Motion cues provide the bee's visual world with a third dimension

Lehrer, M.; Srinivasan, Mandyam V.; Zhang, Shaowu; Horridge, George Adrian

doi:10.1038/332356a0

Cited by 227 publications

(96 citation statements)

References 13 publications

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“…Honeybees appear to have only two mechanisms of depth perception. One is to rely on the projected retinal size of an object, and the other is motion parallax (Lehrer, Srinivasan, Zhang, & Horridge, 1988;Srinivasan, Lehrer, Zhang, & Horridge, 1989). In following the expansion and contraction of arrays of landmarks and of single landmarks, then, the honeybees are shown to rely heavily on projected retinal size as a mechanism of judging distance.…”

Section: Discussionmentioning

confidence: 99%

Learning the configuration of a landmark array: I. Touch-screen studies with pigeons and humans.

Spetch

Chen²,

MacDonald³

1996

Journal of Comparative Psychology

125

141

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Pigeons and humans searched on a touch-screen monitor for an unmarked goal located relative to an array of landmarks presented in varied screen locations. After training with the goal centered in various square arrays of 4 landmarks, humans, but not pigeons, transferred accurately to arrays with novel elements. Humans searched in the middle of expanded arrays, whereas pigeons preserved the distance and direction to a single landmark. When trained with the goal centered below 2 identical horizontally aligned landmarks, humans responded to horizontal expansions or contractions of the airay by shifting their search vertically, preserving angles from landmarks to goal. Pigeons did not adjust their search vertically. Humans trained with a single landmark adjusted search distance when landmark size was changed. Both pigeons and humans use the configuration of a landmark array, but the underlying processes seem to differ.Experimental work has demonstrated that many animal species can encode the location of an important place in terms of nearby visual landmarks (see Gallistel, 1990, and Collett, 1992, for reviews). In several investigations of landmark use, a goal was located near an array of two or more local landmarks, with a directionally stable frame of reference provided by other cues (e.g.,

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Section: Discussionmentioning

confidence: 99%

Learning the configuration of a landmark array: I. Touch-screen studies with pigeons and humans.

Spetch

Chen²,

MacDonald³

1996

Journal of Comparative Psychology

125

141

View full text Add to dashboard Cite

show abstract

“…To safely fly in cluttered environments, insects instead rely on image motion, also known as optic flow 38,39 , generated by their own displacement relative to the surroundings 40 . It has been experimentally shown that their neural system reacts to optic flow patterns 41,42 to produce a large variety of flight capabilities, such as obstacle avoidance 40,43 , speed maintenance 44 , odometry estimation 45 , wall following and corridor centring 46 , altitude regulation 47,48 , orientation control 49 and landing 50,51 . Optic flow intensity is proportional to the distance from objects only during translational movements, but not during rotational movements when it is proportional to the rotational velocity of the agent.…”

Section: Review Insightmentioning

confidence: 99%

Science, technology and the future of small autonomous drones

Floreano

Wood

2015

Nature

1,087

552

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Drones, a popular nickname for unmanned aerial vehicles and micro aerial vehicles, often conjure up images of unmanned aeroplanes that fly thousands of miles for espionage and to deploy munitions. However, over the past few years, an increasing number of public and private research laboratories have been working on small, human-friendly drones that one day may autonomously fly in confined spaces and in close proximity to people. The development of these small drones, which is the main focus of this Review, has been supported by the miniaturization and cost reduction of electronic components (microprocessors, sensors, batteries and wireless communication units), largely driven by the portable electronic device industry. These improvements have enabled the prototyping and commercialization of small (typically less than 1 kg) drones at smartphone prices.Small drones will have important socio-economic impacts (Fig. 1). Images from drones that are capable of flying a few metres above the ground will fill a gap between expensive, weather-dependent and lowresolution images provided by satellites and car-based images limited to human-level perspectives and the availability of accessible roads. Specialized flying cameras and cloud-based data analytics will allow farmers to continuously monitor the quality of crop growth. Such platforms will enable construction companies to measure work progress in real time. Drones will let mining companies obtain precise volumetric data of excavations. Energy and infrastructure companies will be able to exhaustively survey pipelines, roads and cables. Humanitarian organizations could immediately assess and adapt aid efforts in continuously changing refugee camps. Transportation drones that are capable of safely taking off and landing in the proximity of buildings and humans will allow developing countries -without a suitable road network -to rapidly deliver goods and to finally unleash the full potential of their e-commerce telecommunication infrastructure. Transportation drones will also help developed countries to improve the quality of service in congested or remote areas, and will enable rescue organizations to quickly deliver medical supplies in the field and on demand. Inspection drones that are capable of flying in confined spaces will help fire-fighting and emergency units to assess dangers faster and more safely, logistic companies to detect cracks in the inner and outer shells of ships, road maintenance companies to measure signs of wear and tear in bridges and tunnels, security companies to improve building safety by monitoring areas outside the range of surveillance cameras, and disaster mitigation agencies to inspect partially collapsed buildings where ground clutter is an obstacle for terrestrial robots. Coordinated teams of autonomous drones will enable missions that last longer than the flight time of a single drone by allowing some drones to temporarily leave the team for battery replacement. Drone teams will permit rescue organizations to quickly deploy dedicated commu...

