieval algorithms to identify the relevant video segments for user queries.Based on an extensive literature review, existing segmentation features and approaches are summarized and research challenges and questions are presented. Manual segmentation studies are conducted to understand the content structure of a lecture video and a set of potential segmentation features and methods are extracted to facilitate the design of automated segmentation approaches. Two static algorithms are developed to segment a lecture video into a list of topics. Features from multimodalities and various knowledge sources (e.g. electronic slides) are used in the segmentation algorithms. A dynamic segmentation method is also developed to retrieve relevant video segments of appropriate sizes based on the questions asked by users. A series of evaluation studies are conducted and results are presented to demonstrate the effectiveness and usefulness of the automated segmentation approaches.