Spatiotemporal visual analysis of human actions

In this dissertation we propose four methods for the recognition of human activities. In all four of them, the representation of the activities is based on spatiotemporal features that are automatically detected at areas where there is a significant amount of independent motion, that is, motion that is due to ongoing activities in the scene. We propose the use of spatiotemporal salient points as features throughout this dissertation. The algorithms presented, however, can be used with any kind of features, as long as the latter are well localized and have a well-defined area of support in space and time. We introduce the utilized spatiotemporal salient points in the first method presented in this dissertation. By extending previous work on spatial saliency, we measure the variations in the information content of pixel neighborhoods both in space and time, and detect the points at the locations and scales for which this information content is locally maximized. In this way, an activity is represented as a collection of spatiotemporal salient points. We propose an iterative linear space-time warping technique in order to align the representations in space and time and propose to use Relevance Vector Machines (RVM) in order to classify each example into an action category. In the second method proposed in this dissertation we propose to enhance the acquired representations of the first method. More specifically, we propose to track each detected point in time, and create representations based on sets of trajectories, where each trajectory expresses how the information engulfed by each salient point evolves over time. In order to deal with imperfect localization of the detected points, we augment the observation model of the tracker with background information, acquired using a fully automatic background estimation algorithm. In this way, the tracker favors solutions that contain a large number of foreground pixels. In addition, we perform experiments where the tracked templates are localized on specific parts of the body, like the hands and the head, and we further augment the tracker’s observation model using a human skin color model. Finally, we use a variant of the Longest Common Subsequence algorithm (LCSS) in order to acquire a similarity measure between the resulting trajectory representations, and RVMs for classification. In the third method that we propose, we assume that neighboring salient points follow a similar motion. This is in contrast to the previous method, where each salient point was tracked independently of its neighbors. More specifically, we propose to extract a novel set of visual descriptors that are based on geometrical properties of three-dimensional piece-wise polynomials. The latter are fitted on the spatiotemporal locations of salient points that fall within local spatiotemporal neighborhoods, and are assumed to follow a similar motion. The extracted descriptors are invariant in translation and scaling in space-time. Coupling the neighborhood dimensions to the scale at which the corresponding spatiotemporal salient points are detected ensures the latter. The descriptors that are extracted across the whole dataset are subsequently clustered in order to create a codebook, which is used in order to represent the overall motion of the subjects within small temporal windows.Finally,we use boosting in order to select the most discriminative of these windows for each class, and RVMs for classification. The fourth and last method addresses the joint problem of localization and recognition of human activities depicted in unsegmented image sequences. Its main contribution is the use of an implicit representation of the spatiotemporal shape of the activity, which relies on the spatiotemporal localization of characteristic ensembles of spatiotemporal features. The latter are localized around automatically detected salient points. Evidence for the spatiotemporal localization of the activity is accumulated in a probabilistic spatiotemporal voting scheme. During training, we use boosting in order to create codebooks of characteristic feature ensembles for each class. Subsequently, we construct class-specific spatiotemporal models, which encode where in space and time each codeword ensemble appears in the training set. During testing, each activated codeword ensemble casts probabilistic votes concerning the spatiotemporal localization of the activity, according to the information stored during training. We use a Mean Shift Mode estimation algorithm in order to extract the most probable hypotheses from each resulting voting space. Each hypothesis corresponds to a spatiotemporal volume which potentially engulfs the activity, and is verified by performing action category classification with an RVM classifier

Eugenics in the house: Modernism, architecture and eugenics and the production of Kensal House in the UK during the interwar period

Kensal House, a working-class housing development in West London became the beacon of Modernist housing schemes to be produced in Britain in the period between the First and the Second World War. Privately funded, by the Gas Company and realised mainly by the collaboration of two individuals, the architect Maxwell Fry and the housing consultant Elizabeth Denby, it was destined to become the greatest example for the use of gas in domestic environments at the same time as it will provide a functional, efficient and hygienic environment to the 68 families that will be rehoused there following slum clearance. Moreover, its programme included unique provisions for social interaction between the residents and a revolutionary for the period Nursery school. At a period where Britain faces difficult times ahead, with the quality of the population significantly dropping, and financial problems looming in the horizon, Kensal House was faithful to the nation's eugenics interests. Its creation also marked a shift in eugenic practices in the country, a shift that proclaimed the will for an evolutionary environment for all. Looking at Kensal House, through the ideas of that period's leading eugenist, Julien Huxley, this analysis points at the similar goals of Modernist housing design and eugenics ideology for a scientifically constructed Utopia and questions the scheme's creation using Foucault's notion of biopower to critically approach the relation between Kensal House and eugenics of every type

