TY  - CONF
AU  - Patricia Suarez
AU  - Angel Sappa
AU  - Boris X. Vintimilla
A2  - ECMSM
PY  - 2017//
TI  - Cross-Spectral Image Patch Similarity using Convolutional Neural Network
BT  - IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics
N2  - The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a lowcost hardware. Hence, obtained results are compared with bothclassical approaches, showing improvements, and a state of the art CNN based approach.
L1  - http://refbase.cvc.uab.es/files/SSV2017a.pdf
UR  - http://dx.doi.org/10.1109/ECMSM.2017.7945888
N1  - ADAS; 600.086; 600.118
ID  - Patricia Suarez2017
ER  -