Abstract
In this paper, we propose an analog circuit for binary neural firing model that can extract various image features. Both computational and hardware models were designed for feature extraction algorithm that explores the dependency of firing rates on the pixel intensity in alignment with inhibition and excitation principles. The circuit for translating each pixel intensity into a series of pulses is implemented using a well timed circuit consisting of a series of difference circuits, comparators for thresholding, memory circuits and resistive networks for averaging. The circuit can be configured to select a required number of inhibition and excitation pixels, and can be used to generate a range of filtered signals from different sized filter windows. The difference between the features from different sized filter windows are used to extract the fine to coarse features from the images reflected as image edges, background features, and object textures.
| Original language | English |
|---|---|
| Title of host publication | 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1098-1102 |
| Number of pages | 5 |
| Volume | 2017-January |
| ISBN (Electronic) | 9781509063673 |
| DOIs | |
| Publication status | Published - Nov 30 2017 |
| Externally published | Yes |
| Event | 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India Duration: Sep 13 2017 → Sep 16 2017 |
Conference
| Conference | 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 |
|---|---|
| Country | India |
| City | Manipal, Mangalore |
| Period | 9/13/17 → 9/16/17 |
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Keywords
- Edge detection
- Image filters
- LGN
- Neural circuits
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Information Systems
Cite this
Coarse to fine difference edge detection with binary neural firing model. / Dorzhigulov, Anuar; Berdaliyev, Yerlan; James, Alex Pappachen.
2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 1098-1102.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Coarse to fine difference edge detection with binary neural firing model
AU - Dorzhigulov, Anuar
AU - Berdaliyev, Yerlan
AU - James, Alex Pappachen
PY - 2017/11/30
Y1 - 2017/11/30
N2 - In this paper, we propose an analog circuit for binary neural firing model that can extract various image features. Both computational and hardware models were designed for feature extraction algorithm that explores the dependency of firing rates on the pixel intensity in alignment with inhibition and excitation principles. The circuit for translating each pixel intensity into a series of pulses is implemented using a well timed circuit consisting of a series of difference circuits, comparators for thresholding, memory circuits and resistive networks for averaging. The circuit can be configured to select a required number of inhibition and excitation pixels, and can be used to generate a range of filtered signals from different sized filter windows. The difference between the features from different sized filter windows are used to extract the fine to coarse features from the images reflected as image edges, background features, and object textures.
AB - In this paper, we propose an analog circuit for binary neural firing model that can extract various image features. Both computational and hardware models were designed for feature extraction algorithm that explores the dependency of firing rates on the pixel intensity in alignment with inhibition and excitation principles. The circuit for translating each pixel intensity into a series of pulses is implemented using a well timed circuit consisting of a series of difference circuits, comparators for thresholding, memory circuits and resistive networks for averaging. The circuit can be configured to select a required number of inhibition and excitation pixels, and can be used to generate a range of filtered signals from different sized filter windows. The difference between the features from different sized filter windows are used to extract the fine to coarse features from the images reflected as image edges, background features, and object textures.
KW - Edge detection
KW - Image filters
KW - LGN
KW - Neural circuits
UR - http://www.scopus.com/inward/record.url?scp=85042775463&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042775463&partnerID=8YFLogxK
U2 - 10.1109/ICACCI.2017.8125988
DO - 10.1109/ICACCI.2017.8125988
M3 - Conference contribution
VL - 2017-January
SP - 1098
EP - 1102
BT - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -