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Biomedical subjects

Chris Christodoulou

Publications and source records attributed to Chris Christodoulou.

6 recordsLinked to original sources
PubMed2004

Comparing different classifiers for automatic age estimation.

We describe a quantitative evaluation of the performance of different classifiers in the task of automatic age estimation. In this context, we generate a statistical model of facial appearance, which is subsequently used as the basis for obtaining a compact parametric description of face images. The aim of our work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image. For this application, we have tested different classifiers: a classifier based on the use of quadratic functions for modeling the relationship between face model parameters and age, a shortest distance classifier, and artificial neural network based classifiers. We also describe variations to the basic method where we use age-specific and/or appearance specific age estimation methods. In this context, we use age estimation classifiers for each age group and/or classifiers for different clusters of subjects within our training set. In those cases, part of the classification procedure is devoted to choosing the most appropriate classifier for the subject/age range in question, so that more accurate age estimates can be obtained. We also present comparative results concerning the performance of humans and computers in the task of age estimation. Our results indicate that machines can estimate the age of a person almost as reliably as humans.

Age Factors↗
PubMed2002

A spiking neuron model: applications and learning.

This paper presents a biologically inspired, hardware-realisable spiking neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI). The dynamic applications of the model as well as its applications in Computational Neuroscience are demonstrated and a learning algorithm based on postsynaptic delays is proposed. The TNLI incorporates temporal dynamics at the neuron level by modelling both the temporal summation of dendritic postsynaptic currents which have controlled delay and duration and the decay of the somatic potential due to its membrane leak. Moreover, the TNLI models the stochastic neurotransmitter release by real neuron synapses (with probabilistic RAMs at each input) and the firing times including the refractory period and action potential repolarisation. The temporal features of the TNLI make it suitable for use in dynamic time-dependent tasks like its application as a motion and velocity detector system presented in this paper. This is done by modelling the experimental velocity selectivity curve of the motion sensitive H1 neuron of the visual system of the fly. This application of the TNLI indicates its potential applications in artificial vision systems for robots. It is also demonstrated that Hebbian-based learning can be applied in the TNLI for postsynaptic delay training based on coincidence detection, in such a way that an arbitrary temporal pattern can be detected and recognised. The paper also demonstrates that the TNLI can be used to control the firing variability through inhibition; with 80% inhibition to concurrent excitation, firing at high rates is nearly consistent with a Poisson-type firing variability observed in cortical neurons. It is also shown with the TNLI, that the gain of the neuron (slope of its transfer function) can be controlled by the balance between inhibition and excitation, the gain being a decreasing function of the proportion of inhibitory inputs. Finally, in the case of perfect balance between inhibition and excitation, i.e. where the average input current is zero, the neuron can still fire as a result of membrane potential fluctuations. The firing rate is then determined by the average input firing rate. Overall this work illustrates how a hardware-realisable neuron model can capitalise on the unique computational capabilities of biological neurons.

Analog-Digital Conversion↗