TRICLOPS: a high-performance trinocular active vision system

Wavering, Albert J.; Fiala, John C.; Roberts, Karen J.; Lumia, Ronald

doi:10.1109/robot.1993.292207

Cited by 21 publications

(14 citation statements)

References 15 publications

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“…Many robots or robotic heads endowed with attentive motion capabilities have been developed in order to collect the visual data accordingly. Separate mechanisms have been proposed for different visual behaviors such as free viewing (Clark and Ferrier 1988;Burt 1988;Reid et al 1993;Ude et al 2005;Meger et al 2007;Dankers et al 2007), detecting visual onsets (Norrima et al 2007), search (Vijakumar et al 2001;Soyer et al 2006;Tsotsos and Shubina 2007;Rasolzadeh et al 2006;Vogel and de Freitas 2008;Forssen et al 2008) and remaining fixated and tracking (Clark and Ferrier 1988;Wavering et al 1993;Coombs and Brown 1992;Blackburn and Nguyen 1995;Rivlin and Rotstein 2000). In most of this work, the body of the robot is stationary while the head is moving with few notable exceptions (Frintrop et al 2010a).…”

Section: Robotic Vision Systemsmentioning

confidence: 99%

Artificial potential functions based camera movements and visual behaviors in attentive robots

Erkent

Bozma

2011

Auton Robot

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An attentive robot needs to exhibit a plethora of different visual behaviors including free viewing, detecting visual onsets, search, remaining fixated and tracking depending on the vision task at hand. The robot's associated camera movements-ranging from saccades to smooth pursuit-direct its optical axis in a manner that is dependent on the current visual behavior. This paper proposes a closedloop dynamical systems approach to the generation of camera movements based on a family of artificial potential functions. Each movement from the current fixation point to the next is associated with an artificial potential function that encodes saliency and possibly inhibition depending on the visual behavior that the robot is engaged in. The novelty of this approach is that since the nature of resulting motion can vary from being saccadic to smooth pursuit, the full repertoire of visual behaviors all become possible within the same framework. The robot can switch its visual behavior simply by changing the parameters of the constructed artificial potential functions appropriately. Furthermore, automated reflexive changes among the different visual behaviors can be achieved via a simple switching automaton. Experimental results with APES robot serve to show the performance properties of a robot engaged in each different visual behavior.

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Section: Robotic Vision Systemsmentioning

confidence: 99%

Artificial potential functions based camera movements and visual behaviors in attentive robots

Erkent

Bozma

2011

Auton Robot

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“…Consequently, the correspondence problem may not be trivial enough to yield to salience images. Wavering et al (1993) describe binocular smooth pursuit on a high-performance head. High-speed pursuit is achieved for frame-rate visual processing with careful calibration of the visuomotor system.…”

Section: The Pursuit Problem and Related Workmentioning

confidence: 99%

“…Some simple predictive control is described here, and some simulation investigations of predictive gaze control are described by McDonald et al, (1983), Brown (1990), and Brown and Coombs (1991). Experiments using prediction to improve pursuit performance are reported by Wavering et al, (1993). A simpler approach reduces overshooting simply by reducing the responsiveness of the system enough to make it stable.…”

Section: Smooth Pursuit Controlmentioning

confidence: 99%

Real-time binocular smooth pursuit

Coombs¹,

Brown

1993

Int J Comput Vision

107

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This article examines the problem of a moving robot tracking a moving object with its cameras, without requiring the ability to recognize the target to distinguish it from distracting surroundings. A novel aspect of the approach taken is the use of controlled camera movements to simplify the visual processing necessary to keep the cameras locked on the target. A gaze-holding system implemented on a robot's binocular head demonstrates this approach. Even while the robot is moving, the cameras are able to track an object that rotates and moves in three dimensions.The central idea is that localizing attention in 3-D space makes precategorical visual processing sufficient to hold gaze. Visual fixation can help separate the target object from distracting surroundings. Converged cameras produce a horopter (surface of zero stereo disparity) in the scene. Binocular features with no disparity can be located with a simple filter, showing the object's location in the image. Similarly, an object that is being tracked is imaged near the center of the field of view, so spatially localized processing helps concentrate visual attention on the target. Instead of requiring a way to recognize the target, the system relies on active control of camera movements and binocular fixation segmentation to locate the target.

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“…Details of some examples are given in [1]- [7]. What has been lacking, however, is a standardized approach to the notation used in describing the motions of such devices.…”

Section: Introductionmentioning

confidence: 99%