Dense connection decoding network for crisp contour detection. Issue 4 (4th January 2021)

• Record Type: Journal Article
• Authors: Xu, Guili; Lin, Chuan; Cheng, Yuehua
• Is Part Of: IET image processing. Volume 15:Issue 4(2021)
• Page Start: 956
• Page End: 963
• Publication Date: 2021-01-04
• Subjects: Optical, image and video signal processing -- Image recognition -- Computer vision and image processing techniques; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12076
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: In the past few years, contour detection algorithm has made obvious progress with the help of convolutional neural networks. The aim of this paper is to present a novel network connecting low‐ and high‐resolution features to make the network achieving richer feature representation. First, VGG net is used as encoding part with outputting the features of different resolutions, and then the feature maps are combined in some specific resolution with up‐ or down‐sample method. The combining process can be stack step‐by‐step. The proposed network makes the encoding part deeper to extract richer convolutional features. The experiments have shown that the proposed method improves the contour detection performances and outperform some existed convolutional neural networks based methods on BSDS500 and NYUD‐V2 datasets.