An Incremental Navigation Localization Methodology for Application to Semi-Autonomous Mobile Robotic Platforms to Assist Individuals Having Severe Motor Disabilities.

In the present work, the author explores the issues surrounding the design and development of an intelligent wheelchair platform incorporating the semi-autonomous system paradigm, to meet the needs of individuals with severe motor disabilities. The author presents a discussion of the problems of navigation that must be solved before any system of this type can be instantiated, and enumerates the general design issues that must be addressed by the designers of systems of this type. This discussion includes reviews of various methodologies that have been proposed as solutions to the problems considered. Next, the author introduces a new navigation method, called Incremental Signature Recognition (ISR), for use by semi-autonomous systems in structured environments. This method is based on the recognition, recording, and tracking of environmental discontinuities: sensor reported anomalies in measured environmental parameters. The author then proposes a robust, redundant, dynamic, self-diagnosing sensing methodology for detecting and compensating for hidden failures of single sensors and sensor idiosyncrasies. This technique is optimized for the detection of spatial discontinuity anomalies. Finally, the author gives details of an effort to realize a prototype ISR based system, along with insights into the various implementation choices made

A study in the cognition of individuals’ identity: Solving the problem of singular cognition in object and agent tracking

This article compares the ability to track individuals lacking mental states with the ability to track intentional agents. It explains why reference to individuals raises the problem of explaining how cognitive agents track unique individuals and in what sense reference is based on procedures of perceptual-motor and epistemic tracking. We suggest applying the notion of singular-files from theories in perception and semantics to the problem of tracking intentional agents. In order to elucidate the nature of agent-files, three views of the relation between object- and agent-tracking are distinguished: the Independence, Deflationary and Organism-Dependence Views. The correct view is argued to be the latter, which states that perceptual and epistemic tracking of a unique human organism requires tracking both its spatio-temporal object-properties and its agent-properties

Dynamics of perceptual learning in visual search

The present work is concerned with a phenomenon referred to as contextual cueing. In visual search, if a searched-for target object is consistently encountered within a stable spatial arrangement of distractor objects, detecting the target becomes more efficient over time, relative to non-repeated, random arrangements. This effect is attributed to learned target-distractor spatial associations stored in long-term memory, which expedite visual search. This Thesis investigates four aspects of contextual cueing: Study 1 tackled the implicit-explicit debate of contextual cueing from a new perspective. Previous studies tested explicit access to learned displays by applying a recognition test, asking observers whether they have seen a given display in the previous search task. These tests, however, typically yield mixed findings and there is an on-going controversy whether contextual cueing can be described as an implicit or an explicit effect. The current study applied the new perspective of metacognition to contextual cueing and combined a contextual cueing task with metacognitive ratings about the clarity of the visual experience, either of the display configuration or the target stimulus. Bayesian analysis revealed that there was an effect of repeated context on metacognitive sensitivity for configuration, but not target, ratings. It was concluded that effects of contextual memory on metacognition are content-specific and lead to increased metacognitive access to the display configuration, but not to the target stimulus. The more general implication is that from the perspective of metacognition, contextual cueing can be considered as an explicit effect. Study 2 aimed at testing how explicit knowledge affects memory-guided visual search. Two sets of search displays were shown to participants: explicit and implicit displays. Explicit displays were introduced prior to the search experiment, in a dedicated learning session, and observers should deliberately learn these displays. Implicit displays, on the other hand, were first shown in the search experiment and learning was incidental through repeated exposure to these displays. Contextual cueing arising from explicit and implicit displays was assessed relative to a baseline condition of non-repeated displays. The results showed a standard contextual cueing effect for explicit displays and, interestingly, a negative cueing effect for implicit displays. Recognition performance was above chance for both types of repeated displays; however, it was higher for explicit displays. This pattern of results confirmed – in part – the predictions of a single memory model of attention-moderated associative learning, in which different display types compete for behavior and explicit representations block the retrieval of implicit representations. Study 3 investigates interactions between long-term contextual memory with short-term perceptual hypotheses. Both types of perceptual memory share high similarities with respect to their content, therefore the hypothesis was formulated that they share a common memory resource. In three experiments of interrupted search with repeated and non-repeated displays, it was shown that contextual cueing expedites performance in interrupted search; however, there was no interaction of contextual cueing with the generation or the confirmation of perceptual hypotheses. Rather, the analysis of fixational eye movements showed that long-term memory exerts its influence on search performance upon the first glance of a given display, essentially affecting the starting point of the search process. The behavior of approaching the target stimulus is then a product of generating and confirming perceptual hypotheses with these processes being unaffected by long-term contextual memory. It was concluded that long-term and short-term memory representations of the same search display are independent and exhibit additive effects on search performance. Study 4 is concerned with the effects of reward on perceptual learning. It was argued that rewarding repeated displays in a contextual cueing paradigm leads to an acceleration of the learning effect; however, it was not considered whether reward also has an effect in non-repeated displays. In these displays, at least the target position is kept constant while distractor configurations are random across repetitions. Usually this is done in order to account for target position-specific probability learning in contextual cueing. However, it is possible that probability learning itself is modulated by reward. The current experiment introduced high or low reward to repeated and importantly, also non-repeated displays. It was shown that reward had a huge effect on non-repeated displays, indicating that rewarding certain target positions, irrespective of the distractor layout, facilitates RT performance. Interestingly, reward effects were even larger for non-repeated compared to repeated displays. It was concluded that reward has a strong effect on probability-, and not context learning

