Paper
17 March 2017 Very deep recurrent convolutional neural network for object recognition
Author Affiliations +
Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 1034107 (2017) https://doi.org/10.1117/12.2268672
Event: Ninth International Conference on Machine Vision, 2016, Nice, France
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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sourour Brahimi, Najib Ben Aoun, and Chokri Ben Amar "Very deep recurrent convolutional neural network for object recognition", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 1034107 (17 March 2017); https://doi.org/10.1117/12.2268672
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Cited by 7 scholarly publications.
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KEYWORDS
Object recognition

Convolutional neural networks

Image classification

Machine vision

Computer vision technology

Image filtering

Surface plasmons

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