Image Decomposition by Answer-in-Weights Neural Network
Summary :
We propose an algorithm for image decomposition based on Hadamard functions, realized by answer-in-weights neural network, which has simple architecture and is explored with steepest decent method. This scheme saves memory consumption and it converges fast. Simulations with least mean square (LMS) and absolute mean (AM) errors on a 128
- Publication
- IEICE TRANSACTIONS on Information Vol.E78-D No.9 pp.1221-1224
- Publication Date
- 1995/09/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- LETTER
- Category
- Image Processing, Computer Graphics and Pattern Recognition
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Iren VALOVA, Keisuke KAMEYAMA, Yukio KOSUGI, "Image Decomposition by Answer-in-Weights Neural Network" in IEICE TRANSACTIONS on Information,
vol. E78-D, no. 9, pp. 1221-1224, September 1995, doi: .
Abstract: We propose an algorithm for image decomposition based on Hadamard functions, realized by answer-in-weights neural network, which has simple architecture and is explored with steepest decent method. This scheme saves memory consumption and it converges fast. Simulations with least mean square (LMS) and absolute mean (AM) errors on a 128
URL: https://global.ieice.org/en_transactions/information/10.1587/e78-d_9_1221/_p
Copy
@ARTICLE{e78-d_9_1221,
author={Iren VALOVA, Keisuke KAMEYAMA, Yukio KOSUGI, },
journal={IEICE TRANSACTIONS on Information},
title={Image Decomposition by Answer-in-Weights Neural Network},
year={1995},
volume={E78-D},
number={9},
pages={1221-1224},
abstract={We propose an algorithm for image decomposition based on Hadamard functions, realized by answer-in-weights neural network, which has simple architecture and is explored with steepest decent method. This scheme saves memory consumption and it converges fast. Simulations with least mean square (LMS) and absolute mean (AM) errors on a 128
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - Image Decomposition by Answer-in-Weights Neural Network
T2 - IEICE TRANSACTIONS on Information
SP - 1221
EP - 1224
AU - Iren VALOVA
AU - Keisuke KAMEYAMA
AU - Yukio KOSUGI
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E78-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 1995
AB - We propose an algorithm for image decomposition based on Hadamard functions, realized by answer-in-weights neural network, which has simple architecture and is explored with steepest decent method. This scheme saves memory consumption and it converges fast. Simulations with least mean square (LMS) and absolute mean (AM) errors on a 128
ER -
English
