Efficient active monocular fixation using the log-polar sensor

Yeung, Albert; Barnes, Nick

doi:10.1504/ijista.2005.007313

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…2 and 4. In Table 1, the values for SNR are given for different va− lues of the contrast S, and for the number of sensors N = 2 16 .…”

Section: Numerical Results and Comparison With Log-polar Samplingmentioning

confidence: 99%

“…Image sampling based on the product log−polar quantization is called product log−polar sampling, while based on the optimal log−polar quantization is called optimal log−polar sampling. Product log−polar image sampling was used in many papers [8][9][10][11][12][13][14][15][16]. Optimal log−polar image sampling was introduced in Ref.…”

Section: Introductionmentioning

confidence: 99%

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Optimal polar image sampling

Dincic

Perić

Jovanović

2011

Opto-Electronics Review

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In this paper, a problem of efficient image sampling (deployment of image sensors) is considered. This problem is solved using techniques of two-dimensional quantization in polar coordinates, taking into account human visual system (HVS) and eye sensitivity function. The optimal radial compression function for polar quantization is derived. Optimization of the number of the phase levels for each amplitude level is done. Using optimal radial compression function and optimal number of phase levels for each amplitude level, optimal polar quantization is defined. Using deployment of quantization cells for the optimal polar quantization, deployment of image sensors is done, and therefore optimal polar image sampling is obtained. It is shown that our solution (the optimal polar sampling) has many advantages compared to presently used solutions, based on the log-polar sampling. The optimal polar sampling gives higher SNR (signal-to-noise ratio), compared to the log-polar sampling, for the same number of sensors. Also, the optimal polar sampling needs smaller number of sensors, to achieve the same SNR, compared to the log-polar sampling. Furthermore, with the optimal polar sampling, points in the image middle can be sampled, which is not valid for the log-polar sampling. This is very important since human eye is the most sensitive to these points, and therefore the optimal polar sampling gives better subjective quality.

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“…2 and 4. In Table 1, the values for SNR are given for different va− lues of the contrast S, and for the number of sensors N = 2 16 .…”

Section: Numerical Results and Comparison With Log-polar Samplingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal polar image sampling

Dincic

Perić

Jovanović

2011

Opto-Electronics Review

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“…These parameters are the output of the search radar and they are more suitable for PHT. A similar transform is used in log-polar sensors for image processing [10]. The proposed polar Hough transform represents each line point in the form…”

Section: Polar Hough Detectormentioning

confidence: 99%

Radar Detection and Track Determination with a Transform Analogous to the Hough Transform

Garvanov¹,

Kabakchiev²

2006

2006 International Radar Symposium

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We propose in this paper the standard Hough transform to be changed with analogous polar Hough transform (PHT). The obtained polar Hough detection algorithm is very comfortable for track and target detection because the input parameters of this transform are the output parameters of the search radar. Another important advantage is the transform stability when the target changes its speed and flies at different azimuths.

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“…The former typically employs a pan-tilt-zoom (PTZ) camera such that the point of interest is aligned with the optical axis and projected at the image center (fovea) [18].The latter usually considers a stereo head [1], with the point of interest being foveated by intersecting the optical axes of both cameras at the exact target location (the vergence/fixation point). Since the fixation point lies in the horopter [8], many binocular systems use target disparity between retinas as feedback control signal [6].…”

Section: Introductionmentioning

confidence: 99%

Active stereo tracking of multiple free-moving targets

Perdigoto¹,

Barreto²,

Caseiro³

et al. 2009

2009 IEEE Conference on Computer Vision and Pattern Recognition

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This article presents a general approach for the active stereo tracking of multiple moving targets. The problem is formulated on the plane, where cameras are modeled as "line scan cameras" and targets are described as points with unconstrained motion. We propose to control the active system parameters in such a manner that the images of the targets in the two views are related by an homography. This homography is specified during the design stage and implicitly encodes the tracking behavior. It is shown that this formulation leads to an elegant geometric framework that enables to decide about the feasibility of a particular active tracking task. We apply it to prove that two cameras with rotation and zoom control, can track up to three moving targets, while assuring that the image location of each target is the same for both views. In addition, the framework is also useful for devising tracking strategies and deriving the required control equations. This feature is illustrated through a real experiment on tracking two independent targets using a binocular stereo head.

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Efficient active monocular fixation using the log-polar sensor

Cited by 8 publications

References 26 publications

Optimal polar image sampling

Optimal polar image sampling

Radar Detection and Track Determination with a Transform Analogous to the Hough Transform

Active stereo tracking of multiple free-moving targets

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