GU Lin

Segment 2D and 3D Filaments by Learning Sructured and Contextual Features

Published date : 31 Oct 2016

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

type
Journal Paper
journal
IEEE Transactions on Medical Imaging, Issue 99, 2016, doi: 10.1109/TMI.2016.2623357
Impact Factor
2.536

Outdoor Shadow Modelling and Its Applications

Published date : 15 Mar 2016

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.

type
Book/Book Chapter
journal
Handbook of Pattern Recognition and Computer Vision, 5th Edition, 15 Mar 2016, ISBN: 978-981 4656258

Learning to Boost Filamentary Structure Segmentation

Published date : 13 Dec 2015

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:

type
Conference Paper/Poster
journal
International conference on computer vision (ICCV), 13-16 Dec 2015

author

Segmentation and Estimation of Spatially Varying Illumination

Published date : 12 Jun 2014

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

type
Journal Paper
journal
IEEE Transactions on Image Processing (Volume:23, Issue: 8 ), Pg 3478-3489 doi: 10.1109/TIP.2014.2330768