Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Characterising Place by Scene Depth

Turner and Penn introduced the notion of integration of isovist fields as a means to understand such fields syntactically - as a set of components with a structural relationship to a global whole (1999). This research was further refined to put forward the concept of visibility graph analysis (VGA) as a tool for architectural analysis (Turner, Doxa, O’sullivan, & Penn, 2001), which has become widely used. We suggest a complementary method of characterising place that does not make use of integration or a graph yet which allows - as visibility graph analysis does - discrete view points to be dimensioned in relation to a set of such viewpoints. In our method, Principal Component Analysis (PCA), a statistical technique, is employed to infer salient characteristics of a set of views and then to situate these component views within a low dimensional space in order to compare the extent to which each view corresponds to these characteristics. We demonstrate the method by reference to two distinct urban areas with differing spatial characteristics. Because PCA operates on vectors, order of the data has important implications. We consider some of these implications including view orientation and chirality (handedness) and assess the variance of results with regard to these factors

Data fluidity in DARIAH -- pushing the agenda forward

This paper provides both an update concerning the setting up of the European DARIAH infrastructure and a series of strong action lines related to the development of a data centred strategy for the humanities in the coming years. In particular we tackle various aspect of data management: data hosting, the setting up of a DARIAH seal of approval, the establishment of a charter between cultural heritage institutions and scholars and finally a specific view on certification mechanisms for data

Hyperspectral image analysis for questioned historical documents.

This thesis describes the application of spectroscopy and hyperspectral image processing to examine historical manuscripts and text. Major activities in palaeographic and manuscript studies include the recovery of illegible or deleted text, the minute analyses of scribal hands, the identification of inks and the segmentation and dating of text. This thesis describes how Hyperspectral Imaging (HSI), applied in a novel manner, can be used to perform quality text recovery, segmentation and dating of historical documents. The non-destructive optical imaging process of Spectroscopy is described in detail and how it can be used to assist historians and document experts in the exemption of aged manuscripts. This non-destructive optical method of analysis can distinguish subtle differences in the reflectance properties of the materials under study. Many historically significant documents from libraries such as the Royal Irish Academy and the Russell Library at the National University of Ireland, Maynooth, have been the selected for study using the hyperspectral imaging technique. Processing techniques have are described for the applications to the study of manuscripts in a poor state of conservation. The research provides a comprehensive overview of Hyperspectral Imaging (HSI) and associated statistical and analytical methods, and also an in-depth investigation of the practical implementation of such methods to aid document analysts. Specifically, we provide results from employing statistical analytical methods including principal component analysis (PCA), independent component analysis (ICA) and both supervised and automatic clustering methods to historically significant manuscripts and text VIII such as Leabhar na hUidhre, a 12th century Irish text which was subject to part-erasure and rewriting, a 16th Century pastedown cover, and a multi-ink example typical of that found in, for example, late medieval administrative texts such as Gttingen’s kundige bok. The purpose of which is to achieve an overall greater insight into the historical context of the document, which includes the recovery or enhancement of faded or illegible text or text lost through fading, staining, overwriting or other forms of erasure. In addition, we demonstrate prospect of distinguishing different ink-types, and furnishing us with details of the manuscript’s composition, all of which are refinements, which can be used to answer questions about date and provenance. This process marks a new departure for the study of manuscripts and may provide answer many long-standing questions posed by palaeographers and by scholars in a variety of disciplines. Furthermore, through text retrieval, it holds out the prospect of adding considerably to the existing corpus of texts and to providing very many new research opportunities for coming generations of scholars

Methods for Analysing Endothelial Cell Shape and Behaviour in Relation to the Focal Nature of Atherosclerosis

