383,490 research outputs found
Conceptual Frameworks for Multimodal Social Signal Processing
This special issue is about a research area which is developing rapidly. Pentland gave it a name which has become widely used, ‘Social Signal Processing’ (SSP for short), and his phrase provides the title of a European project, SSPnet, which has a brief to consolidate the area. The challenge that Pentland highlighted was understanding the nonlinguistic signals that serve as the basis for “subconscious discussions between humans about relationships, resources, risks, and rewards”. He identified it as an area where computational research had made interesting progress, and could usefully make more
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Your Friends Mention It. What About Visiting It? A Mobile Social-Based Sightseeing Application
In this short poster paper, we present an application for suggesting attractions to be visited by users, based on social signal processing technique
A Hilbert Space Theory of Generalized Graph Signal Processing
Graph signal processing (GSP) has become an important tool in many areas such
as image processing, networking learning and analysis of social network data.
In this paper, we propose a broader framework that not only encompasses
traditional GSP as a special case, but also includes a hybrid framework of
graph and classical signal processing over a continuous domain. Our framework
relies extensively on concepts and tools from functional analysis to generalize
traditional GSP to graph signals in a separable Hilbert space with infinite
dimensions. We develop a concept analogous to Fourier transform for generalized
GSP and the theory of filtering and sampling such signals
Discrete Signal Processing on Graphs: Frequency Analysis
Signals and datasets that arise in physical and engineering applications, as
well as social, genetics, biomolecular, and many other domains, are becoming
increasingly larger and more complex. In contrast to traditional time and image
signals, data in these domains are supported by arbitrary graphs. Signal
processing on graphs extends concepts and techniques from traditional signal
processing to data indexed by generic graphs. This paper studies the concepts
of low and high frequencies on graphs, and low-, high-, and band-pass graph
filters. In traditional signal processing, there concepts are easily defined
because of a natural frequency ordering that has a physical interpretation. For
signals residing on graphs, in general, there is no obvious frequency ordering.
We propose a definition of total variation for graph signals that naturally
leads to a frequency ordering on graphs and defines low-, high-, and band-pass
graph signals and filters. We study the design of graph filters with specified
frequency response, and illustrate our approach with applications to sensor
malfunction detection and data classification
Social signal processing for studying parent–infant interaction
International audienceStudying early interactions is a core issue of infant development and psychopathology. Automatic social signal processing theoretically offers the possibility to extract and analyze communication by taking an integrative perspective, considering the multimodal nature and dynamics of behaviors (including synchrony).This paper proposes an explorative method to acquire and extract relevant social signals from a naturalistic early parent–infant interaction. An experimental setup is proposed based on both clinical and technical requirements. We extracted various cues from body postures and speech productions of partners using the IMI2S (Interaction, Multimodal Integration, and Social Signal) Framework. Preliminary clinical and computational results are reported for two dyads (one pathological in a situation of severe emotional neglect and one normal control) as an illustration of our cross-disciplinary protocol. The results from both clinical and computational analyzes highlight similar differences: the pathological dyad shows dyssynchronic interaction led by the infant whereas the control dyad shows synchronic interaction and a smooth interactive dialog.The results suggest that the current method might be promising for future studies
A psycho-ethological approach to social signal processing
The emerging field of social signal processing can benefit from a theoretical framework to guide future research activities. The present article aims at drawing attention to two areas of research that devoted considerable efforts to the understanding of social behaviour: ethology and social psychology. With a long tradition in the study of animal signals, ethology and evolutionary biology have developed theoretical concepts to account for the functional significance of signalling. For example, the consideration of divergent selective pressures responsible for the evolution of signalling and social cognition emphasized the importance of two classes of indicators: informative cues and communicative signals. Social psychology, on the other hand, investigates emotional expression and interpersonal relationships, with a focus on the mechanisms underlying the production and interpretation of social signals and cues. Based on the theoretical considerations developed in these two fields, we propose a model that integrates the processing of perceivable individual features (social signals and cues) with contextual information, and we suggest that output of computer-based processing systems should be derived in terms of functional significance rather than in terms of absolute conceptual meanin
SAM: The School Attachment Monitor
Secure Attachment relationships have been shown to minimise social and behavioural problems in children and boosts resilience to risks later on such as antisocial behaviour, heart pathologies, and suicide. Attachment assessment is an expensive and time-consuming process that is not often performed. The School Attachment Monitor (SAM) automates Attachment assessment to support expert assessors. It uses doll-play activities with the dolls augmented with sensors and the child's play recorded with cameras to provide data for assessment. Social signal processing tools are then used to analyse the data and to automatically categorize Attachment patterns. This paper presents the current SAM interactive prototype
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