In this paper, we propose a biologically inspired spiking neural network approach to obtaining an opponent pair which is invariant to illumination variations and can be employed for colour discrimination. The model is motivated by the neural mechanisms involved in processing the visual stimulus starting from the cone photo receptors to the centre-surround receptive fields present in the retinal ganglion cells and the striate cortex. For our spiking neural network, we have employed the excitatory and inhibitory lateral synaptic connections, the Spike-Timing Dependent Plasticity (STDP) and long term potentiation and depression (LTP/LTD). Here, we employ a feed-forward leaky integrate-and-fire spiking neural network trained using a dataset of Munsell spectra. We have performed tests on perceptually similar colours under large illuminant power variations and done experiments on colour-based object recognition. We have also compared our results to those yielded by a number of alternatives.

In this paper an algorithm is proposed to extract two illuminant invariant chromaticity features from three image sensor responses. The algorithm extracts these chromaticity features at pixel level and therefore can perform well in scenes illuminated with non-uniform illuminant. An approach is proposed to use the algorithm with cameras of unknown sensitivity. The algorithm was tested for separability of perceptually similar colours under the International Commission on Illumination (CIE) standard illuminants and obtained a good performance. It was also tested for colour based object recognition by illuminating objects with typical indoor illuminants and obtained a better performance compared to other existing algorithms investigated in this paper. Finally, the algorithm was tested for skin detection invariant to illuminant, ethnic background and imaging device. In this investigation, daylight scenes under different weather conditions and scenes illuminated by typical indoor illuminants were used. The proposed algorithm gives a better skin detection performance compared to widely used standard colour spaces. Based on the results presented, the proposed illuminant invariant chromaticity space can be used for machine vision applications including illuminant invariant colour based object recognition and skin detection.

This paper proposes a biologically plausible system for object recognition based on tactile form perception. A spiking neural network, an encoding scheme for converting the input values into spike trains, a method for converting the output spike pattern into reliable features for object recognition and a training approach for the spiking neural network are proposed. Three separate spiking neural networks are used in this recognition system. Three features, based on the output firing pattern of the three networks, are projected onto a three dimensional space. Each class of objects forms a cluster in the three-dimensional feature space. During the training the firing threshold of the hidden layer is modified in such a way that the cluster formed by an object is small and does not overlap with neighbouring clusters. The system has been tested with a number of objects for recognition based on shape. In addition, the system has also been tested for the ability to recognise objects of the same shape but different size. The results show the proposed system gives good performance in recognising objects based on tactile form perception.

In this paper a biologically inspired spiking neural network based haptic object recognition system is proposed. A number of different encoding schemes to convert the haptic measurements into spike train are proposed and investigated for haptic object recognition and compared with existing encoding schemes. The spiking neural network was trained using a supervised training approach that is based on the steepest descent algorithm. During the training, firing threshold of the hidden layer neurons were modified in such a way that the ability of the system in recognising different objects is maximised. A multiplexing scheme is used to convert the parallel spike train of the hidden layer into a serial stream. To convert the output spike train into a reliable feature that represents the shape of an object, moment of the spikes with respect to a reference time is calculated. A robot hand with three fingers that have the same number of degrees of freedom as the human fingers was used for testing the system. The hand was made to grasp different objects and the joint angles were recorded. These recorded angles were converted into spike train using different encoding schemes and applied as input to the network. Test results show that the performance of the system varies depending on the input encoding scheme and with the best encoring scheme the system can recognise 100% of 7 different objects.