UAV Autonomous Localization using Macro-Features Matching with a CAD Model.

Research in the field of autonomous Unmanned Aerial Vehicles (UAVs) has significantly advanced in recent years, mainly due to their relevance in a large variety of commercial, industrial, and military applications. However, UAV navigation in GPS-denied environments continues to be a challenging problem that has been tackled in recent research through sensor-based approaches. This paper presents a novel offline, portable, real-time in-door UAV localization technique that relies on macro-feature detection and matching. The proposed system leverages the support of machine learning, traditional computer vision techniques, and pre-existing knowledge of the environment. The main contribution of this work is the real-time creation of a macro-feature description vector from the UAV captured images which are simultaneously matched with an offline pre-existing vector from a Computer-Aided Design (CAD) model. This results in a quick UAV localization within the CAD model. The effectiveness and accuracy of the proposed system were evaluated through simulations and experimental prototype implementation. Final results reveal the algorithm's low computational burden as well as its ease of deployment in GPS-denied environments.

Automatic diagnosis of melanoma using linear and nonlinear features from digital image.

Melanoma is the most serious type of skin cancer and causes more deaths than other forms of skin cancer. It is a tiny small malignant mole that is usually black or brown but also appears in other color patterns. Early detection of melanoma is key as this is the time period when it is most likely to be cured. Due to the advancement of smartphone technology, automatic and efficient detection of melanoma mole using a smartphone is an active area of research. In this study, we developed an automatic melanoma diagnosis system using images captured from the digital camera. Our work differs from other studies in the area of segmentation of melanoma region and consideration of non-linear features for classification of malignant and benign melanoma. In this paper, a combination of Otsu and k-means clustering segmentation methods are applied to automatically segment and extract the borders of affected region with satisfactory accuracy. Also, we explored and extracted different non-linear features along with color and texture features existed in literature from the lesion mole. The effectiveness of these features was predicted with a machine learning model consisting of five different classifiers. Our model predicted the diagnosis of mole with an accuracy of 89.7%, i.e., around 10% more than reported results by others (to the best of our knowledge) with the same database.