Precis of neuroconstructivism: how the brain constructs cognition

Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment

Local, Semi-Local and Global Models for Texture, Object and Scene Recognition

This dissertation addresses the problems of recognizing textures, objects, and scenes in photographs. We present approaches to these recognition tasks that combine salient local image features with spatial relations and effective discriminative learning techniques. First, we introduce a bag of features image model for recognizing textured surfaces under a wide range of transformations, including viewpoint changes and non-rigid deformations. We present results of a large-scale comparative evaluation indicating that bags of features can be effective not only for texture, but also for object categization, even in the presence of substantial clutter and intra-class variation. We also show how to augment the purely local image representation with statistical co-occurrence relations between pairs of nearby features, and develop a learning and classification framework for the task of classifying individual features in a multi-texture image. Next, we present a more structured alternative to bags of features for object recognition, namely, an image representation based on semi-local parts, or groups of features characterized by stable appearance and geometric layout. Semi-local parts are automatically learned from small sets of unsegmented, cluttered images. Finally, we present a global method for recognizing scene categories that works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting spatial pyramid representation demonstrates significantly improved performance on challenging scene categorization tasks

Recent Developments in Video Surveillance

With surveillance cameras installed everywhere and continuously streaming thousands of hours of video, how can that huge amount of data be analyzed or even be useful? Is it possible to search those countless hours of videos for subjects or events of interest? Shouldn’t the presence of a car stopped at a railroad crossing trigger an alarm system to prevent a potential accident? In the chapters selected for this book, experts in video surveillance provide answers to these questions and other interesting problems, skillfully blending research experience with practical real life applications. Academic researchers will find a reliable compilation of relevant literature in addition to pointers to current advances in the field. Industry practitioners will find useful hints about state-of-the-art applications. The book also provides directions for open problems where further advances can be pursued

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Event-Related Potentials Reveal Rapid Verification of Predicted Visual Input

Human information processing depends critically on continuous predictions about upcoming events, but the temporal convergence of expectancy-based top-down and input-driven bottom-up streams is poorly understood. We show that, during reading, event-related potentials differ between exposure to highly predictable and unpredictable words no later than 90 ms after visual input. This result suggests an extremely rapid comparison of expected and incoming visual information and gives an upper temporal bound for theories of top-down and bottom-up interactions in object recognition