Welcome to the DC/NoVA Papers We Love meetup!
Papers We Love is an international organization centered around the appreciation of computer science research papers. There's so much we can learn from the landmark research that shaped the field and the current studies that are shaping our future. Our goal is to create a community of tech professionals passionate about learning and sharing knowledge. Come join us!
New to research papers? Watch The Refreshingly Rewarding Realm of Research Papers (https://www.youtube.com/watch?v=8eRx5Wo3xYA) by Sean Cribbs.
Ideas and suggestions are welcome–fill our our interest survey here (https://docs.google.com/forms/d/e/1FAIpQLSeJwLQhnmzWcuyodPrSmqHgqrvNxRbnNSbiWAuwzHwshhy_Sg/viewform) and let us know what motivates you!
// Tentative Schedule
• 7:00-7:30–Informal paper discussion
• 7:30-7:35–Introduction and announcements
• 7:35-8:40–Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background by Garret Vo (https://arxiv.org/abs/1609.08078)
• 8:40-9:00–Informal paper discussion
Excella Consulting Arlington Tech Exchange (https://www.excella.com/events/arlington-tech-exchange)
2300 Wilson Blvd
Arlington, VA 22201
This month, Excella Consulting is hosting us at the Arlington Tech Exchange. It's located conveniently off Wilson Blvd in Arlington. There's parking available, and it's just a quick walk from the Courthouse Metro Station. We'll be on the 6th floor; follow the signs.
If you're late, we totally understand–please still come! Just be sure to slip in quietly if a speaker is presenting.
Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background by Garret Vo and Chiwoo Park
Abstract: "This paper presents a robust regression approach for image binarization under significant background variations and observation noises. The work is motivated by the need of identifying foreground regions in noisy microscopic image or degraded document images, where significant background variation and severe noise make an image binarization challenging. The proposed method first estimates the background of an input image, subtracts the estimated background from the input image, and apply a global thresholding to the subtracted outcome for achieving a binary image of foregrounds. A robust regression approach was proposed to estimate the background intensity surface with minimal effects of foreground intensities and noises, and a global threshold selector was proposed on the basis of a model selection criterion in a sparse regression. The proposed approach was validated using 26 test images and the corresponding ground truths, and the outcomes of the proposed work were compared with those from nine existing image binarization methods. The approach was also combined with three state-of-the-art morphological segmentation methods to show how the proposed approach can improve their image segmentation outcomes."