Analog VLSI implementation of spatio-temporal frequency tuned visual motion algorithms

Higgins, Charles M.; Pant, Vivek; Deutschmann, R. A.

doi:10.1109/tcsi.2004.841601

Cited by 13 publications

(18 citation statements)

References 42 publications

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“…Due to transistor mismatch, the motion detector response of each pixel has a small random dc offset. The contrast and spatio-temporal frequency response of this detector were discussed in detail in [23].…”

Section: Characterization Resultsmentioning

confidence: 99%

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A Biomimetic VLSI Sensor for Visual Tracking of Small Moving Targets

Higgins

Pant

2004

IEEE Trans. Circuits Syst. I

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Taking inspiration from the visual system of the fly, we describe and characterize a monolithic analog very large-scale integration sensor, which produces control signals appropriate for the guidance of an autonomous robot to visually track a small moving target. This sensor is specifically designed to allow such tracking even from a moving imaging platform which experiences complex background optical flow patterns. Based on relative visual motion of the target and background, the computational model implemented by this sensor emphasizes any small-field motion which is inconsistent with the wide-field background motion.Index Terms-Analog very large-scale integration (VLSI), biomimetic, spatiotemporal frequency, target tracking, visual motion.

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Section: Characterization Resultsmentioning

confidence: 99%

“…The HR motion detector has been described in detail and fully characterized in [23]. Used for phototransduction in the HR motion pixel (Fig.…”

Section: A Hr Motion Detectormentioning

confidence: 99%

A Biomimetic VLSI Sensor for Visual Tracking of Small Moving Targets

Higgins

Pant

2004

IEEE Trans. Circuits Syst. I

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“…Arreguit et al [64] presented the first 2-D array for block matching, which used spatial edges as features and was designed for use as a pointing device (computer Barlow-Levick (center), and Adelson-Bergen (right) models, similar to that in [28]. Spatial filters (dark blue) can either differ in location (as shown) or in phase.…”

Section: ) Block Matchingmentioning

confidence: 99%

Bioinspired Visual Motion Estimation

Orchard

Etienne‐Cummings

2014

Proc. IEEE

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Visual motion estimation is a computationally intensive, but important task for sighted animals. Replicating the robustness and efficiency of biological visual motion estimation in artificial systems would significantly enhance the capabilities of future robotic agents. 25 years ago, in this very journal, Carver Mead outlined his argument for replicating biological processing in silicon circuits. His vision served as the foundation for the field of neuromorphic engineering, which has experienced a rapid growth in interest over recent years as the ideas and technologies mature. Replicating biological visual sensing was one of the first tasks attempted in the neuromorphic field. In this paper we focus specifically on the task of visual motion estimation. We describe the task itself, present the progression of works from the early first attempts through to the modern day state-of-the-art, and provide an outlook for future directions in the field.Comment: 16 pages, 11 figures, 1 tabl

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“…For example, Etienne-Cummings et al use a pair of spatial filters which are discrete approximations to first and second-order derivatives of Gaussian functions, and two pairs of third-order temporal filters with zeros at the origin used to alter the filter phase and approximate a quadrature phase relationship between the filters 1057-7130/$20.00 © 2006 IEEE [1]. Higgins et al approximate the responses of a pair of quadrature phase spatial filters by using spatially offset outputs from a single Gabor-like spatial filter, and approximate the responses of pairs of quadrature phase temporal filters either by unfiltered and low-pass filtered versions of the spatial filter outputs in a monolithic implementation [2], [3] or by two spike integrator circuits with different phase delays in a multi-chip system [4].…”

Section: Introductionmentioning

confidence: 99%

“…1 is four, compared to eight in [1]. Although it is possible to reduce the total filter order when using four independent filters to four [4] or even two [2], [3], this comes at the expense of a poorer approximation to the quadrature phase relationship.…”

Section: Introductionmentioning

confidence: 99%

An on-off Temporal Filter Circuit for Computing Motion Energy

Shi

Tsang

2006

IEEE Trans. Circuits Syst. II

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Abstract-Motion energy units are important components of biologically inspired algorithms for image motion analysis. We describe an analog VLSI temporal filter circuit that can be cascaded with a spatial filter to implement the spatio-temporal filters required to compute motion energy. This circuit adopts an ON-OFF differential representation used by a previously developed chip for spatial filtering. It has two main advantages over previous continuous-time temporal filtering circuits used to compute motion energy. First, its outputs are in exact quadrature phase, rather than an approximation. Second, it halves the total temporal filter order required to compute motion energy. We describe the filter architecture, analyze its dynamics theoretically, and provide measurement results from a prototype fabricated in a 1.5-m CMOS n-well process.

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Analog VLSI implementation of spatio-temporal frequency tuned visual motion algorithms

Cited by 13 publications

References 42 publications

A Biomimetic VLSI Sensor for Visual Tracking of Small Moving Targets

A Biomimetic VLSI Sensor for Visual Tracking of Small Moving Targets

Bioinspired Visual Motion Estimation

An on-off Temporal Filter Circuit for Computing Motion Energy

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