The Impact of Graph Layouts on the Perception of Graph Properties

abstract: Graphs are commonly used visualization tools in a variety of fields. Algorithms have been proposed that claim to improve the readability of graphs by reducing edge crossings, adjusting edge length, or some other means. However, little research has been done to determine which of these algorithms best suit human perception for particular graph properties. This thesis explores four different graph properties: average local clustering coefficient (ALCC), global clustering coefficient (GCC), number of triangles (NT), and diameter. For each of these properties, three different graph layouts are applied to represent three different approaches to graph visualization: multidimensional scaling (MDS), force directed (FD), and tsNET. In a series of studies conducted through the crowdsourcing platform Amazon Mechanical Turk, participants are tasked with discriminating between two graphs in order to determine their just noticeable differences (JNDs) for the four graph properties and three layout algorithm pairs. These results are analyzed using previously established methods presented by Rensink et al. and Kay and Heer.The average JNDs are analyzed using a linear model that determines whether the property-layout pair seems to follow Weber's Law, and the individual JNDs are run through a log-linear model to determine whether it is possible to model the individual variance of the participant's JNDs. The models are evaluated using the R2 score to determine if they adequately explain the data and compared using the Mann-Whitney pairwise U-test to determine whether the layout has a significant effect on the perception of the graph property. These tests indicate that the data collected in the studies can not always be modelled well with either the linear model or log-linear model, which suggests that some properties may not follow Weber's Law. Additionally, the layout algorithm is not found to have a significant impact on the perception of some of these properties.Dissertation/ThesisMasters Thesis Computer Science 201

Quality Analysis of a Printed Natural Reference Image

Tämän diplomityön tarkoituksena oli tutkia paperin vaikutusta koettuun kuvanlaatuun. Päätavoitteeksi asetettiin automaattisen, objektiivisen ohjelmistojärjestelmän kehittäminen ennustamaan ihmisen arviota paperin kuvanlaatuominaisuuksista. Tutkimusprojekti koostui neljästä vaiheesta: testikuvan suunnittelusta kuvanlaadun tutkimukseen, subjektiivisen kokonaislaadun ja laatuattribuuttien arvioinnista testikuvasta, ohjelmiston kehittämisestä ennustamaan laatuattribuutteja sekä visuaalisen laatumallin muodostamisesta ilmaisemaan kokonaislaatua yhdellä laatuarvosanalla. Tutkimuksessa käsiteltiin neljää laatuattribuuttia: värikkyyttä, kontrastia, terävyyttä ja kohinaa. Painatusmenetelmänä käytettiin mustesuihkutulostusta. Ensimmäisessä vaiheessa luotiin luonnollinen referenssikuva kuvanlaadun subjektiivista ja objektiivista arviointia varten. Suunnittelussa painotettiin laatuominaisuuksien lisäksi korkean tason ominaisuuksia, kuten luonnollisuutta, tasapainoa, ja esteettistä vaikutelmaa. Erityispiirteenä kuvaan lisättiin seitsemän GretagMacbeth testiväriä, jotka sisällytettiin kuvassa sijaitseviin luonnollisiin esineisiin. Seuraavassa vaiheessa suoritettiin subjektiivinen testaus ihmisen visuaalisen laatuarvion mittaamiseksi, josta saatuja laatuattribuuttien referenssiarvoja käytettiin objektiivisten laatumittojen suunnittelussa Matlab-ohjelmistolle. Lopuksi kehitetyt laatumitat yhdistettiin tilastollisen regressioanalyysin avulla yhdeksi arvosanaksi paperin kokonaislaadusta, ns. visuaaliseksi laatumalliksi. Myös laatuattribuuteille muodostettiin regressiomallit. Tutkimuksen tuloksena luotiin toimivat ja tilastollisesti tarkat objektiiviset mitat kolmelle laatuattribuutille: värikkyydelle, kontrastille ja kohinalle. Lisäksi kehitettiin mitta värivirheen laskentaan. Myös visuaalisen laatumallin toteutuksessa onnistuttiin hyvin, ja kaikkien regressiomallien selitysasteet olivat tilastollisesti korkeita. Subjektiivisten arvosanojen samankaltaisuus laadun ja laatuattribuuttien välillä johti kuitenkin ongelmiin regressiomallien yleistämisessä, mistä johtuen mallien käyttöä ei voitu suositella reaalimaailman sovelluksissa. Erityistä paneutumista vaativat myös testikuvan suuri värikkyys sekä ohjelmallisten laatumittojen optimointi paperi- ja painatusympäristöön.This thesis was contributed to study the image quality properties of printing papers. The main goal was to produce an automatic, objective software system for predicting human opinion on the print quality of papers. To reach this goal, the project was divided into four phases: the development of a reference image for image quality evaluation, the assessment of subjective print quality from the reference image, the programming of quality analysis software for quality attributes, and the construction of a single grade for print quality, visual quality index (VQI). Four low-level quality attributes were studied: colorfulness, contrast, sharpness, and noise. Only inkjet printing technology was covered. In the first phase, a natural reference image was developed for subjective and objective image quality testing. Focus was placed not only on quality aspects, but also on the high-level properties of the image, i.e. naturalness, balance, and aesthetical expression. Furthermore, presenting a unique feature for a reference image of this kind, seven GretagMacbeth test colors were implemented into natural objects in the image. During later phases, subjective tests were arranged to gather the subjective reference data of print quality for software development with Matlab. Finally, the computed quality attribute scores were combined with statistical regression analysis into a single grade for the print quality of papers, VQI, accompanied with individual regression models for the quality attributes. The outcome of the software development was three functional and statistically accurate Matlab implementations, i.e. for colorfulness, contrast, and noise, complemented with a color difference method. The implementation of the VQI was successful as well, showing remarkably strong goodness measures. However, the generalization of the regression models was compromised by the strong cross-attribute similarity of the subjective reference data, eventually preventing the feasibility of the models in real world applications. Other issues requiring attention included handling the high colorfulness of the reference image and optimizing the software to the print context