The aim of this thesis is to develop automated methods for the analysis of the spatial patterns, and the functional behaviour of endothelial cells, viewed under microscopy, with applications to the understanding of atherosclerosis. Initially, a radial search approach to segmentation was attempted in order to trace the cell and nuclei boundaries using a maximum likelihood algorithm; it was found inadequate to detect the weak cell boundaries present in the available data. A parametric cell shape model was then introduced to fit an equivalent ellipse to the cell boundary by matching phase-invariant orientation fields of the image and a candidate cell shape. This approach succeeded on good quality images, but failed on images with weak cell boundaries. Finally, a support vector machines based method, relying on a rich set of visual features, and a small but high quality training dataset, was found to work well on large numbers of cells even in the presence of strong intensity variations and imaging noise. Using the segmentation results, several standard shear-stress dependent parameters of cell morphology were studied, and evidence for similar behaviour in some cell shape parameters was obtained in in-vivo cells and their nuclei. Nuclear and cell orientations around immature and mature aortas were broadly similar, suggesting that the pattern of flow direction near the wall stayed approximately constant with age. The relation was less strong for the cell and nuclear length-to-width ratios. Two novel shape analysis approaches were attempted to find other properties of cell shape which could be used to annotate or characterise patterns, since a wide variability in cell and nuclear shapes was observed which did not appear to fit the standard parameterisations. Although no firm conclusions can yet be drawn, the work lays the foundation for future studies of cell morphology. To draw inferences about patterns in the functional response of cells to flow, which may play a role in the progression of disease, single-cell analysis was performed using calcium sensitive florescence probes. Calcium transient rates were found to change with flow, but more importantly, local patterns of synchronisation in multi-cellular groups were discernable and appear to change with flow. The patterns suggest a new functional mechanism in flow-mediation of cell-cell calcium signalling

Spatial and temporal background modelling of non-stationary visual scenes

PhDThe prevalence of electronic imaging systems in everyday life has become increasingly apparent in recent years. Applications are to be found in medical scanning, automated manufacture, and perhaps most significantly, surveillance. Metropolitan areas, shopping malls, and road traffic management all employ and benefit from an unprecedented quantity of video cameras for monitoring purposes. But the high cost and limited effectiveness of employing humans as the final link in the monitoring chain has driven scientists to seek solutions based on machine vision techniques. Whilst the field of machine vision has enjoyed consistent rapid development in the last 20 years, some of the most fundamental issues still remain to be solved in a satisfactory manner. Central to a great many vision applications is the concept of segmentation, and in particular, most practical systems perform background subtraction as one of the first stages of video processing. This involves separation of ‘interesting foreground’ from the less informative but persistent background. But the definition of what is ‘interesting’ is somewhat subjective, and liable to be application specific. Furthermore, the background may be interpreted as including the visual appearance of normal activity of any agents present in the scene, human or otherwise. Thus a background model might be called upon to absorb lighting changes, moving trees and foliage, or normal traffic flow and pedestrian activity, in order to effect what might be termed in ‘biologically-inspired’ vision as pre-attentive selection. This challenge is one of the Holy Grails of the computer vision field, and consequently the subject has received considerable attention. This thesis sets out to address some of the limitations of contemporary methods of background segmentation by investigating methods of inducing local mutual support amongst pixels in three starkly contrasting paradigms: (1) locality in the spatial domain, (2) locality in the shortterm time domain, and (3) locality in the domain of cyclic repetition frequency. Conventional per pixel models, such as those based on Gaussian Mixture Models, offer no spatial support between adjacent pixels at all. At the other extreme, eigenspace models impose a structure in which every image pixel bears the same relation to every other pixel. But Markov Random Fields permit definition of arbitrary local cliques by construction of a suitable graph, and 3 are used here to facilitate a novel structure capable of exploiting probabilistic local cooccurrence of adjacent Local Binary Patterns. The result is a method exhibiting strong sensitivity to multiple learned local pattern hypotheses, whilst relying solely on monochrome image data. Many background models enforce temporal consistency constraints on a pixel in attempt to confirm background membership before being accepted as part of the model, and typically some control over this process is exercised by a learning rate parameter. But in busy scenes, a true background pixel may be visible for a relatively small fraction of the time and in a temporally fragmented fashion, thus hindering such background acquisition. However, support in terms of temporal locality may still be achieved by using Combinatorial Optimization to derive shortterm background estimates which induce a similar consistency, but are considerably more robust to disturbance. A novel technique is presented here in which the short-term estimates act as ‘pre-filtered’ data from which a far more compact eigen-background may be constructed. Many scenes entail elements exhibiting repetitive periodic behaviour. Some road junctions employing traffic signals are among these, yet little is to be found amongst the literature regarding the explicit modelling of such periodic processes in a scene. Previous work focussing on gait recognition has demonstrated approaches based on recurrence of self-similarity by which local periodicity may be identified. The present work harnesses and extends this method in order to characterize scenes displaying multiple distinct periodicities by building a spatio-temporal model. The model may then be used to highlight abnormality in scene activity. Furthermore, a Phase Locked Loop technique with a novel phase detector is detailed, enabling such a model to maintain correct synchronization with scene activity in spite of noise and drift of periodicity. This thesis contends that these three approaches are all manifestations of the same broad underlying concept: local support in each of the space, time and frequency domains, and furthermore, that the support can be harnessed practically, as will be demonstrated experimentally