Proceedings of the Eighth Australian and New Zealand Intelligent Information Systems Conference

These are the full proceedings of the conference.

Instantiating deformable models with a neural net

Deformable models are an attractive approach to recognizing objects which have considerable within-class variability such as handwritten characters. However, there are severe search problems associated with fitting the models to data which could be reduced if a better starting point for the search were available. We show that by training a neural network to predict how a deformable model should be instantiated from an input image, such improved starting points can be obtained. This method has been implemented for a system that recognizes handwritten digits using deformable models, and the results show that the search time can be significantly reduced without compromising recognition performance. © 1997 Academic Press.

Effect of uncorrected astigmatism on vision

PURPOSE: To examine the effect of uncorrected astigmatism in older adults. SETTING: University Vision Clinic METHOD: Twenty-one healthy presbyopes, aged 58.9±2.8 years, had astigmatism of 0.0 to -4.0 x 90?DC and -3.0DC of cylinder at 90?, 180? and 45? induced with spectacle lenses, with the mean spherical equivalent compensated to plano, in random order. Visual acuity was assessed binocularly using a computerised test chart at 95%, 50% and 10% contrast. Near acuity and reading speed were measured using standardised reading texts. Light scatter was quantified with the cQuant and driving reaction times with a computer simulator. Finally visual clarity of a mobile phone and computer screen was subjectively rated. RESULTS: Distance visual acuity decreased with increasing uncorrected astigmatic power (F=174.50, p<0.001) and was reduced at lower contrasts (F=170.77, p<0.001). Near visual acuity and reading speed also decreased with increasing uncorrected astigmatism power (p<0.001). Light scatter was not significantly affected by uncorrected astigmatism (p>0.05), but the reliability and variability of measurements decreased with increasing uncorrected astigmatic power (p<0.05). Driving simulator performance was also unaffected by uncorrected astigmatism (p>0.05), but subjective rating of clarity decreased with increasing uncorrected astigmatic power (p<0.001). Uncorrected astigmatism at 45? or 180? orientation resulted in a worse distance and near visual acuity, and subjective rated clarity than 90? orientation (p<0.05). CONCLUSION: Uncorrected astigmatism, even as low as 1.0DC, causes a significant burden on a patient’s vision. If left uncorrected, this could impact significantly on their independence, quality of life and wellbeing.

Bars and edges: what makes a feature for human vision?

There have been two main approaches to feature detection in human and computer vision - luminance-based and energy-based. Bars and edges might arise from peaks of luminance and luminance gradient respectively, or bars and edges might be found at peaks of local energy, where local phases are aligned across spatial frequency. This basic issue of definition is important because it guides more detailed models and interpretations of early vision. Which approach better describes the perceived positions of elements in a 3-element contour-alignment task? We used the class of 1-D images defined by Morrone and Burr in which the amplitude spectrum is that of a (partially blurred) square wave and Fourier components in a given image have a common phase. Observers judged whether the centre element (eg ±458 phase) was to the left or right of the flanking pair (eg 0º phase). Lateral offset of the centre element was varied to find the point of subjective alignment from the fitted psychometric function. This point shifted systematically to the left or right according to the sign of the centre phase, increasing with the degree of blur. These shifts were well predicted by the location of luminance peaks and other derivative-based features, but not by energy peaks which (by design) predicted no shift at all. These results on contour alignment agree well with earlier ones from a more explicit feature-marking task, and strongly suggest that human vision does not use local energy peaks to locate basic first-order features. [Supported by the Wellcome Trust (ref: 056093)]

Investigation of neural correlates of faces and emotional expressions using magnetoencephalography

