We focus on the challenging problem of filamentary structure segmentation in both 2D and 3D images, including retinal vessels and neurons, among others. Despite the increasing amount of efforts in learning based methods to tackle this
This chapter deals with shadow modelling and its utilities in shadow detection and weather estimation in out-door space. Noting the diuse skylight is scattered by particles in the atmosphere, we view the skylight, which casts the shadow, as a linear combination of scattered sunlight obeying Rayleigh scattering and Mie theory. Thus, we propose a ratio on the shadow-sunlit boundary which only depends on atmospheric condition. This ratio recasts recovering the shadow areas into a clustering setting making use of active contours.
The challenging problem of filamentary structure segmentation has a broad range of applications in biological and medical fields. A critical yet challenging issue remains on how to detect and restore the small filamentary fragments from backgrounds: The small fragments are of diverse shapes and appearances, meanwhile the backgrounds could be cluttered and ambiguous. Focusing on this issue, this paper proposes an iterative two-step learning-based approach to boost the performance based on a base segmenter arbitrarily chosen from a number of existing segmenters:
In this paper, we present an unsupervised method for segmenting the illuminant regions and estimating the illumination
power spectrum from a single image of a scene lit by multiple light sources. Here, illuminant region segmentation is