2024/07/15 by Antoine Habis, Habis, Antoine, Vannary Meas‐Yedid +5
Computer Science · Engineering · #Advanced Vision and Imaging #Computer Graphics and Visualization Techniques #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.2407.10696
This paper introduces a novel approach that combines unsupervised active contour models with deep learning for robust and adaptive image segmentation. Indeed, traditional active contours, provide a flexible framework for contour evolution and learning offers the capacity to learn intricate features and patterns directly from raw data. Our proposed methodology leverages the strengths of both paradigms, presenting a framework for both unsupervised and one-shot approaches for image segmentation. It is capable of capturing complex object boundaries without the need for extensive labeled training data. This is particularly required in histology, a field facing a significant shortage of annotations due to the challenging and time-consuming nature of the annotation process. We illustrate and compare our results to state of the art methods on a histology dataset and show significant improvements.