Faculty Publications
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Item Project spear: Reporting human trafficking using crowdsourcing(Institute of Electrical and Electronics Engineers Inc., 2017) Roshan, S.; Vinay Kumar, S.; Kumar, M.Human trafficking is a severe crime which is prevailing in the society. It is hard to track and report incidents of human trafficking to the concerned authorities, due to the complex nature of this crime. This paper introduces a crowdsourcing approach to report incidents of human trafficking, using a mobile application. Crowdsourcing is the practice of obtaining information from a large number of people. Information about human trafficking incidents reported using this mobile application is forwarded to the concerned authorities of the country where the crime has been reported. A user can also upload crime scene photos and provide details of the crime location using Global Positioning System. The mobile application introduced in this paper currently focuses on top ten countries which have the highest rates of human trafficking in the world, while very basic support for the rest of the countries. Since the process of reporting human trafficking incidents to the authorities can be complex, time consuming and dangerous, the mobile application allows users to stay anonymous. The aim of this mobile application is to create huge impact in the fight against human trafficking, by using the collective power of the crowd to report incidents of human trafficking. Also, a section of the mobile application is dedicated to educating the user about basic knowledge of human trafficking, its types and measures taken by various governments to fight against it. © 2017 IEEE.Item Machine learning for mobile wound assessment(SPIE spie@spie.org, 2018) Kamath, S.; Sirazitdinova, E.; Deserno, T.M.Chronic wounds affect millions of people around the world. In particular, elderly persons in home care may develop decubitus. Here, mobile image acquisition and analysis can provide a good assistance. We develop a system for mobile wound capture using mobile devices such as smartphones. The photographs are acquired with the integrated camera of the device and then calibrated and processed to determine the size of various tissues that are present in a wound, i.e., necrotic, sloughy, and granular tissue. The random forest classifier based on various color and texture features is used for that. These features are Sobel, Hessian, membrane projections, variance, mean, median, anisotropic diffusion, and bilateral as well as Kuwahara filters. The resultant probability output is thresholded using the Otsu technique. The similarity between manual ground truth labeling and the classification is measured. The acquired results are compared to those achieved with a basic technique of color thresholding, as well as those produced by the SVM classifier. The fast random forest was found to produce better results. It is also seen to have a superior performance when the method is applied only to the wound regions having the background subtracted. Mean similarity is 0.89, 0.39, and 0.44 for necrotic, sloughy, and granular tissue, respectively. Although the training phase is time consuming, the trained classifier performs fast enough to be implemented on the mobile device. This will allow comprehensive monitoring of skin lesions and wounds. © 2018 SPIE.