Computer Vision has provided immense support to medical diagnostics over the past two decades. Analogous to Non Destructive Testing of mechanical parts, advances in medical imaging has enabled surgeons to determine root cause of an illness by consulting medical images particularly 3-D imaging. 3-D modeling in medical imaging has been pursued using surface rendering, volume rendering and regularization based methods. Tomographic reconstruction in 3D is different from camera based scene reconstruction which has been achieved using various techniques including minimal surfaces, level sets, snakes, graph cuts, silhouettes, multi-scale approach, patchwork etc. In tomography limitations of image aquisition method i-e CT Scan, X Rays and MRI as well as non availability of camera parameters for calibration restrict the quality of final reconstruction. In this work, a comprehensive study of related approaches has been carried out with a view to provide a summary of state of the art 3D modeling algorithms developed over the past four decades and also to provide a foundation study for our future work which will include precise 3D reconstruction of human spine.
Clustering is the most common method for organizing unlabeled data into its natural groups (called clusters), based on similarity (in some sense or another) among data objects. The Partitioning Around Medoids (PAM) algorithm belongs to the partitioning-based methods of clustering widely used for objects categorization, image analysis, bioinformatics and data compression, but due to its high time complexity, the PAM algorithm cannot be used with large datasets or in any embedded or real-time application. In this work, we propose a simple and scalable parallel architecture for the PAM algorithm to reduce its running time. This architecture can easily be implemented either on a multi-core processor system to deal with big data or on a reconfigurable hardware platform, such as FPGA and MPSoCs, which makes it suitable for real-time clustering applications. Our proposed model partitions data equally among multiple processing cores. Each core executes the same sequence of tasks simultaneously on its respective data subset and shares intermediate results with other cores to produce results. Experiments show that the computational complexity of the PAM algorithm is reduced exponentially as we increase the number of cores working in parallel. It is also observed that the speedup graph of our proposed model becomes more linear with the increase in number of data points and as the clusters become more uniform. The results also demonstrate that the proposed architecture produces the same results as the actual PAM algorithm, but with reduced computational complexity.
This paper address the problem area of Unmanned Aerial Vehicles (UAV) emergency scenarios in which forced or emergency landing becomes imperative. Emergency or forced landing becomes crucial when there is system failure which impacts the flight safety and UAV is unable to fly back to the emergency landing runway. This failure could be due to data link loses, GPS failure, engine or flight surface failure. Forced landing needs to be performed on safe landing site which could be plane surface, open fields or grounds. First step to accomplish the successful forced landing safely is to search and select the safe landing site. This article presents the system design which assists the UAV in selection of safe landing site having no obstacles, buildings and trees. The proposed system design uses computer vision and machine learning techniques in order to classify feasible and non-feasible landing sites. The proposed algorithms in this article also incorporate the scenarios having low lighting conditions due to clouds. The system has been designed and simulated in MATLAB and promising results have been achieved with very less processing time and computational power.
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