Three-Dimensional Motion Estimation Using Shading Information in Multiple Frames

A new formulation for recovering the structure and motion parameters of a moving patch using both motion and shading information is presented. It is based on a new differential constraint equation (FICE) that links the spatiotemporal gradients of irradiance to the motion and structure parameters and the temporal variations of the surface shading. The FICE separates the contribution to the irradiance spatiotemporal gradients of the gradients due to texture from those due to shading and allows the FICE to be used for textured and textureless surface. The new approach, combining motion and shading information, leads directly to two different contributions: it can compensate for the effects of shading variations in recovering the shape and motion; and it can exploit the shading/illumination effects to recover motion and shape when they cannot be recovered without it. The FICE formulation is also extended to multiple frames.

Image segmentation integrating colour, texture and boundary information

La tesis se centra en la Visión por Computador y, más concretamente, en la segmentación de imágenes, la cual es una de las etapas básicas en el análisis de imágenes y consiste en la división de la imagen en un conjunto de regiones visualmente distintas y uniformes considerando su intensidad, color o textura. Se propone una estrategia basada en el uso complementario de la información de región y de frontera durante el proceso de segmentación, integración que permite paliar algunos de los problemas básicos de la segmentación tradicional. La información de frontera permite inicialmente identificar el número de regiones presentes en la imagen y colocar en el interior de cada una de ellas una semilla, con el objetivo de modelar estadísticamente las características de las regiones y definir de esta forma la información de región. Esta información, conjuntamente con la información de frontera, es utilizada en la definición de una función de energía que expresa las propiedades requeridas a la segmentación deseada: uniformidad en el interior de las regiones y contraste con las regiones vecinas en los límites. Un conjunto de regiones activas inician entonces su crecimiento, compitiendo por los píxeles de la imagen, con el objetivo de optimizar la función de energía o, en otras palabras, encontrar la segmentación que mejor se adecua a los requerimientos exprsados en dicha función. Finalmente, todo esta proceso ha sido considerado en una estructura piramidal, lo que nos permite refinar progresivamente el resultado de la segmentación y mejorar su coste computacional. La estrategia ha sido extendida al problema de segmentación de texturas, lo que implica algunas consideraciones básicas como el modelaje de las regiones a partir de un conjunto de características de textura y la extracción de la información de frontera cuando la textura es presente en la imagen. Finalmente, se ha llevado a cabo la extensión a la segmentación de imágenes teniendo en cuenta las propiedades de color y textura. En este sentido, el uso conjunto de técnicas no-paramétricas de estimación de la función de densidad para la descripción del color, y de características textuales basadas en la matriz de co-ocurrencia, ha sido propuesto para modelar adecuadamente y de forma completa las regiones de la imagen. La propuesta ha sido evaluada de forma objetiva y comparada con distintas técnicas de integración utilizando imágenes sintéticas. Además, se han incluido experimentos con imágenes reales con resultados muy positivos.

The temporal impulse response underlying saccadic decisions

Models of perceptual decision making often assume that sensory evidence is accumulated over time in favor of the various possible decisions, until the evidence in favor of one of them outweighs the evidence for the others. Saccadic eye movements are among the most frequent perceptual decisions that the human brain performs. We used stochastic visual stimuli to identify the temporal impulse response underlying saccadic eye movement decisions. Observers performed a contrast search task, with temporal variability in the visual signals. In experiment 1, we derived the temporal filter observers used to integrate the visual information. The integration window was restricted to the first similar to 100 ms after display onset. In experiment 2, we showed that observers cannot perform the task if there is no useful information to distinguish the target from the distractor within this time epoch. We conclude that (1) observers did not integrate sensory evidence up to a criterion level, (2) observers did not integrate visual information up to the start of the saccadic dead time, and (3) variability in saccade latency does not correspond to variability in the visual integration period. Instead, our results support a temporal filter model of saccadic decision making. The temporal impulse response identified by our methods corresponds well with estimates of integration times of V1 output neurons.