Functional Inequalities and Heat Kernel Asymptotics on Some Classes of Singular Riemannian Manifolds

This thesis consists of two parts. In the first part we are focusing on stratified pseudomanifolds equipped with an iterated edge metric. More specifically, in Chapter 1 we give the basic definitions and review some basic constructions concerning stratified spaces and iterated edge metrics. Furthermore we introduce the notion of edge vector fields and weighted Sobolev spaces which naturally arise in these spaces, and prove some of their properties. In Chapter 2 we are focusing on stratified pseudomanifolds of depth 1, the so called simple edge spaces. We introduce Sobolev spaces and we compare them with the weighted Sobolev spaces we previously defined. Furthermore, by taking into account the special structure of simple edge spaces we prove the validity of the classical functional inequalities (Sobolev, Poincare, Sobolev-Poincare). Moreover, we examine the existence of appropriate cut-off functions and as an application we obtain an optimality result on the B-constant of the Sobolev inequality. In the second part of the thesis we are focusing on the Dirichlet heat kernel and it's asymptotics as t → 0. More precisely, in Chapter 3 we consider the case of compact manifolds with corners satisfying a specific assumption on the metric. Under this assumption we construct a heat calculus that contains information about the asymptotic behaviour and examine it's properties. After elaborating on this, we prove that the Dirichlet heat kernel belongs in this calculus and therefore we are able to obtain a complete asymptotic expansion as t → 0

Spatiotemporal salient points for visual recognition of human actions

This paper addresses the problem of human action recognition by introducing a sparse representation of image sequences as a collection of spatiotemporal events that are localized at points that are salient both in space and time. We detect the spatiotemporal salient points by measuring the variations in the information content of pixel neighborhoods not only in space but also in time. We introduce an appropriate distance metric between two collections of spatiotemporal salient points that is based on the Chamfer distance and an iterative linear time warping technique that deals with time expansion or time compression issues. We propose a classification scheme that is based on Relevance Vector Machines and on the proposed distance measure. We present results on real image sequences from a small database depicting people performing 19 aerobic exercises

FTIR STUDY OF TWO DIFFERENT LIGNITE LITHOTYPES FROM NEOCENE ACHLADA LIGNITE DEPOSITS IN NW GREECE

The FTIR spectra for both Neogene xylite and matrix lignite samples from Achlada NW Greece show significant differences, which are mainly evident in aliphatic stretching region (3000-2800 cm-1) where the intensities of the vibrations are reduced in matrix lignite lithotype compared to xylite one. The intense bands in the region 3402-3416 cm-1 are attributed to -OH stretching of H2O and phenol groups. The bands at ~3697 cm-1 and ~3623 cm-1 as well as at ~538 cm-1 and 470 cm-1, which are more evident in the FTIR spectra of matrix lignite, are attributed to higher content of clay minerals in the samples of this lithotype. The stretching vibration appears at ~1032 cm-1 is intense in all matrix lignite samples and it is broadening in the xylite ones. The FTIR spectra of all samples confirm the progressive elimination of aliphatic vibrations from xylite lithotype to matrix lignite one and the appearance of clay minerals in the latter. As a whole the FTIR spectra of both xylite and matrix lignite confirm the significant differences between these two lignite lithotypes

Optimization of human mesenchymal stem cell manufacturing: the effects of animal/xeno-free media.

Due to their immunosuppressive properties, mesenchymal stem cells (MSC) have been evaluated for the treatment of immunological diseases. However, the animal-derived growth supplements utilized for MSC manufacturing may lead to clinical complications. Characterization of alternative media formulations is imperative for MSC therapeutic application. Human BMMSC and AdMSC were expanded in media supplemented with either human platelet lysates (HPL), serum-free media/xeno-free FDA-approved culture medium (SFM/XF), or fetal bovine serum (FBS) and the effects on their properties were investigated. The immunophenotype of resting and IFN-γ primed BMMSC and AdMSC remained unaltered in all media. Both HPL and SFM/XF increased the proliferation of BMMSC and AdMSC. Expansion of BMMSC and AdMSC in HPL increased their differentiation, compared to SFM/XF and FBS. Resting BMMSC and AdMSC, expanded in FBS or SFM/XF, demonstrated potent immunosuppressive properties in both non-primed and IFN-γ primed conditions, whereas HPL-expanded MSC exhibited diminished immunosuppressive properties. Finally, IFN-γ primed BMMSC and AdMSC expanded in SFM/XF and HPL expressed attenuated levels of IDO-1 compared to FBS. Herein, we provide strong evidence supporting the use of the FDA-approved SFM/XF medium, in contrast to the HPL medium, for the expansion of MSC towards therapeutic applications