1,276 research outputs found
Visually Indicated Sounds
Objects make distinctive sounds when they are hit or scratched. These sounds
reveal aspects of an object's material properties, as well as the actions that
produced them. In this paper, we propose the task of predicting what sound an
object makes when struck as a way of studying physical interactions within a
visual scene. We present an algorithm that synthesizes sound from silent videos
of people hitting and scratching objects with a drumstick. This algorithm uses
a recurrent neural network to predict sound features from videos and then
produces a waveform from these features with an example-based synthesis
procedure. We show that the sounds predicted by our model are realistic enough
to fool participants in a "real or fake" psychophysical experiment, and that
they convey significant information about material properties and physical
interactions
Audio-coupled video content understanding of unconstrained video sequences
Unconstrained video understanding is a difficult task. The main aim of this thesis is to
recognise the nature of objects, activities and environment in a given video clip using
both audio and video information. Traditionally, audio and video information has not
been applied together for solving such complex task, and for the first time we propose,
develop, implement and test a new framework of multi-modal (audio and video) data
analysis for context understanding and labelling of unconstrained videos.
The framework relies on feature selection techniques and introduces a novel algorithm
(PCFS) that is faster than the well-established SFFS algorithm. We use the framework for
studying the benefits of combining audio and video information in a number of different
problems. We begin by developing two independent content recognition modules. The
first one is based on image sequence analysis alone, and uses a range of colour, shape,
texture and statistical features from image regions with a trained classifier to recognise
the identity of objects, activities and environment present. The second module uses audio
information only, and recognises activities and environment. Both of these approaches
are preceded by detailed pre-processing to ensure that correct video segments containing
both audio and video content are present, and that the developed system can be made
robust to changes in camera movement, illumination, random object behaviour etc. For
both audio and video analysis, we use a hierarchical approach of multi-stage
classification such that difficult classification tasks can be decomposed into simpler and
smaller tasks.
When combining both modalities, we compare fusion techniques at different levels of
integration and propose a novel algorithm that combines advantages of both feature and
decision-level fusion. The analysis is evaluated on a large amount of test data comprising
unconstrained videos collected for this work. We finally, propose a decision correction
algorithm which shows that further steps towards combining multi-modal classification
information effectively with semantic knowledge generates the best possible results
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
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