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Convolutional neural networks with superpixels : toward detail-preserving image segmentation (本文)

鈴木, 哲平 慶應義塾大学

2022.03.23

概要

In the computer vision field, image recognition and understanding are the main tasks. In particular, dense prediction tasks, such as image segmentation and depth estimation, are important for image editing and scene understanding. To solve such tasks, fully convolutional networks (FCNs), which is a variant of convolutional neural networks (CNNs), have been proposed and have become a de fact standard method. Although FCNs achieved better accuracy for image seg- mentation tasks than traditional methods, detailed information, such as image edges, boundaries, and small and/or thin objects, is often missed due to the downsampling layers, which are used for reducing computational costs and expanding receptive fields. In this thesis, the detail-preserving framework utilizing superpixels in downsampling layers is proposed. The proposed method mitigates the detailed in- formation loss by incorporating it into existing FCNs.

Chapter 1 describes image segmentation, its application, and research questions.

Chapter 2 describes existing image segmentation methods using classi- cal Markov random fields and deep neural networks and their variants.

Chapter 3 defines superpixel segmentation as the maximization of mutual information and then proposes an unsupervised superpixel segmentation framework using CNNs. The proposed method shows the CNNs have a strong prior for superpixel segmentation.

Chapter 4 describes graph convolutional networks and then defines convolution operations for superpixel images. Compared to general CNNs and the model replacing the convolution with the proposed convolution shows the effectiveness of superpixels in CNNs.

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