Vector field visualization: Analysis of feature extraction methods

G. Harikumar; Y. Bresler

doi:10.1109/ICIP.1994.413529

Vector field visualization: Analysis of feature extraction methods

G. Harikumar
, Y. Bresler

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper addresses the problem of the visualization of vector-valued images. Attempting to synthesize a display matched to the capabilities of a human observer, we have reduced the problem to the extraction of the best linear feature of the vector field. In previous work, we have proposed and demonstrated several new nonparametric feature extraction criteria (projection indices) that make use of both the spatial and multivariate structures of the data. We present a theoretical analysis of these projection indices and a Monte-Carlo study of their effectiveness. The study uses performance measures derived from a decision-theoretic model of the human observer.

Original language	English (US)
Article number	413529
Pages (from-to)	51-55
Number of pages	5
Journal	Proceedings - International Conference on Image Processing, ICIP
Volume	2
DOIs	https://doi.org/10.1109/ICIP.1994.413529
State	Published - 1994
Event	The 1994 1st IEEE International Conference on Image Processing - Austin, TX, USA Duration: Nov 13 1994 → Nov 16 1994

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/ICIP.1994.413529

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title = "Vector field visualization: Analysis of feature extraction methods",

abstract = "This paper addresses the problem of the visualization of vector-valued images. Attempting to synthesize a display matched to the capabilities of a human observer, we have reduced the problem to the extraction of the best linear feature of the vector field. In previous work, we have proposed and demonstrated several new nonparametric feature extraction criteria (projection indices) that make use of both the spatial and multivariate structures of the data. We present a theoretical analysis of these projection indices and a Monte-Carlo study of their effectiveness. The study uses performance measures derived from a decision-theoretic model of the human observer.",

author = "G. Harikumar and Y. Bresler",

note = "Supported in part by the Whitaker Foundation, National Science Foundation grant No. MIP 91-57377 and a Schlumberger-Doll research grant. Also supported in part by the Joint Services Electronics Program, grant No. N00014-90-J-1270.; The 1994 1st IEEE International Conference on Image Processing ; Conference date: 13-11-1994 Through 16-11-1994",

year = "1994",

doi = "10.1109/ICIP.1994.413529",

language = "English (US)",

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journal = "Proceedings - International Conference on Image Processing, ICIP",

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T2 - The 1994 1st IEEE International Conference on Image Processing

AU - Harikumar, G.

AU - Bresler, Y.

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PY - 1994

Y1 - 1994

N2 - This paper addresses the problem of the visualization of vector-valued images. Attempting to synthesize a display matched to the capabilities of a human observer, we have reduced the problem to the extraction of the best linear feature of the vector field. In previous work, we have proposed and demonstrated several new nonparametric feature extraction criteria (projection indices) that make use of both the spatial and multivariate structures of the data. We present a theoretical analysis of these projection indices and a Monte-Carlo study of their effectiveness. The study uses performance measures derived from a decision-theoretic model of the human observer.

AB - This paper addresses the problem of the visualization of vector-valued images. Attempting to synthesize a display matched to the capabilities of a human observer, we have reduced the problem to the extraction of the best linear feature of the vector field. In previous work, we have proposed and demonstrated several new nonparametric feature extraction criteria (projection indices) that make use of both the spatial and multivariate structures of the data. We present a theoretical analysis of these projection indices and a Monte-Carlo study of their effectiveness. The study uses performance measures derived from a decision-theoretic model of the human observer.

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U2 - 10.1109/ICIP.1994.413529

DO - 10.1109/ICIP.1994.413529

M3 - Conference article

AN - SCOPUS:84997706190

SN - 1522-4880

VL - 2

SP - 51

EP - 55

JO - Proceedings - International Conference on Image Processing, ICIP

JF - Proceedings - International Conference on Image Processing, ICIP

M1 - 413529

Y2 - 13 November 1994 through 16 November 1994

ER -

Vector field visualization: Analysis of feature extraction methods

Abstract

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