The recognition of faces and of facial expressions in an important evolutionary skill, and an integral part of social communication. It has been argued that the processing of faces is distinct from the processing of non-face stimuli and functional neuroimaging investigations have even found evidence of a distinction between the perception of faces and of emotional expressions. Structural and temporal correlates of face perception and facial affect have only been separately identified. Investigation neural dynamics of face perception per se as well as facial affect would allow the mapping of these in space, time and frequency specific domains. Participants were asked to perform face categorisation and emotional discrimination tasks and Magnetoencephalography (MEG) was used to measure the neurophysiology of face and facial emotion processing. SAM analysis techniques enable the investigation of spectral changes within specific time-windows and frequency bands, thus allowing the identification of stimulus specific regions of cortical power changes. Furthermore, MEG’s excellent temporal resolution allows for the detection of subtle changes associated with the processing of face and non-face stimuli and different emotional expressions. The data presented reveal that face perception is associated with spectral power changes within a distributed cortical network comprising occipito-temporal as well as parietal and frontal areas. For the perception of facial affect, spectral power changes were also observed within frontal and limbic areas including the parahippocampal gyrus and the amygdala. Analyses of temporal correlates also reveal a distinction between the processing of faces and facial affect. Face perception per se occurred at earlier latencies whereas the discrimination of facial expression occurred within a longer time-window. In addition, the processing of faces and facial affect was differentially associated with changes in cortical oscillatory power for alpha, beta and gamma frequencies. The perception of faces and facial affect is associated with distinct changes in cortical oscillatory activity that can be mapped to specific neural structures, specific time-windows and latencies as well as specific frequency bands. Therefore, the work presented in this thesis provides further insight into the sequential processing of faces and facial affect.

Self-calibrated, multi-spectral photometric stereo for 3D face capture

This paper addresses the problem of obtaining 3d detailed reconstructions of human faces in real-time and with inexpensive hardware. We present an algorithm based on a monocular multi-spectral photometric-stereo setup. This system is known to capture high-detailed deforming 3d surfaces at high frame rates and without having to use any expensive hardware or synchronized light stage. However, the main challenge of such a setup is the calibration stage, which depends on the lights setup and how they interact with the specific material being captured, in this case, human faces. For this purpose we develop a self-calibration technique where the person being captured is asked to perform a rigid motion in front of the camera, maintaining a neutral expression. Rigidity constrains are then used to compute the head's motion with a structure-from-motion algorithm. Once the motion is obtained, a multi-view stereo algorithm reconstructs a coarse 3d model of the face. This coarse model is then used to estimate the lighting parameters with a stratified approach: In the first step we use a RANSAC search to identify purely diffuse points on the face and to simultaneously estimate this diffuse reflectance model. In the second step we apply non-linear optimization to fit a non-Lambertian reflectance model to the outliers of the previous step. The calibration procedure is validated with synthetic and real data.

Genetic modulation of the response bias towards facial displays of anger and happiness

Background: Investigating genetic modulation of emotion processing may contribute to the understanding of heritable mechanisms of emotional disorders. The aim of the present study was to test the effects of catechol- O-methyltransferase (COMT) val158met and serotonin-transporter-linked promoter region (5-HTTLPR) polymorphisms on facial emotion processing in healthy individuals. Methods: Two hundred and seventy five (167 female) participants were asked to complete a computerized facial affect recognition task, which involved four experimental conditions, each containing one type of emotional face (fearful, angry, sad or happy) intermixed with neutral faces. Participants were asked to indicate whether the face displayed an emotion or was neutral. The COMT-val158met and 5-HTTLPR polymorphisms were genotyped. Results: Met homozygotes (COMT) showed a stronger bias to perceive neutral faces as expressions of anger, compared with val homozygotes. However, the S-homozygotes (5-HTTLPR) showed a reduced bias to perceive neutral faces as expressions of happiness, compared to L-homozygotes. No interaction between 5-HTTLPR and COMT was found. Conclusions: These results add to the knowledge of individual differences in social cognition that are modulated via serotonergic and dopaminergic systems. This potentially could contribute to the understanding of the mechanisms of susceptibility to emotional disorders. © 2013 Elsevier Masson SAS.

A quantum Jensen-Shannon graph kernel for unattributed graphs

In this paper, we use the quantum Jensen-Shannon divergence as a means of measuring the information theoretic dissimilarity of graphs and thus develop a novel graph kernel. In quantum mechanics, the quantum Jensen-Shannon divergence can be used to measure the dissimilarity of quantum systems specified in terms of their density matrices. We commence by computing the density matrix associated with a continuous-time quantum walk over each graph being compared. In particular, we adopt the closed form solution of the density matrix introduced in Rossi et al. (2013) [27,28] to reduce the computational complexity and to avoid the cumbersome task of simulating the quantum walk evolution explicitly. Next, we compare the mixed states represented by the density matrices using the quantum Jensen-Shannon divergence. With the quantum states for a pair of graphs described by their density matrices to hand, the quantum graph kernel between the pair of graphs is defined using the quantum Jensen-Shannon divergence between the graph density matrices. We evaluate the performance of our kernel on several standard graph datasets from both bioinformatics and computer vision. The experimental results demonstrate the effectiveness of the proposed quantum graph kernel.