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.
Insects have very efficient vision algorithms that allow them to perform complex manoeuvres in real time, while using a very limited processing power. In this paper we study some of the properties of these algorithms with the aim of implementing them in microchip devices. To achieve this we simulate insect vision using our software, which utilises the Horridge Template Model, to detect the angular velocity of a moving object. The motion is simulated using a number of rotating images showing both artificial constructs and real life scenes and is captured with a CMOS camera. We investigate the effects of texel density, contrast, luminance and chrominance properties of the moving images. Pre and post template filtering and different threshold settings are used to improve the accuracy of the estimated angular velocity. We then further analyse and compare the results obtained. We will then implement an efficient velocity estimation algorithm that produces reliable results. Lastly, we will also look into developing the estimation of time to impact algorithm.
The insect visual system, with its simplicity and efficiency has gained widespread attention and many biologically inspired models are being used for motion detection and velocity estimation tasks. One of the earliest and most efficient models among them is the Reichardt correlator model. In this paper, we have elaborated the basic Reichardt correlator to include spatial and temporal pre-filtering and additional non-linearites which are believed to be present in the fly visual system to develop a simple yaw sensor. We have used just 16 elaborated EMDs and it is seen that this sensor can detect rotational motion at angular velocities up to several thousand degrees per second. The modelling of these sensors make us realize that the VLSI implementation of such simple detectors can have varied applications for flight control in different fields.
Motion detection and velocity estimation systems based on the
study of insects tries to emulate the extraordinary visual system
of insects with the aim of coming up with low power, computationally simple, highly efficient and robust devices. The Reichardt correlator model is one of the earliest and the most prominent models of motion detection based on insect vision. In this paper we try to extend the Reichardt correlator model to include an additional non-linearity which has been seen to be present in the fly visual system and we study its effect on the contrast dependance of the response and also try to understand its influence on pattern
noise. Experiments are carried out by adding this compressive non-linearity at different positions in the model as has been postulated by previous works and comparison of the physiological data with
modelling results is done.
Despite their limited information processing capabilities, insects (with brains smaller than a pinhead) are able to manoeuvre with precision through environments that are highly-crowded and contain moving objects. Their ability to avoid collisions using limited computing power forms the basis for this project, in which we attempt to simulate the motion detection ability of insects using two models - the Horridge Template Model and the Reichardt Correlation Model. In this project, the direction of motion of a moving object and its angular speed are determined by capturing visual data using a web camera focussed on a moving pattern generated by VisionEgg software. The performance of both the models is quantitatively compared and various error-reducing techniques are investigated.
Insects perform highly complicated navigational tasks even though their visual system is relatively simple. The main idea of work in this area is to study the visual system of insects and to incorporate algorithms used by them in electronic circuits to produce
low power, computationally simple, highly efficient, robust devices capable of accurate motion detection and velocity estimation. The Reichardt correlator model is one of the earliest and the most prominent biologically inspired models of motion detection developed by Hassentein and Reichardt in 1956. In an attempt to get accurate estimates of yaw velocity using an elaborated Reichardt correlator, we have investigated the effect of pattern noise (deviation of the correlator output resulting from the structure of the visual scene) on the correlator response. We have tested different sampling methods here and it is found that a circular sampled array of elementary motion detectors (EMDs) reduces pattern noise effectively compared to an array of rectangular or randomly selected EMDs for measuring rotational motion.
