Investigations On Human Perceptual Maps Using A Stereo-Vision Mobile Robot

Spatial cognition is a branch of cognitive psychology concerning the acquisition, organization, utilization, and revision of knowledge about spatial environments. A new computational theory of human spatial cognitive mapping has been proposed in the literature, and analyzed using a laser-based mobile robot. In contrast with the well-established SLAM (Simultaneous Localization and Mapping) approach that creates a precise and complete map of the environment, the proposed human perceptual map building procedure is more representative of spatial cognitive mapping in the human brain, whereby an imprecise and incomplete perceptual map of an environment can be created easily. The key steps in the methodology are capturing stereo-vision images of the environment, creating the tracked reference objects (TROs), tracking the number of remaining TROs, and expanding the map when the limiting points of the environment are reached. The main contribution of this research is on the use of computer vision techniques and computational mapping algorithms on a stereo-vision mobile robot for formulating the human perceptual map systematically, and evaluating the resulting human perceptual maps pertaining to both indoor and outdoor environments comprehensively. Validating the human perceptual maps using vision-based techniques is important for two reasons. Firstly, vision plays an important role in the development of human spatial cognition; secondly, computer vision systems are less expensive and information-rich in representing an environment. Specifically, computer vision techniques are first developed for analyzing the associated stereo images and retrieving the displacement information of a mobile robot, as well ascreating the necessary tracked reference objects. A number of computational mapping algorithms are then employed to build a human perceptual map of the environment in this research. Four real-world environments, namely two large indoor and two large outdoor environments, are empirically evaluated. The spatial geometry of the test environments vary, and the environments are subject to various natural effects including reflection and noise. The reflection and noise occurrin many parts of the images. Therefore, additional algorithms are developed in order to remove the reflection and noise. The removal of reflection and noise significantly reduces the number of TROs createdfor every immediate view. The outcomes indicate that the proposed computer vision techniques and computational mapping algorithms for human perceptual map building are robust and useful. They are able to create imprecise and incomplete human perceptual maps with good spatial representation of the overall environments. The map is imprecise and incomplete in the sense that it is not accurate in metric terms and has perceived surfaces missing. It is shown that both vision-based and the laser-based systems are able to computer a reasonably accurate spatial geometry of the tested environment

Multimodal Grounding for Language Processing

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference of Computational Linguistics. Please refer to this version for citations: https://www.aclweb.org/anthology/papers/C/C18/C18-1197

A computer vision model for visual-object-based attention and eye movements

This is the post-print version of the final paper published in Computer Vision and Image Understanding. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2008 Elsevier B.V.This paper presents a new computational framework for modelling visual-object-based attention and attention-driven eye movements within an integrated system in a biologically inspired approach. Attention operates at multiple levels of visual selection by space, feature, object and group depending on the nature of targets and visual tasks. Attentional shifts and gaze shifts are constructed upon their common process circuits and control mechanisms but also separated from their different function roles, working together to fulfil flexible visual selection tasks in complicated visual environments. The framework integrates the important aspects of human visual attention and eye movements resulting in sophisticated performance in complicated natural scenes. The proposed approach aims at exploring a useful visual selection system for computer vision, especially for usage in cluttered natural visual environments.National Natural Science of Founda- tion of Chin

A Goal-Directed Bayesian Framework for Categorization

Categorization is a fundamental ability for efficient behavioral control. It allows organisms to remember the correct responses to categorical cues and not for every stimulus encountered (hence eluding computational cost or complexity), and to generalize appropriate responses to novel stimuli dependant on category assignment. Assuming the brain performs Bayesian inference, based on a generative model of the external world and future goals, we propose a computational model of categorization in which important properties emerge. These properties comprise the ability to infer latent causes of sensory experience, a hierarchical organization of latent causes, and an explicit inclusion of context and action representations. Crucially, these aspects derive from considering the environmental statistics that are relevant to achieve goals, and from the fundamental Bayesian principle that any generative model should be preferred over alternative models based on an accuracy-complexity trade-off. Our account is a step toward elucidating computational principles of categorization and its role within the Bayesian brain hypothesis

Dwelling Quietly in the Rich Club: Brain Network Determinants of Slow Cortical Fluctuations

For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously -- elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala, and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow time scales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding "feeder" cortical regions show unstable, rapidly fluctuating dynamics likely crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics.Comment: 35 pages, 6 figure