An in Depth Review Paper on Numerous Image Mosaicing Approaches and Techniques

Image mosaicing is one of the most important subjects of research in computer vision at current. Image mocaicing requires the integration of direct techniques and feature based techniques. Direct techniques are found to be very useful for mosaicing large overlapping regions, small translations and rotations while feature based techniques are useful for small overlapping regions. Feature based image mosaicing is a combination of corner detection, corner matching, motion parameters estimation and image stitching.Furthermore, image mosaicing is considered the process of obtaining a wider field-of-view of a scene from a sequence of partial views, which has been an attractive research area because of its wide range of applications, including motion detection, resolution enhancement, monitoring global land usage, and medical imaging. Numerous algorithms for image mosaicing have been proposed over the last two decades.In this paper the authors present a review on different approaches for image mosaicing and the literature over the past few years in the field of image masaicing methodologies. The authors take an overview on the various methods for image mosaicing.This review paper also provides an in depth survey of the existing image mosaicing algorithms by classifying them into several groups. For each group, the fundamental concepts are first clearly explained. Finally this paper also reviews and discusses the strength and weaknesses of all the mosaicing groups

Development Of A High Performance Mosaicing And Super-Resolution Algorithm

In this dissertation, a high-performance mosaicing and super-resolution algorithm is described. The scale invariant feature transform (SIFT)-based mosaicing algorithm builds an initial mosaic which is iteratively updated by the robust super resolution algorithm to achieve the final high-resolution mosaic. Two different types of datasets are used for testing: high altitude balloon data and unmanned aerial vehicle data. To evaluate our algorithm, five performance metrics are employed: mean square error, peak signal to noise ratio, singular value decomposition, slope of reciprocal singular value curve, and cumulative probability of blur detection. Extensive testing shows that the proposed algorithm is effective in improving the captured aerial data and the performance metrics are accurate in quantifying the evaluation of the algorithm

Swarming Drone Sensors

In the last decades, the presence of UAVs has increased widely in the military world, as they are able of monitoring conflict areas without endangering human lives. Many of these UAVs have the disadvantage of being quite big and expensive; therefore, the trend now is to use lots of smaller and cheaper drones which make it possible for the system to continue working even if a couple of drones get lost or are unable to contribute. In this project it has been designed a robust system that using a fixed number of drones with single cameras on them delivers a good resolution picture comparing with the ones that are obtained from expensive systems. Given a certain mission area, first a task allocation algorithm assigns some tasks or positions in the mission area to each UAV, in a way that the information collected by the images is maximized. After that, an image mosaicing algorithm will process those images in order to return the final mosaic. The whole thesis has been developed in a simulation environment in Matlab. The results show that the proposed algorithms guarantee that the complete mission area will be covered by the UAVs in the shortest possible time. In addition, the obtained final mosaic represents perfectly the considered mission area when an adequate overlapping area is considered. Finally, the system has been proven to be resilient if a UAV is unable to contribute for some reason

Swarming Drone Sensors

In the last decades, the presence of UAVs has increased widely in the military world, as they are able of monitoring conflict areas without endangering human lives. Many of these UAVs have the disadvantage of being quite big and expensive; therefore, the trend now is to use lots of smaller and cheaper drones which make it possible for the system to continue working even if a couple of drones get lost or are unable to contribute. In this project it has been designed a robust system that using a fixed number of drones with single cameras on them delivers a good resolution picture comparing with the ones that are obtained from expensive systems. Given a certain mission area, first a task allocation algorithm assigns some tasks or positions in the mission area to each UAV, in a way that the information collected by the images is maximized. After that, an image mosaicing algorithm will process those images in order to return the final mosaic. The whole thesis has been developed in a simulation environment in Matlab. The results show that the proposed algorithms guarantee that the complete mission area will be covered by the UAVs in the shortest possible time. In addition, the obtained final mosaic represents perfectly the considered mission area when an adequate overlapping area is considered. Finally, the system has been proven to be resilient if a UAV is unable to contribute for some reason

Determination of Characteristic Transport Coefficients of Porous Media from Volumetric Images using the Diffuse Interface Method

Transport in engineered materials such as electrodes, membranes, filters, and natural materials such as rock, sand, soil can be modeled as transport in porous media. Direct Numerical Simulations (DNS) on volumetric images of porous media are commonly done using the Lattice Boltzmann Method (LBM), but this presents various challenges such as long the computational time required to reach steady-state, fixed grid coarseness, and limited availability of reliable LBM software, commercial or otherwise. Traditional finite element based methods require conformal meshes of porous domains that are able to accurately capture fluid/solid interfaces, but at the cost of significant computational complexity and user interaction in order to create the mesh. To address these challenges, this work presents the application of a diffuse-interface finite element method that approximates a phase-field from volumetric images of porous media without user interaction and enables the use of a simple structured grid/mesh for traditional finite element-based fluid mechanics methods. The presented diffuse interface method (DIM) is automated and non-iterative, enabling the direct calculation of three characteristic coefficients from input images: tortuosity, permeability, and inertial constant by simulating Fickian mass diffusion and single component incompressible Navier Stokes equation from low to high range of inlet velocity. Three different 2D test images with varying porosities are used to demonstrate the use of DIM. The method is compared to traditional FEM implementation using conformal meshes with respect to the agreement with the determination of the characteristic coefficients, numerical accuracy, and computational requirements (time). Different parameters affecting the accuracy of DIM were identified and ideal parameters were determined. At ideal parameters, the relative error in tortuosity less than 0.75%, the relative error in permeability less than 1%, and relative error in inertial constant less than 3% were achieved for all three images. Though, DIM was found to be slower than traditional FEM implementation calls for optimized solvers for fluid flow on structured meshes to speed up the DIM simulations. The developed method provides an automated approach for computing effective transport properties from volumetric images of porous media