I Abstract (in English): Diploma thesis Visual lifelogging: automatic memories and picturing ordinary deals with the topic of visual lifelogging in the context of current visual culture. It describes visual lifelogging in the context of photography and as an amplification of human abilities. Diploma thesis deals with history and lifelogging as a whole and it describes its forms, describes the tools of lifelogging and it deals also with topics of surveillance and its contrary, sousveillance. It explores visual lifelogging mostly from the photographic point of view and it deals with the use of different technologies to augment human memory or other human abilities.

abstract: Audio signals, such as speech and ambient sounds convey rich information pertaining to a user’s activity, mood or intent. Enabling machines to understand this contextual information is necessary to bridge the gap in human-machine interaction. This is challenging due to its subjective nature, hence, requiring sophisticated techniques. This dissertation presents a set of computational methods, that generalize well across different conditions, for speech-based applications involving emotion recognition and keyword detection, and ambient sounds-based applications such as lifelogging. The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions is strongly desired. A latent topic models-based method is proposed to learn supra-segmental features from low-level acoustic descriptors. The derived features outperform state-of-the-art approaches over multiple databases. Cross-corpus studies are conducted to determine the ability of these features to generalize well across different databases. The proposed method is also applied to derive features from facial expressions; a multi-modal fusion overcomes the deficiencies of a speech only approach and further improves the recognition performance. Besides affecting the acoustic properties of speech, emotions have a strong influence over speech articulation kinematics. A learning approach, which constrains a classifier trained over acoustic descriptors, to also model articulatory data is proposed here. This method requires articulatory information only during the training stage, thus overcoming the challenges inherent to large-scale data collection, while simultaneously exploiting the correlations between articulation kinematics and acoustic descriptors to improve the accuracy of emotion recognition systems. Identifying context from ambient sounds in a lifelogging scenario requires feature extraction, segmentation and annotation techniques capable of efficiently handling long duration audio recordings; a complete framework for such applications is presented. The performance is evaluated on real world data and accompanied by a prototypical Android-based user interface. The proposed methods are also assessed in terms of computation and implementation complexity. Software and field programmable gate array based implementations are considered for emotion recognition, while virtual platforms are used to model the complexities of lifelogging. The derived metrics are used to determine the feasibility of these methods for applications requiring real-time capabilities and low power consumption. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2015