Repousser les limites de l'identification faciale en contexte de vidéo-surveillance

Les systèmes d'identification de personnes basés sur le visage deviennent de plus en plus répandus et trouvent des applications très variées, en particulier dans le domaine de la vidéosurveillance. Or, dans ce contexte, les performances des algorithmes de reconnaissance faciale dépendent largement des conditions d'acquisition des images, en particulier lorsque la pose varie mais également parce que les méthodes d'acquisition elles mêmes peuvent introduire des artéfacts. On parle principalement ici de maladresse de mise au point pouvant entraîner du flou sur l'image ou bien d'erreurs liées à la compression et faisant apparaître des effets de blocs. Le travail réalisé au cours de la thèse porte donc sur la reconnaissance de visages à partir d'images acquises à l'aide de caméras de vidéosurveillance, présentant des artéfacts de flou ou de bloc ou bien des visages avec des poses variables. Nous proposons dans un premier temps une nouvelle approche permettant d'améliorer de façon significative la reconnaissance des visages avec un niveau de flou élevé ou présentant de forts effets de bloc. La méthode, à l'aide de métriques spécifiques, permet d'évaluer la qualité de l'image d'entrée et d'adapter en conséquence la base d'apprentissage des algorithmes de reconnaissance. Dans un second temps, nous nous sommes focalisés sur l'estimation de la pose du visage. En effet, il est généralement très difficile de reconnaître un visage lorsque celui-ci n'est pas de face et la plupart des algorithmes d'identification de visages considérés comme peu sensibles à ce paramètre nécessitent de connaître la pose pour atteindre un taux de reconnaissance intéressant en un temps relativement court. Nous avons donc développé une méthode d'estimation de la pose en nous basant sur des méthodes de reconnaissance récentes afin d'obtenir une estimation rapide et suffisante de ce paramètre.The person identification systems based on face recognition are becoming increasingly widespread and are being used in very diverse applications, particularly in the field of video surveillance. In this context, the performance of the facial recognition algorithms largely depends on the image acquisition context, especially because the pose can vary, but also because the acquisition methods themselves can introduce artifacts. The main issues are focus imprecision, which can lead to blurred images, or the errors related to compression, which can introduce the block artifact. The work done during the thesis focuses on facial recognition in images taken by video surveillance cameras, in cases where the images contain blur or block artifacts or show various poses. First, we are proposing a new approach that allows to significantly improve facial recognition in images with high blur levels or with strong block artifacts. The method, which makes use of specific noreference metrics, starts with the evaluation of the quality level of the input image and then adapts the training database of the recognition algorithms accordingly. Second, we have focused on the facial pose estimation. Normally, it is very difficult to recognize a face in an image taken from another viewpoint than the frontal one and the majority of facial identification algorithms which are robust to pose variation need to know the pose in order to achieve a satisfying recognition rate in a relatively short time. We have therefore developed a fast and satisfying pose estimation method based on recent recognition techniques.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF