论文标题
自动图像分割的统一区域，边缘和轮廓模型的深入学习
Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation
论文作者
论文摘要
图像分割是计算机视觉中的一个基本且具有挑战性的问题，其应用程序涵盖了多个区域，例如医疗成像，遥感和自动驾驶汽车。最近，卷积神经网络（CNN）在自动分割管道的设计中获得了吸引力。尽管基于CNN的模型擅长从原始图像数据中学习抽象功能，但它们的性能取决于合适的培训数据集的可用性和大小。此外，这些模型通常无法捕获对象边界的细节，并概括为看不见的类别。在本论文中，我们设计了解决这些问题的新方法，并为医学成像和主流计算机视觉中的全自动语义细分建立了强大的表示学习框架。特别是，我们的贡献包括（1）用于计算机视觉和医学图像分析的最先进的2D和3D图像分割网络，（2）一个可端到端的可训练的图像分割框架，该框架将CNN和具有可学习参数的可学习参数统一，以快速，可靠的对象描述，（3）不阐明对象的新型方法，（3）新颖的方法，是一种新型的边缘和文学网络，该模型是针对4段的（4）。有监督的设置和半监督的设置，在训练数据有限的情况下，利用潜在和图像空间之间的协同作用来学习分段图像。
Image segmentation is a fundamental and challenging problem in computer vision with applications spanning multiple areas, such as medical imaging, remote sensing, and autonomous vehicles. Recently, convolutional neural networks (CNNs) have gained traction in the design of automated segmentation pipelines. Although CNN-based models are adept at learning abstract features from raw image data, their performance is dependent on the availability and size of suitable training datasets. Additionally, these models are often unable to capture the details of object boundaries and generalize poorly to unseen classes. In this thesis, we devise novel methodologies that address these issues and establish robust representation learning frameworks for fully-automatic semantic segmentation in medical imaging and mainstream computer vision. In particular, our contributions include (1) state-of-the-art 2D and 3D image segmentation networks for computer vision and medical image analysis, (2) an end-to-end trainable image segmentation framework that unifies CNNs and active contour models with learnable parameters for fast and robust object delineation, (3) a novel approach for disentangling edge and texture processing in segmentation networks, and (4) a novel few-shot learning model in both supervised settings and semi-supervised settings where synergies between latent and image spaces are leveraged to learn to segment images given limited training data.
