Image recovery from edge primitives

Alter-Gartenberg, Rachel; Huck, Friedrich O.; Narayanswamy, Ramkumar

doi:10.1364/josaa.7.000898

Cited by 15 publications

(3 citation statements)

References 12 publications

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“…where ~o = (2/~re) 1/4 corresponds to the smallest channel in early human vision [21,22], a~ >_ ao controls the edge enhancement bandwidth, ~ controls its amount, and 0.2 < ai < 0.5 controls the smoothing of blank areas. This enhancement filter suppresses the visibility of the quantization errors while sharpening the image transitions, at the cost of a modest reduction in the resolution and sharpness of the representation.…”

Section: Controlled Visual Qualitymentioning

confidence: 99%

Efficient visual communication channels

Alter-Gartenberg

1995

J Math Imaging Vis

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Abstract. This paper compares the efficiency of uniform quantization in the spatial domain with frequency dependent quantization in the spatial frequency domain, in the context of the end-to-end performance of visual-communication channels. Results show that the minimum data density required for informationally lossless transmission depends on the design of the image-gathering device. The information in the acquired signal, not the energy, dictates the trade-off between data transmission and visual quality. Frequency dependent quantization that maintains the information capacity of the channel while reducing the entropy of the encoded signal, improves its information efficiency. Information bit-allocation is preferable for optimized visual communication for restoration, whereas energy bit-allocation can be used only for image reconstruction.

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Section: Controlled Visual Qualitymentioning

confidence: 99%

Efficient visual communication channels

Alter-Gartenberg

1995

J Math Imaging Vis

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“…(3) That is, when the input intensity increases by a factor of c, the height of the spread function also increases by a factor of c while its width decreases by 1//. The IDS model inherently assumes a constant SNR for all intensity levels (photon noise), and its response to ideal step edges obeys Weber's law7.…”

Section: Spread In Summation Networkmentioning

confidence: 99%

“…2 We refer to this response to a step edge as an "edge pattern" . Alter-Gartenberg et al 3 have recently shown that it is possible to recover a nearly complete representation of the original scene using only the information contained in the filtered image. We term these features extracted from the IDS filtered image as "edge primitives" .…”

Section: Introductionmentioning

confidence: 99%

<title>Robust image coding with a model of adaptive retinal processing</title>

Narayanswamy

Alter-Gartenberg

Huck

1991

Optics, Illumination, and Image Sensing for Machine Vision V

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Object structure is one of the most important features for many imaging applications. In many applications in space, recording the spatial structure adequately is a challenge due to the the wide range of illumination conditions encountered. Moreover, communication constraints often limit the amount of data that can be transmitted. Motivated by these concerns, we have developed a coding scheme which is robust to the variations in illumination conditions, preserves high structural fidelity, and provides high compression ratios. The high correlation between the original and decoded images demonstrates the potential of this coding scheme for machine vision applications.

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Spatial reconstruction of signals from short-wavelength cones

Brainard

Williams

1993

Vision Research

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Image recovery from edge primitives

Cited by 15 publications

References 12 publications

Efficient visual communication channels

Efficient visual communication channels

<title>Robust image coding with a model of adaptive retinal processing</title>

Spatial reconstruction of signals from short-wavelength cones

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