Paper
11 January 2007 Implementation of saturation for modelling pattern noise using naturalistic stimuli
Author Affiliations +
Proceedings Volume 6414, Smart Structures, Devices, and Systems III; 641424 (2007) https://doi.org/10.1117/12.696105
Event: SPIE Smart Materials, Nano- and Micro-Smart Systems, 2006, Adelaide, Australia
Abstract
Insects with their amazing visual system are able to perform exceptional navigational feats. In order to understand how they perform motion detection and velocity estimation, much work has been done in the past 40 years and many models of motion detection have been proposed. One of the earliest and most prominent models is the Reichardt correlator model. We have elaborated the Reichardt correlator model to include additional non-linearities that mimic known properties of the insect motion pathway, including logarithmic encoding of luminance and saturation at various stages of processing. In this paper, we compare the response of our elaborated model with recordings from fly HS neurons to naturalistic image panoramas. Such responses are dominated by noise which is largely non-random. Deviations in the correlator response are likely due to the structure of the visual scene, which we term Pattern noise. Pattern noise is investigated by implementing saturation at different stages in our model and comparison of each of these models with the physiological data from the fly is performed using cross covariance technique.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sreeja Rajesh, Tamath Rainsford, Russell S. A. Brinkworth, Derek Abbott, and David C. O'Carroll "Implementation of saturation for modelling pattern noise using naturalistic stimuli", Proc. SPIE 6414, Smart Structures, Devices, and Systems III, 641424 (11 January 2007); https://doi.org/10.1117/12.696105
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Cited by 4 scholarly publications.
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KEYWORDS
Motion models

Optical correlators

Eye models

Sensors

Visual process modeling

Motion detection

Data modeling

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