Clustering-based Methods for Fast Epitome Generation

International audienceThis paper deals with epitome generation, mainly dedicated here to image coding applications. Existing approaches are known to be memory and time consuming due to exhaustive self-similarities search within the image for each non-overlapping block. We propose here a novel approach for epitome construction that first groups close patches together. In a second time the self-similarities search is performed for each group. By limiting the number of exhaustive searches we limit the memory occupation and the processing time. Results show that interesting complexity reduction can be achieved while keeping a good epitome quality (down to 18.08 % of the original memory occupation and 41.39 % of the original processing time)

Techniques de codage d'images basées représentations parcimonieuses de scènes et prédiction spatiale multi-patches

In recent years, video compression eld has increased signicantly since the apparitionof H.264/AVC standard and of its successor HEVC. Spatial prediction in these standardsare based on the unidirectional propagation of neighboring pixels. Although very effectiveto extend pattern with the same characteristics, this prediction has limited performances toextrapolate complex textures. This thesis aims at exploring new spatial prediction schemesto improve the current intra prediction techniques, by extending these local schemes toglobal, multidimensional and multi-patches schemes. A hybrid prediction method based ontemplate and block matching is first investigated. This hybrid approach is then extended tomulti-patchs-based prediction of type "Neighbor Embedding" (NE). The other part of thisthesis is dedicated to the study of epitome image within the scope of image compression.The idea is to exploit spatial redundancies in the original image in order to rst extracta summary image containing the texture patches the most representative of the image,and then use this compacted representation to rebuild the original image. The conceptof epitome has been incorporated in two compression schemes, one of these algorithms isin rupture with the traditional techniques since the image blocks are processed, both atencoder and decoder sides, in a spatial order that depends on the image content and this inthe interest of propagating image structures. In this last compression algorithm, extendedH.264 Intra directional prediction modes and advanced multi-patches prediction methodshave been also included. These different solutions have been integrated in a H.264/AVCencoder in order to assess their coding performances with respect to H.264 intra modesand the state of the art relative to these dierent techniques.Au cours de ces dernières années, le domaine de la compression vidéo a connu un essorconsidérable avec le standard H.264/AVC et l'arrivée de son successeur HEVC. La prédictionspatiale de ces standards repose sur la propagation unidirectionnelle de pixels voisins.Bien que très efficace pour étendre des motifs répondants aux mêmes caractéristiques,cette prédiction présente des performances limitées lorsqu'il s'agit de propager des texturescomplexes. Cette thèse vise à explorer de nouveaux schémas de prédiction spatiale afind'améliorer les techniques actuelles de prédiction intra, en étendant ces schémas locaux etmonodimensionnels à des schémas globaux, multidimensionnels et multi-patches. Une première méthode de prédiction hybride intégrant correspondance de bloc et correspondancede gabarit (template) a été investiguée. Cette approche hybride a ensuite été étendue enprédiction multi-patches de type "neighbor embedding" (NE). L'autre partie de la thèseest dédiée à l'étude des épitomes dans un contexte de compression d'images. L'idée estd'exploiter la redondance spatiale de l'image d'origine afin d'extraire une image résumécontenant les patches de texture les plus représentatifs de l'image, puis ensuite utilisercette représentation compacte pour reconstruire l'image de départ. Ce concept d'épitome aété intégré dans deux schémas de compression, l'un de ces algorithmes s'avère vraiment enrupture avec les techniques traditionnelles dans la mesure où les blocs de l'image sont traités, à l'encodeur et au décodeur, dans un ordre spatial qui dépend du contenu et cela dansun souci de propagation des structures de l'image. Dans ce dernier algorithme de compression,des modes de prédiction directionnelle intra H.264 étendus et des méthodes avancéesde prédiction multi-patches y ont été également introduits. Ces différentes solutions ont étéintégrées dans un encodeur de type H.264/AVC afin d'évaluer leurs performances de codagepar rapport aux modes intra H.264 et à l'état de l'art relatif à ces différentes techniques

WATCHING PEOPLE: ALGORITHMS TO STUDY HUMAN MOTION AND ACTIVITIES

Nowadays human motion analysis is one of the most active research topics in Computer Vision and it is receiving an increasing attention from both the industrial and scientific communities. The growing interest in human motion analysis is motivated by the increasing number of promising applications, ranging from surveillance, human–computer interaction, virtual reality to healthcare, sports, computer games and video conferencing, just to name a few. The aim of this thesis is to give an overview of the various tasks involved in visual motion analysis of the human body and to present the issues and possible solutions related to it. In this thesis, visual motion analysis is categorized into three major areas related to the interpretation of human motion: tracking of human motion using virtual pan-tilt-zoom (vPTZ) camera, recognition of human motions and human behaviors segmentation. In the field of human motion tracking, a virtual environment for PTZ cameras (vPTZ) is presented to overcame the mechanical limitations of PTZ cameras. The vPTZ is built on equirectangular images acquired by 360° cameras and it allows not only the development of pedestrian tracking algorithms but also the comparison of their performances. On the basis of this virtual environment, three novel pedestrian tracking algorithms for 360° cameras were developed, two of which adopt a tracking-by-detection approach while the last adopts a Bayesian approach. The action recognition problem is addressed by an algorithm that represents actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. The proposed method learns a codebook of frequent sequential patterns by means of an apriori-like algorithm. An action is then represented with a Bag-of-Frequent-Sequential-Patterns approach. In the last part of this thesis a methodology to semi-automatically annotate behavioral data given a small set of manually annotated data is presented. The resulting methodology is not only effective in the semi-automated annotation task but can also be used in presence of abnormal behaviors, as demonstrated empirically by testing the system on data collected from children affected by neuro-developmental disorders

Why teach a fish to swim? A design-based research study incorporating social media into the professional writing curriculum to shape professional practice and identity

Research has found that professional communicators are not prepared for the challenges that social media presents and face a number of barriers due to a lack of social media knowledge and skills. Correspondingly, higher education has failed to include enough social media and online content to provide learners with the necessary skills for professional practice. Furthermore, the neoliberal objective to shape a flexible workforce has engendered a new form of professionalism that tasks individuals with developing an incorporated branded self. Within the framework of the higher education curriculum, social media can perform two roles for learners: foster workforce competences and provide an authentic community of practice to comodify their brand. The issue for educators is that no comprehensive studies have fully examined the incorporation of a social media component into a professional writing course, identifying the barriers, skills, and processes that facilitate or foster the professionalization of the tools for learners and enable them to use these technologies both appropriately and strategically. This dissertation employed a design-based research methodology to systematically study how to design an effective learning environment for the integration of social media technologies and addressed the following research questions: • What problems might educators face when integrating social media practices into the curriculum? • How can social media technologies facilitate professional identity formation to bridge the transition from the everyday practices of learners to professional practices? The study spanned the time period 2012-2016 and involved the developing, testing, investigating, and refining of a yearlong professional writing course, which included the tools, curriculum, activities, software, and theoretical constructs for the course design (Reeves, 2006, p. 58). The results indicated that students lacked agency on social networks and required guidance when articulating modes of online authenticity. The final iteration of the course design effectively produced a virtual community of practice, as measured through learning analytics, and provided a means to shape professional social media practices and foster professional identity