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Jerome D. Tietz

dblp:64/6393 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Computational photography and imaging · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging
image acquisition
0.012001
Image capture: simulation of sensor responses from hyperspectral images · IEEE Trans. Image Process. 2001
Computational photography and imaging › spectral imaging
hyperspectral imaging
0.012001
Image capture: simulation of sensor responses from hyperspectral images · IEEE Trans. Image Process. 2001

Methods — techniques the papers use, named apart from their topics

spectral sensitivity modeling · 0.0simulation · 0.0
YearPublicationVenuePosition
2001 Image capture: simulation of sensor responses from hyperspectral images
abstract
This paper describes the design and performance of an image capture simulator. The general model underlying the simulator assumes that the image capture device contains multiple classes of sensors with different spectral sensitivities and that each sensor responds in a known way to irradiance over most of its operating range. The input to the simulator is a set of narrow-band images of the scene taken with a custom-designed hyperspectral camera system. The parameters for the simulator are the number of sensor classes, the sensor spectral sensitivities, the noise statistics and number of quantization levels for each sensor class, the spatial arrangement of the sensors and the exposure duration. The output of the simulator is the raw image data that would have been acquired by the simulated image capture device. To test the simulator, we acquired images of the same scene both with the hyperspectral camera and with a calibrated Kodak DCS-200 digital color camera. We used the simulator to predict the DCS-200 output from the hyperspectral data. The agreement between simulated and acquired images validated the image capture response model and our simulator implementation. We believe the simulator will provide a useful tool for understanding the effect of varying the design parameters of an image capture device.
Poorvi L. Vora, Joyce E. Farrell, Jerome D. Tietz, David H. Brainard
IEEE Trans. Image Process.3