Insects have a very efficient visual system that helps them to
perform extraordinarily complicated navigational acts and
precisely controlled aerobatic flight. Physiological evidence
suggests that flight control is guided by a small system of
'tangential' neurons tuned to very specific types of complex
motion by the way that they collate information from local motion
detectors. One class of tangential neurons, the 'horizontal
system' (HS) neurons, respond with opponent graded responses to
yaw stimuli. Using the results of physiological experiments, we
have developed a model, based on an array of Reichardt correlators, for the receptive field of HS neurons that view optical flow along the equator. Our model incorporates additional non-linearities that mimic known properties of the insect motion pathway, including logarithmic encoding of luminance, saturation and motion adaptation (adaptive gain-control). In this paper, we compare the response of our elaborated model with fly HS neuron responses 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". To investigate the influence of anisotropic features in producing pattern noise, we presented a panoramic image at various initial positions, and versions of the same image modified to disrupt vertical contours. We conclude that the response of the fly neurons shows evidence of local saturation at key stages in the motion pathway. This saturation reduces the effect of pattern noise and improves the coding of velocity. Our model provides an excellent basis for the development of biomimetic yaw sensors for robotic applications.
Flying insects are capable of performing complex and extremely diffcult navigational tasks at high speeds with
amazing ability. The neural computations underlying these complicated maneuvers and the motor activity of
the insects have been extensively investigated in the last few decades.1-5 One the most important discovery
was that the motion detectors involved in the control of the optomotor responses are of the correlation type.6
In order to improve the velocity estimation by the Reichardt correlators, many scientists have come up with
different kinds of elaborations to the basic Reichardt correlator model.
In this paper, we have expanded the Dror’s elaborated Reichardt model7 and we have included feedback
adaptation and saturation in our model and we have conducted a comparative study on the effects of the
addition of each elaboration on the performance of the model. The relative error in each case is also studied.
Insects are blessed with a very efficient yet simple visual system which enable them to navigate with great ease and accuracy. Though a lot has been done in the field of insect vision, there is still not a clear understanding of how velocity is determined in biological vision systems. The dominant model for insect motion detection, first proposed by Hassentein and Reichardt in 1956 has gained widespread acceptance in the invertebrate vision community. The template model, proposed later by Horridge in 1990, permits simple tracking techniques and lends itself easily to both hardware and software. Analysis and simulation by Dror suggest that the inclusion of additional system components to perform pre-filtering, response compression, integration and adaptation, to a basic Reichardt correlator can make it less sensitive to contrast and spatial structure thereby providing a more robust estimate of local image velocity. It was found from the data obtained, from the intracellular recordings of the steady state responses of wide field neurons in the hoverfly Volucella, that the shape of the curves obtained, agreed perfectly with the theoretical predictions made by Dror. In order to compare it with the template model, an experiment was done to get the velocity response curves of the template model using the same image statistics. The results leads us to believe that the fly motion detector emulates a modified Reichardt correlator.
The study of insect vision is believed to provide a key solution to many different aspects of motion detection and velocity estimation. The main reason for this is that motion detection in the fly is extremely fast, with computations requiring only a few milliseconds. So the insect visual system serves as the basis for many models of motion detection. The earliest and the most prominent model is the Reichardt correlator model. But it is found that in the absence of additional system components, the response of a simple Reichardt correlator model is dependent on contrast and spatial frequency. Dror has demonstrated in his work that the addition of spatial and temporal filtering, saturation, integration and adaptation in a correlator based system can make it act as a reliable velocity estimator.
In this paper, we try to further investigate and expand his model to improve the correlator performance. Our recent neurobiological experiments suggest that adaptive mechanisms decrease EMD (elementary motion detector) dependence on pattern contrast and improve reliability. So appropriate modelling of an adaptive feedback mechanism is done to normalise contrast of input signals.
The Horridge Template model is an empirical motion detection model inspired by insect vision. This model has been successfully implemented in several micro-sensor VLSI chips using grayscale pixels. The template model is based on movement of detected edges rather than the whole object, which consequently facilitates simple tracking techniques. Simple tracking algorithms developed by Nguyen have been successful in tracking coherent movement of objects in a simple environment. An extension of the template model using color templates developed by Chin has also been successful in tracking objects moving in close proximity to each other. This paper introduces a low-cost insect vision prototype based on the use of a color CMOS camera. We implement a further extension to the above algorithms with error checking. Several error-checking schemes are used during template formation and manipulation to reduce noise and randomness. This enables the detection of moving objects in noisy environments, which may be applied to many real life situations.
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