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“…For example, honeybees¯ying through a tunnel maintain equidistance from the side walls by balancing the velocities of the images on the two eyes, even when the walls carry gratings of dierent spatial periods (Kirchner and Srinivasan 1989;Srinivasan et al 1991). Bees can use image motion to distinguish objects at dierent distances, independently of their absolute size (Lehrer et al 1988;Kirchner and Srinivasan 1989); they can also distinguish objects of dierent absolute sizes, irrespective of their ranges (Horridge et al 1992). These ®ndings imply that bees can disentangle size cues from velocity cues, and measure the two parameters independently.…”

Section: Discussionmentioning

confidence: 99%

Speed tuning in elementary motion detectors of the correlation type

Zanker

Srinivasan

Egelhaaf

1999

Biological Cybernetics

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Abstract. A prominent model of visual motion detection is the so-called correlation or Reichardt detector. Whereas this model can account for many properties of motion vision, from humans to insects (review, Borst and Egelhaaf 1989), it has been commonly assumed that this scheme of motion detection is not well suited to the measurement of image velocity. This is because the commonly used version of the model, which incorporates two unidirectional motion detectors with opposite preferred directions, produces a response which varies not only with the velocity of the image, but also with its spatial structure and contrast. On the other hand, information on image velocity can be crucial in various contexts, and a number of recent behavioural experiments suggest that insects do extract velocity for navigational purposes (review, Srinivasan et al. 1996). Here we show that other versions of the correlation model, which consists of a single unidirectional motion detector or incorporates two oppositely directed detectors with unequal sensitivities, produce responses which vary with image speed and display tuning curves that are substantially independent of the spatial structure of the image. This surprising feature suggests simple strategies of reducing ambiguities in the estimation of speed by using components of neural hardware that are already known to exist in the visual system. IntroductionThe simplest way in which a visual system can determine how fast and in what direction an object travels would be to determine how long the object needs to cover the distance between two given points. Indeed,`featuretracking' mechanisms have been proposed along these lines to explain motion vision (Braddick 1980;Ullman 1983) and are sometimes used in machine vision (Murray and Buxton 1990). The disadvantage of such schemes, however, is that they need to identify the object, or features within it, before carrying out the tracking. In another class of models, this problem is avoided by using information on local spatiotemporal changes of intensity to measure velocity. In one subclass of these`intensity-based' models, the so-called gradient models, image speed is determined by computing the ratio of the local temporal and spatial gradients of intensity (Fennema and Thompson 1975;Limb and Murphy 1975). Gradient models have the property that they measure the speed of an image independently of its spatial structure. Modi®cations of this scheme have been proposed for measuring image velocity in two dimensions and for overcoming problems associated with low and sparsely distributed contrasts (Hildreth and Koch 1987;Johnston et al. 1992;Srinivasan 1990).Another subclass of the intensity-based models is the so-called Reichardt or correlation detector, which extracts a motion signal from the spatiotemporal correlations that are present in the moving image (Reichardt 1957). In this model, which has been very successful in describing motion sensitivity in animal vision from insects to primates (review, Borst and Egelhaaf 1989), the signal fr...

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Motion cues provide the bee's visual world with a third dimension

Cited by 227 publications

References 13 publications

Learning the configuration of a landmark array: I. Touch-screen studies with pigeons and humans.

Learning the configuration of a landmark array: I. Touch-screen studies with pigeons and humans.

Science, technology and the future of small autonomous drones

Speed tuning in elementary motion detectors of the correlation type

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