TY - GEN
T1 - Spiking cortical model for geometry invariant and antinoise texture retrieval
AU - Yang, Ruijia
AU - Lyu, Congyi
AU - Liu, Yunhui
AU - Zhou, Weiguo
AU - Chen, Chen
AU - Jiang, Xin
AU - Li, Peng
AU - Chen, Haoyao
AU - Xu, Ruishuo
AU - Wang, Yukun
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In recent years, CBIR (content-based image retrieval) becomes a new hotspot. In the technology, image querying is achieved based on the characteristics of the color, shape, texture, spatial position of the object or the combination of these features. As the images are the most intuitive contents in the multimedia, content-based image retrieval is a very important problem in the multimedia information processing. Spiking cortical model (SCM) used in this paper is a neural network algorithm that generates a series of binary pulse images when excited by the grayscale or color images. And it has a superior performance in the feature extraction and the texture retrieval of images due to the properties of anti-noise and the geometry invariant of rotation, scale and translation. In order to improve the speed of texture retrieval, SCM is modeled based on FPGA in this paper.
AB - In recent years, CBIR (content-based image retrieval) becomes a new hotspot. In the technology, image querying is achieved based on the characteristics of the color, shape, texture, spatial position of the object or the combination of these features. As the images are the most intuitive contents in the multimedia, content-based image retrieval is a very important problem in the multimedia information processing. Spiking cortical model (SCM) used in this paper is a neural network algorithm that generates a series of binary pulse images when excited by the grayscale or color images. And it has a superior performance in the feature extraction and the texture retrieval of images due to the properties of anti-noise and the geometry invariant of rotation, scale and translation. In order to improve the speed of texture retrieval, SCM is modeled based on FPGA in this paper.
UR - https://www.scopus.com/pages/publications/85050645760
U2 - 10.1109/RCAR.2017.8311936
DO - 10.1109/RCAR.2017.8311936
M3 - 会议稿件
AN - SCOPUS:85050645760
T3 - 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
SP - 645
EP - 650
BT - 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
Y2 - 14 July 2017 through 18 July 2017
ER -