Share Email Print
cover

Proceedings Paper

Very deep recurrent convolutional neural network for object recognition
Author(s): Sourour Brahimi; Najib Ben Aoun; Chokri Ben Amar
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.

Paper Details

Date Published: 17 March 2017
PDF: 5 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 1034107 (17 March 2017); doi: 10.1117/12.2268672
Show Author Affiliations
Sourour Brahimi, Univ. de Sfax (Tunisia)
Najib Ben Aoun, Univ. de Sfax (Tunisia)
Al Baha Univ. (Saudi Arabia)
Chokri Ben Amar, Univ. de Sfax (Tunisia)


Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

© SPIE. Terms of Use
Back to Top