Abstract
A two-level 3-D rotational invariant classification is developed based on the fractional differencing model. In the first level, classification is done with a fractal scale, and in the second level, textures are classified in detail with additional frequency parameters. Because of the properties of the fractal scale and multi-level procedure, the proposed 3-D rotational invariant classification scheme reduces the processing time and gives enough accuracy to the classification simultaneously. As a result of a series of experiments involving the differently oriented samples of natural textures, it is concluded that these combined features allow this multi-level classification method to have a strong class separability power for arbitrarily oriented 3-D texture patterns.
| Original language | English |
|---|---|
| Pages (from-to) | 794-799 |
| Number of pages | 6 |
| Journal | Conference Record - Asilomar Conference on Circuits, Systems & Computers |
| Volume | 2 |
| Publication status | Published - 1991 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
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Multi-level 3-D rotational invariant classification. / Kashyap, R. L.; Choe, Y.
In: Conference Record - Asilomar Conference on Circuits, Systems & Computers, Vol. 2, 01.12.1991, p. 794-799.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Multi-level 3-D rotational invariant classification
AU - Kashyap, R. L.
AU - Choe, Y.
PY - 1991/12/1
Y1 - 1991/12/1
N2 - A two-level 3-D rotational invariant classification is developed based on the fractional differencing model. In the first level, classification is done with a fractal scale, and in the second level, textures are classified in detail with additional frequency parameters. Because of the properties of the fractal scale and multi-level procedure, the proposed 3-D rotational invariant classification scheme reduces the processing time and gives enough accuracy to the classification simultaneously. As a result of a series of experiments involving the differently oriented samples of natural textures, it is concluded that these combined features allow this multi-level classification method to have a strong class separability power for arbitrarily oriented 3-D texture patterns.
AB - A two-level 3-D rotational invariant classification is developed based on the fractional differencing model. In the first level, classification is done with a fractal scale, and in the second level, textures are classified in detail with additional frequency parameters. Because of the properties of the fractal scale and multi-level procedure, the proposed 3-D rotational invariant classification scheme reduces the processing time and gives enough accuracy to the classification simultaneously. As a result of a series of experiments involving the differently oriented samples of natural textures, it is concluded that these combined features allow this multi-level classification method to have a strong class separability power for arbitrarily oriented 3-D texture patterns.
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M3 - Conference article
AN - SCOPUS:0026385359
VL - 2
SP - 794
EP - 799
JO - Conference Record - Asilomar Conference on Circuits, Systems & Computers
JF - Conference Record - Asilomar Conference on Circuits, Systems & Computers
SN - 0736-5861
ER -