Temporal interval production and short-term memory

Interference with time estimation from concurrent nontemporal processing has been shown to depend on the short-term memory requirements of the concurrent task (Fortin Breton, 1995; Fortin, Rousseau, Bourque, & Kirouac, 1993). In particular, it has been claimed that active processing of information in short-term memory produces interference, whereas simply maintaining information does not. Here, four experiments are reported in which subjects were trained to produce a 2,500-msec interval and then perform concurrent memory tasks. Interference with timing was demonstrated for concurrent memory tasks involving only maintenance. In one experiment, increasing set size in a pitch memory task systematically lengthened temporal production. Two further experiments suggested that this was due to a specific interaction between the short-term memory requirements of the pitch task and those of temporal production. In the final experiment, subjects performed temporal production while concurrently remembering the durations of a set of tones. Interference with interval production was comparable to that produced by the pitch memory task. Results are discussed in terms of a pacemaker-counter model of temporal processing, in which the counter component is supported by short-term memory.

Robustness of mutual information to inter-subject variability for automatic artefact removal from EEG

The externally recorded electroencephalogram (EEG) is contaminated with signals that do not originate from the brain, collectively known as artefacts. Thus, EEG signals must be cleaned prior to any further analysis. In particular, if the EEG is to be used in online applications such as Brain-Computer Interfaces (BCIs) the removal of artefacts must be performed in an automatic manner. This paper investigates the robustness of Mutual Information based features to inter-subject variability for use in an automatic artefact removal system. The system is based on the separation of EEG recordings into independent components using a temporal ICA method, RADICAL, and the utilisation of a Support Vector Machine for classification of the components into EEG and artefact signals. High accuracy and robustness to inter-subject variability is achieved.

Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks

Automatic indexing and retrieval of digital data poses major challenges. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions, or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. For a number of years research has been ongoing in the field of ontological engineering with the aim of using ontologies to add such (meta) knowledge to information. In this paper, we describe the architecture of a system (Dynamic REtrieval Analysis and semantic metadata Management (DREAM)) designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval. The DREAM Demonstrator has been evaluated as deployed in the film post-production phase to support the process of storage, indexing and retrieval of large data sets of special effects video clips as an exemplar application domain. This paper provides its performance and usability results and highlights the scope for future enhancements of the DREAM architecture which has proven successful in its first and possibly most challenging proving ground, namely film production, where it is already in routine use within our test bed Partners' creative processes. (C) 2009 Published by Elsevier B.V.

Capturing temporality and intentionality in information systems

Information modelling is a topic that has been researched a great deal, but still many questions around it have not been solved. An information model is essential in the design of a database which is the core of an information system. Currently most of databases only deal with information that represents facts, or asserted information. The ability of capturing semantic aspect has to be improved, and yet other types, such as temporal and intentional information, should be considered. Semantic Analysis, a method of information modelling, has offered a way to handle various aspects of information. It employs the domain knowledge and communication acts as sources of information modelling. It lends itself to a uniform structure whereby semantic, temporal and intentional information can be captured, which builds a sound foundation for building a semantic temporal database.

Keyphrase extraction by synonym analysis of n-grams for e-journals categorisation

Automatic keyword or keyphrase extraction is concerned with assigning keyphrases to documents based on words from within the document. Previous studies have shown that in a significant number of cases author-supplied keywords are not appropriate for the document to which they are attached. This can either be because they represent what the author believes the paper is about not what it actually is, or because they include keyphrases which are more classificatory than explanatory e.g., “University of Poppleton” instead of “Knowledge Discovery in Databases”. Thus, there is a need for a system that can generate appropriate and diverse range of keyphrases that reflect the document. This paper proposes a solution that examines the synonyms of words and phrases in the document to find the underlying themes, and presents these as appropriate keyphrases. The primary method explores taking n-grams of the source document phrases, and examining the synonyms of these, while the secondary considers grouping outputs by their synonyms. The experiments undertaken show the primary method produces good results and that the secondary method produces both good results and potential for future work.

Automatic keyphrase extraction: a comparison of methods

There are many published methods available for creating keyphrases for documents. Previous work in the field has shown that in a significant proportion of cases author selected keyphrases are not appropriate for the document they accompany. This requires the use of such automated methods to improve the use of keyphrases. Often the keyphrases are not updated when the focus of a paper changes or include keyphrases that are more classificatory than explanatory. The published methods are all evaluated using different corpora, typically one relevant to their field of study. This not only makes it difficult to incorporate the useful elements of algorithms in future work but also makes comparing the results of each method inefficient and ineffective. This paper describes the work undertaken to compare five methods across a common baseline of six corpora. The methods chosen were term frequency, inverse document frequency, the C-Value, the NC-Value, and a synonym based approach. These methods were compared to evaluate performance and quality of results, and to provide a future benchmark. It is shown that, with the comparison metric used for this study Term Frequency and Inverse Document Frequency were the best algorithms, with the synonym based approach following them. Further work in the area is required to determine an appropriate (or more appropriate) comparison metric.

