Bio-inspired target detection in natural scenes: optimal thresholds and ego-motion

We have developed a numerical model of Small Target Motion Detector neurons, bio-inspired from electrophysiological experiments in the fly brain. These neurons respond selectively to small moving features within complex moving surrounds. Interestingly, these cells still respond robustly when the targets are embedded in the background, without relative motion cues. This model contains representations of neural elements along a proposed pathway to the target-detecting neuron and the resultant processing enhances target discrimination in moving scenes. The model encodes high dynamic range luminance values from natural images (via adaptive photoreceptor encoding) and then shapes the transient signals required for target discrimination (via adaptive spatiotemporal high-pass filtering). Following this, a model for Rectifying Transient Cells implements a nonlinear facilitation between rapidly adapting, and independent polarity contrast channels (an 'on' and an 'off' pathway) each with center-surround antagonism. The recombination of the channels results in increased discrimination of small targets, of approximately the size of a single pixel, without the need for relative motion cues. This method of feature discrimination contrasts with traditional target and background motion-field computations. We improve the target-detecting output with inhibition from correlation-type motion detectors, using a form of antagonism between our feature correlator and the more typical motion correlator. We also observe that a changing optimal threshold is highly correlated to the value of observer ego-motion. We present an elaborated target detection model that allows for implementation of a static optimal threshold, by scaling the target discrimination mechanism with a model-derived velocity estimation of ego-motion.