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However, none of these algorithms succeed to process more than a few tens of images when the amount of missing elements reaches 90% of the measurement matrix and cameras have large field of view (Martinec & Pajdla 2005). (Martinec & Pajdla 2005) proposes an algorithm has the following advantages: First, it provides an overall scene structure in a single step without requirements such as linear ordering of images in a sequence. Second, the solution is obtained as a global optimum of a reasonable cost function defined on an approximation to the original SFM (structure-from-motion) problem.
(Martin & Doerry 2005) modify the existing Polar Format MATLAB implementation utilizing the Chirp Z-Transform that improves performance and memory usage achieving near real-time results for smaller apertures. They also add two new image formation options that perform a more traditional interpolation style image formation. These options allow the continued exploration of possible interpolation methods for image formation and some preliminary results comparing image quality are given. In this paper we review the principles used in reconstruction of three dimension images from two dimension images from a digital camera.
The reconstruction algorithm of Synthetic Aperture Radar (SAR) is presented and implemented in MATLAB in the following sections. This section overviews the process of representing a three-dimensional world into a two-dimensional representation. The projection process loses one dimension. This process is usually central projection in which a ray from a point in space is drawn from a three dimension world point through a fixed point in space, the centre of projection. This ray will intersect the chosen plane as the image plane.
The intersection of the ray with the image plane represents the image of the point. This projection is similar to the camera in which a ray of light from a point in the world passes through the lens of a camera
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