Spatial and temporal patterns in antimicrobial resistance of Salmonella Typhimurium in cattle in England and Wales

Salmonella is the second most commonly reported human foodborne pathogen in England and Wales, and antimicrobial-resistant strains of Salmonella are an increasing problem in both human and veterinary medicine. In this work we used a generalized linear spatial model to estimate the spatial and temporal patterns of antimicrobial resistance in Salmonella Typhimurium in England and Wales. Of the antimicrobials considered we found a common peak in the probability that an S. Typhimurium incident will show resistance to a given antimicrobial in late spring and in mid to late autumn; however, for one of the antimicrobials (streptomycin) there was a sharp drop, over the last 18 months of the period of investigation, in the probability of resistance. We also found a higher probability of resistance in North Wales which is consistent across the antimicrobials considered. This information contributes to our understanding of the epidemiology of antimicrobial resistance in Salmonella.

Epoch-specific functional networks involved in working memory

Working memory (WM) is not a unitary construct. There are distinct processes involved in encoding information, maintaining it on-line, and using it to guide responses. The anatomical configurations of these processes are more accurately analyzed as functionally connected networks than collections of individual regions. In the current study we analyzed event-related functional magnetic resonance imaging (fMRI) data from a Sternberg Item Recognition Paradigm WM task using a multivariate analysis method that allowed the linking of functional networks to temporally-separated WM epochs. The length of the delay epochs was varied to optimize isolation of the hemodynamic response (HDR) for each task epoch. All extracted functional networks displayed statistically significant sensitivity to delay length. Novel information extracted from these networks that was not apparent in the univariate analysis of these data included involvement of the hippocampus in encoding/probe, and decreases in BOLD signal in the superior temporal gyrus (STG), along with default-mode regions, during encoding/delay. The bilateral hippocampal activity during encoding/delay fits with theoretical models of WM in which memoranda held across the short term are activated long-term memory representations. The BOLD signal decreases in the STG were unexpected, and may reflect repetition suppression effects invoked by internal repetition of letter stimuli. Thus, analysis methods focusing on how network dynamics relate to experimental conditions allowed extraction of novel information not apparent in univariate analyses, and are particularly recommended for WM experiments for which task epochs cannot be randomized.

The link between temporal attention and emotion: a playground for psychology, neuroscience, and plausible artificial neural networks

In this paper, we will address the endeavors of three disciplines, Psychology, Neuroscience, and Artificial Neural Network (ANN) modeling, in explaining how the mind perceives and attends information. More precisely, we will shed some light on the efforts to understand the allocation of attentional resources to the processing of emotional stimuli. This review aims at informing the three disciplines about converging points of their research and to provide a starting point for discussion.

TRCM: a methodology for temporal analysis of evolving concepts in Twitter

The Twitter network has been labelled the most commonly used microblogging application around today. With about 500 million estimated registered users as of June, 2012, Twitter has become a credible medium of sentiment/opinion expression. It is also a notable medium for information dissemination; including breaking news on diverse issues since it was launched in 2007. Many organisations, individuals and even government bodies follow activities on the network in order to obtain knowledge on how their audience reacts to tweets that affect them. We can use postings on Twitter (known as tweets) to analyse patterns associated with events by detecting the dynamics of the tweets. A common way of labelling a tweet is by including a number of hashtags that describe its contents. Association Rule Mining can find the likelihood of co-occurrence of hashtags. In this paper, we propose the use of temporal Association Rule Mining to detect rule dynamics, and consequently dynamics of tweets. We coined our methodology Transaction-based Rule Change Mining (TRCM). A number of patterns are identifiable in these rule dynamics including, new rules, emerging rules, unexpected rules and ?dead' rules. Also the linkage between the different types of rule dynamics is investigated experimentally in this paper.