@inproceedings{2e6b3661f71843fcb3ea11a757104b7a,
title = "Searching ROI for Object Detection based on CNN",
abstract = "Several studies have explored the structural design of CNN to improve the network's performance since a well-designed feature extractor can benefit convolution-based tasks. Although CNNs are able to learn important patterns on raw images, images may contain unpredictable noise that can negatively influence the convolutional stage. Feature extraction cannot always accurately capture the desired features based solely on the input image, but including extra information could improve the result. This paper proposes a fusion input design to generate an additional feature that a CNN can use to provide extra ROI information. Whether a model can utilize the additional information is a determining factor that affects the performance improvement. The proposed method is tested on two public datasets with different structural designs. Overall, the results indicate that additional ROI information can deliver benefits to specific tasks.",
keywords = "Convolution Neural Network, Deep Learning, Region of Interest",
author = "Wu, {Chia Lin} and Lin, {Chih Yang} and Phanuvich Hirunsirisombut and Ng, {Hui Fuang} and Shih, {Timothy K.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 ; Conference date: 03-12-2019 Through 06-12-2019",
year = "2019",
month = dec,
doi = "10.1109/ISPACS48206.2019.8986381",
language = "???core.languages.en_GB???",
series = "Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019",
}