Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟱𝟳 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗕𝗶𝗻𝗮𝗿𝘆 𝗜𝗺𝗮𝗴𝗲 𝗦𝗸𝗲𝗹𝗲𝘁𝗼𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗨𝘀𝗶𝗻𝗴 2-𝗦𝘁𝗮𝗴𝗲 𝗨-𝗡𝗲𝘁 Follow me for a similar post: 🇮🇳 Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗕𝗶𝗻𝗮𝗿𝘆 𝗜𝗺𝗮𝗴𝗲 𝗦𝗸𝗲𝗹𝗲𝘁𝗼𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗨𝘀𝗶𝗻𝗴 2-𝗦𝘁𝗮𝗴𝗲 𝗨-𝗡𝗲𝘁 🔸 This paper is published arxiv2021. 🔸 Skeletonization is an interesting task because it collapses object shapes into their backbone structure, allowing for constructing a minimal representation of it. Such skeletal representation are useful for many purposes. One simple example of that would be object detection by skeleton matching. Another more involved application is anatomical labelling of different parts of the body such as pulmonary airway trees which requires extracting and identifying different branches of such airways. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Object Skeletonization is the process of extracting skeletal, line-like representations of shapes. 🔹It provides a very useful tool for geometric shape understanding and minimal shape representation. 🔸It also has a wide variety of applications, most notably in anatomical research and activity detection. 🔹Several mathematical algorithmic approaches have been developed to solve this problem, and some of them have been proven quite robust. 🔸However, a lesser amount of attention has been invested into deep learning solutions for it. 🔹In this paper, we use a 2-stage variant of the famous U-Net architecture to split the problem space into two sub-problems: shape minimization and corrective skeleton thinning. 🔸Our model produces results that are visually much better than the baseline SkelNetOn model. 🔹We propose a new metric, M-CCORR, based on normalized correlation coefficients as an alternative to F1 for this challenge as it solves the problem of class imbalance, managing to recognize skeleton similarity without suffering from F1's over-sensitivity to pixel-shifts. #computervision #artificialintelligence #innovation