Horizontal Attention Convolution Layer for Stereo Matching
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Obtaining a disparity map with stereo matching is one of the most important research topics in areas such as image processing and computer vision. Disparity maps are frequently used by autonomous systems that need depth information of the environment. Recently, high accuracy disparity maps have been obtained with end-to-end deep learning. In this study, a horizontal attention-based convolution layer has been proposed in order to better extract the sequential information in the horizontal plane in the rectified stereo images in methods based on deep learning. The proposed structure has been applied to the DispNetC network, which has been widely used in the literature, and has increased the performance of the network. On the other hand, the proposed method have a very low effect on the network's runtime. The results obtained are shown on the Scene Flow dataset. The codes of the study are available at the following address: https://github.com/aemlek/HADN
Açıklama
29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORK
Anahtar Kelimeler
disparity map, stereo vision, convolutional neural networks
Kaynak
29th Ieee Conference on Signal Processing and Communications Applications (Siu 2021)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
