No abstract
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.
This paper presents the development of a real time tracking algorithm that runs on a 1.2 GHz PC/104 computer onboard a small UAV. The algorithm uses zero mean normalized cross correlation to detect and locate an object in the image. A Kalman filter is used to make the tracking algorithm computationally efficient. Object position in an image frame is predicted using the motion model and a search window centered at the predicted position is generated. Object position is updated with the measurement from object detection. The detected position is sent to the motion controller to move the gimbal so that the object stays at the center of the image frame. Detection and tracking is autonomously carried out on the payload computer and the system is able to work in two different methods. The first method starts detecting and tracking using a stored image patch. The second method allows the operator on the ground to select the interest object for the UAV(Unmanned Aerial Vehicle) to track. The system is capable of re-detecting an object, in the event of tracking failure. Performance of the tracking system was verified both in the lab and on the field by mounting the payload on a vehicle and simulating a flight. Tests show that the system can detect and track a diverse set of objects in real time. Flight testing of the system will be conducted at the next available opportunity. I American Institute of Aeronautics and Astronautics 2 A substantial amount of research on visual tracking with small UAV 2-6 has been carried out and a wide range of object recognition and tracking methods have been used. However existing object recognition algorithms are computationally expensive and run on a powerful ground computer. The ground computer receives video information from the UAV and navigation information is then sent to the UAV. A major disadvantage with running the tracking algorithm on a ground computer is its dependency on communication between the ground computer and the UAV. Communication between the UAV and ground computer are often interrupted due to reliability issues with inexpensive commercial off-the-shelf (COTS) equipment, radio frequency interference (RFI) and Electromagnetic interference (EMI) especially when operated near high populated area. Tracking fails when there is a communication failure between the UAV and ground.The tracking system described in Ref. 2 with small UAVs uses a Commercial off-the-shelf (COTS) image processing software running on a separate ground computer. Video from the onboard camera is transmitted to the ground computer where the images are processed and target information is extracted. Then the information is sent to the guidance and navigation module to calculate the aircraft trajectory. The guidance commands are then sent back to the UAV. This system depends heavily on the communication between the UAV and the ground computer and a communication failure will result in the tracking system failure. Another vision based tracking system for a small UAV with a miniature pan-tilt gimbaled camera w...
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