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Skeleton matching with applications in Severe Weather Detection

Mohammad Mahdi Kamani, Farshid Farhat, Stephen Wistar, James Z. Wang
Journal Paper Journal of Applied Soft Computing, Elsevier, 2017.

Abstract

Severe weather conditions cause enormous amount of damages around the globe. Bow echo patterns in radar images are associated with a number of these destructive conditions such as damaging winds, hail, thunderstorms, and tornadoes. They are detected manually by meteorologists. In this paper, we propose an automatic framework to detect these atterns with high accuracy by introducing novel skeletonization and shape matching approaches. In this framework, first we extract regions with high probability of occurring bow echo from radar images, and apply our skeletonization method to extract the skeleton of those regions. Next, we prune these skeletons using our innovative pruning scheme with fuzzy logic. Then, using our proposed shape descriptor, Skeleton Context, we can extract bow echo features from these skeletons in order to use them in shape matching algorithm and classification step. The output of classification indicates whether these regions are bow echo with over 97% accuracy.

Shape Matching using Skeleton Context for automated Bow Echo Detection

Mohammad Mahdi Kamani, Farshid Farhat, Stephen Wistar, James Z. Wang
Conference Papers IEEE International Conference on Big Data. December 2016.

Abstract

Severe weather conditions cause enormous amount of damages around the globe. Bow echo patterns in radar images are associated with a number of these destructive conditions such as damaging winds, hail, thunderstorms, and tornadoes. They are detected manually by meteorologists. In this paper, we propose an automatic framework to detect these atterns with high accuracy by introducing novel skeletonization and shape matching approaches. In this framework, first we extract regions with high probability of occurring bow echo from radar images, and apply our skeletonization method to extract the skeleton of those regions. Next, we prune these skeletons using our innovative pruning scheme with fuzzy logic. Then, using our proposed shape descriptor, Skeleton Context, we can extract bow echo features from these skeletons in order to use them in shape matching algorithm and classification step. The output of classification indicates whether these regions are bow echo with over 97% accuracy.

Image Processing in Paintings using Multispectral Imaging

Mohammad Mahdi Kamani
Master Thesis Sharif University of Technology, Tehran, Iran, June 2015
Master Thesis; Mohammad Mahdi Kamani

Considering the tremendous development in imaging systems’ industry, today we canafford to have imaging equipment, capable of taking multispectral images in very highresolution. One of the remarkable benefits of this technology is in the realm of artsand particularly in museums. Taking advantage of the potentials of multispectral, high-quality imaging, curators will be able to probe their priceless works of art ( e.g. paintings) without putting them in danger through invasive research. Besides, one can investigateand control the transformation of these works through time by using this new imagingmethod.

Recently, Multispectral Imaging of paintings, in different frequency bands from approx-imately 300 nm to 1000 nm, has been proposed and used in some museums. Since theseimages are taken with different filters from visible light to infra-red, one might expectthat they contain some data beyond what is seen in visible light images. So these imagescontain data from beneath layers of the paintings, which can be compared with the RGBimage and result in extracting early sketches of the painter which are the basis of thosepaintings. In this project the ultimate goal is to find and extract those regions from mul-tispectral bands that cannot be seen in the RGB image. We use statistical methods andimage processing tools in order to extract those hidden objects automatically by computer.

The process will start with using a statistical tool known as canonical correlation analysisto find a projection which uncorrelates the frequency bands from 3 RGB bands. Thenwe can use canny edge detector to find edges in the resulting image bands from previoussection and in the RGB file. After that we can implement some morphological opera-tions to reduce redundant edges that represent data which can also be found in the RGBfile, or some noisy edges. At the end we implement an algorithm to find edges that canbe linked together to represent a larger object and connect them using active contours.Results show that this approach can be helpful in finding hidden layers of the paintingswhich is a stepping stone to find the method of painters in their paintings.