L’indexation par le contenu de lifelogs issus de capteurs portées a émergé comme un enjeu à forte valeur ajoutée permettant l’exploitation de ces nouveaux types de donnés. Rendu plus accessible par la récente disponibilité de dispositifs miniaturisés d’enregistrement, les besoins pour l’extraction automatique d’informations pertinents générées par autres applications, la localisation en environnement intérieur est un problème difficile à l’analyse de telles données.Beaucoup des solutions existantes pour la localisation fonctionnent insuffisamment bien ou nécessitent une intervention important à l’intérieur de bâtiment. Dans cette thèse, nous abordons le problème de la localisation topologique à partir de séquences vidéo issues d’une camera portée en utilisant une approche purement visuelle. Ce travail complète d’extraction des descripteurs visuels de bas niveaux jusqu’à l’estimation finale de la localisation à l’aide d’algorithmes automatiques.Dans ce cadre, les contributions principales de ce travail ont été faites pour l’exploitation efficace des informations apportées par descripteurs visuels multiples, par les images non étiquetées et par la continuité temporelle de la vidéo. Ainsi, la fusion précoce et la fusion tardive des données visuelles ont été examinées et l’avantage apporté par la complémentarité des descripteurs visuels a été mis en évidence sur le problème de la localisation. En raison de difficulté à obtenir des données étiquetées en quantités suffisantes, l’ensemble des données a été exploité ; d’une part les approches de réduction de dimensionnalité non-linéaire ont été appliquées, afin d’améliorer la taille des données à traiter et la complexité associée ; d’autre part des approches semi-supervisés ont été étudiées pour utiliser l’information supplémentaire apportée par les images non étiquetées lors de la classification. Ces éléments ont été analysé séparément et on été mis en œuvre ensemble sous la forme d’une nouvelle méthode par co-apprentissage temporelle. Finalement nous avons également exploré la question de l’invariance des descripteurs, en proposant l’utilisation d’un apprentissage invariant à la transformation spatiale, comme un autre réponse possible un manque de données annotées et à la variabilité visuelle.Ces méthodes ont été évaluées sur des séquences vidéo en environnement contrôlé accessibles publiquement pour évaluer le gain spécifique de chaque contribution. Ce travail a également été appliqué dans le cadre du projet IMMED, qui concerne l’observation et l’indexation d’activités de la vie quotidienne dans un objectif d’aide au diagnostic médical, à l’aide d’une caméra vidéo portée. Nous avons ainsi pu mettre en œuvre le dispositif d’acquisition vidéo portée, et montrer le potentiel de notre approche pour l’estimation de la localisation topologique sur un corpus présentant des conditions difficiles représentatives des données réelles. / Visual lifelog indexing by content has emerged as a high reward application. Enabled by the recent availability of miniaturized recording devices, the demand for automatic extraction of relevant information from wearable sensors generated content has grown. Among many other applications, indoor localization is one challenging problem to be addressed.Many standard solutions perform unreliably in indoors conditions or require significant intervention. In this thesis we address from the perspective of wearable video camera sensors using an image-based approach. The key contribution of this work is the development and the study of a location estimation system composed of diverse modules, which perform tasks ranging from low-level visual information extraction to final topological location estimation with the aid of automatic indexing algorithms. Within this framework, important contributions have been made by efficiently leveraging information brought by multiple visual features, unlabeled image data and the temporal continuity of the video.Early and late data fusion were considered, and shown to take advantage of the complementarities of multiple visual features describing the images. Due to the difficulty in obtaining annotated data in our context, semi-supervised approaches were investigated, to use unlabeled data as additional source of information, both for non-linear data-adaptive dimensionality reduction, and for improving classification. Herein we have developed a time-aware co-training approach that combines late data-fusion with the semi-supervised exploitation of both unlabeled data and time information. Finally, we have proposed to apply transformation invariant learning to adapt non-invariant descriptors to our localization framework.The methods have been tested on controlled publically available datasets to evaluate the gain of each contribution. This work has also been applied to the IMMED project, dealing with activity recognition and monitoring of the daily living using a wearable camera. In this context, the developed framework has been used to estimate localization on the real world IMMED project video corpus, which showed the potential of the approaches in such challenging conditions.

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The desire to enhance and make ourselves better is not a new one and it has continued to intrigue throughout the ages. Individuals have continued to seek ways to improve and enhance their well-being for example through nutrition, physical exercise, education and so on. Crucial to this improvement of their well-being is improving their ability to remember. Hence, people interested in improving their well-being, are often interested in memory as well. The rationale being that memory is crucial to our well-being. The desire to improve one’s memory then is almost certainly as old as the desire to improve one’s well-being. Traditionally, people have used different means in an attempt to enhance their memories: for example in learning through storytelling, studying, and apprenticeship. In remembering through practices like mnemonics, repetition, singing, and drumming. In retaining, storing and consolidating memories through nutrition and stimulants like coffee to help keep awake; and by external aids like notepads and computers. In forgetting through rituals and rites. Recent scientific advances in biotechnology, nanotechnology, molecular biology, neuroscience, and information technologies, present a wide variety of technologies to enhance many different aspects of human functioning. Thus, some commentators have identified human enhancement as central and one of the most fascinating subject in bioethics in the last two decades. Within, this period, most of the commentators have addressed the Ethical, Social, Legal and Policy (ESLP) issues in human enhancements as a whole as opposed to specific enhancements. However, this is problematic and recently various commentators have found this to be deficient and called for a contextualized case-by-case analysis to human enhancements for example genetic enhancement, moral enhancement, and in my case memory enhancement (ME). The rationale being that the reasons for accepting/rejecting a particular enhancement vary depending on the enhancement itself. Given this enormous variation, moral and legal generalizations about all enhancement processes and technologies are unwise and they should instead be evaluated individually. Taking this as a point of departure, this research will focus specifically on making a case for ME and in doing so assessing the ESLP implications arising from ME. My analysis will draw on the already existing literature for and against enhancement, especially in part two of this thesis; but it will be novel in providing a much more in-depth analysis of ME. From this perspective, I will contribute to the ME debate through two reviews that address the question how we enhance the memory, and through four original papers discussed in part three of this thesis, where I examine and evaluate critically specific ESLP issues that arise with the use of ME. In the conclusion, I will amalgamate all my contribution to the ME debate and suggest the future direction for the ME debate.