Yaoru Sun

Object-based visual attention for computer vision

In this paper, a novel model of object-based visual attention extending Duncans Integrated Competition Hypothesis [Phil. Trans. R. Soc. London B 353 (1998) 1307-1317] is presented. In contrast to the attention mechanisms used in most previous machine vision systems which drive attention based on the spatial location hypothesis, the mechanisms which direct visual attention in our system are object-driven as well as feature-driven. The competition to gain visual attention occurs not only within an object but also between objects. For this purpose, two new mechanisms in the proposed model are described and analyzed in detail. The first mechanism computes the visual salience of objects and groupings; the second one implements the hierarchical selectivity of attentional shifts. The results of the new approach on synthetic and natural images are reported.

Structure of peer-to-peer social networks

This paper presents a statistical analysis of the structure of peer-to-peer (P2P) social networks that captures social associations of distributed peers in resource sharing. Peer social networks appear to be mainly composed of pure resource providers that guarantee high resource availability and reliability of P2P systems. The major peers that both provide and request resources are only a small fraction. The connectivity between peers, including undirected, directed (out and in) and weighted connections, is scale-free and the social networks of all peers and major peers are small world networks. The analysis also confirms that peer social networks show in general disassortative correlations, except that active providers are connected between each other and by active requesters. The study presented in this paper gives a better understanding of peer relationships in resource sharing, which may help a better design of future P2P networks and open the path to the study of transport processes on top of real P2P topologies.

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

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.

SELF-ORGANIZING PEER-TO-PEER SOCIAL NETWORKS

Peer‐to‐peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self‐organizing peer‐to‐peer social networks (SoPPSoNs) to self‐organize distributed peers in a decentralized way, in which neuron‐like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self‐organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.

Self-Organizing and Adaptive Peer-to-Peer Network

In this paper, an algorithm that forms a dynamic and self-organizing network is demonstrated. The hypothesis of this work is that in order to achieve a resilient and adaptive peer-to-peer (P2P) network, each network node must proactively maintain a minimum number of edges. Specifically, low-level communication protocols are not sufficient by themselves to achieve high-service availability, especially in the case of ad hoc or dynamic networks with a high degree of node addition and deletion. The concept has been evaluated within a P2P agent application in which each agent has a goal to maintain a preferred number of connections to a number of service providing agents. Using this algorithm, the agents update a weight value associated with each connection, based on the perceived utility of the connection to the corresponding agent. This utility function can be a combination of several node or edge parameters, such as degree k of the target node, or frequency of the message response from the node. This weight is updated using a set of Hebbian-style learning rules, such that the network as a whole exhibits adaptive self-organizing behavior. The principal result is the finding that by limiting the connection neighborhood within the overlay topology, the resulting P2P network can be made highly resilient to targeted attacks on high-degree nodes, while maintaining search efficiency

On-line EEG classification for brain-computer interface based on CSP and SVM

Brain-Computer Interface (BCI) research aims at automatically translating neural commands into control signals through classifying the electroencephalogram (EEG) patterns of different mental tasks (e.g. imagined hand and foot movements). This paper presents a method of on-line classification for BCI based on Common Spatial Pattern (CSP) for feature extraction and Support Vector Machine (SVM) as a classifier. The best classification results for three subjects are 86.3%, 91.8%, and 92%. The high classification rate in a real-time 3D computer game indicates that the proposed method is promising for an EEG-based brain-computer interface. It can provide a new way for the EEG automation classification when the EEG is used an input signal to a brain computer interface.

Mirror neural training induced by virtual reality in brain–computer interfaces may provide a promising approach for the autism therapy

Previous studies have suggested that the dysfunction of the human mirror neuron system (hMNS) plays an important role in the autism spectrum disorder (ASD). In this work, we propose a novel training program from our interdisciplinary research to improve mirror neuron functions of autistic individuals by using a BCI system with virtual reality technology. It is a promising approach for the autism to learn and develop social communications in a VR environment. A test method for this hypothesis is also provided.

Electroencephalogram evidence for the activation of human mirror neuron system during the observation of intransitive shadow and line drawing actions

Previous studies have demonstrated that hand shadows may activate the motor cortex associated with the mirror neuron system in human brain. However, there is no evidence of activity of the human mirror neuron system during the observation of intransitive movements by shadows and line drawings of hands. This study examined the suppression of electroencephalography mu waves (8–13 Hz) induced by observation of stimuli in 18 healthy students. Three stimuli were used: real hand actions, hand shadow actions and actions made by line drawings of hands. The results showed significant desynchronization of the mu rhythm (“mu suppression”) across the sensorimotor cortex (recorded at C3, Cz and C4), the frontal cortex (recorded at F3, Fz and F4) and the central and right posterior parietal cortex (recorded at Pz and P4) under all three conditions. Our experimental findings suggest that the observation of “impoverished hand actions”, such as intransitive movements of shadows and line drawings of hands, is able to activate widespread cortical areas related to the putative human mirror neuron system.

Covert Verb Reading Contributes to Signal Classification of Motor Imagery in BCI.

Motor imagery is widely used in the brain–computer interface (BCI) systems that can help people actively control devices to directly communicate with the external world, but its training and performance effect is usually poor for normal people. To improve operators’ BCI performances, here we proposed a novel paradigm, which combined the covert verb reading in the traditional motor imagery paradigm. In our proposed paradigm, participants were asked to covertly read the presented verbs during imagining right hand or foot movements referred by those verbs. EEG signals were recorded with both our proposed paradigm and the traditional paradigm. By the common spatial pattern method, we, respectively, decomposed these signals into spatial patterns and extracted their features used in the following classification of support vector machine. Compared with the traditional paradigm, our proposed paradigm could generate clearer spatial patterns following a somatotopic distribution, which led to more distinguishable features and higher classification accuracies than those in the traditional paradigm. These results suggested that semantic processing of verbs can influence the brain activity of motor imagery and enhance the mu event-related desynchronisation. The combination of semantic processing with motor imagery is therefore a promising method for the improvement of operators’ BCI performances.

Automatic Correction of Hand Pointing in Stereoscopic Depth

In order to examine whether stereoscopic depth information could drive fast automatic correction of hand pointing, an experiment was designed in a 3D visual environment in which participants were asked to point to a target at different stereoscopic depths as quickly and accurately as possible within a limited time window (≤300 ms). The experiment consisted of two tasks: “depthGO” in which participants were asked to point to the new target position if the target jumped, and “depthSTOP” in which participants were instructed to abort their ongoing movements after the target jumped. The depth jump was designed to occur in 20% of the trials in both tasks. Results showed that fast automatic correction of hand movements could be driven by stereoscopic depth to occur in as early as 190 ms.