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The foundational theory of graph cuts was first applied in computer vision in the seminal paper by Greig, Porteous and Seheult [3] of Durham University.Allan Seheult and Bruce Porteous were members of Durham's lauded statistics group of the time, led by Julian Besag and Peter Green, with the optimisation expert Margaret Greig notable as the first ever female member of staff of the Durham ...
In mathematical optimization, the ordered subset expectation maximization (OSEM) method is an iterative method that is used in computed tomography.. In applications in medical imaging, the OSEM method is used for positron emission tomography (PET), for single-photon emission computed tomography (SPECT), and for X-ray computed tomography (X-ray CT) scans.
The Barzilai-Borwein method [1] is an iterative gradient descent method for unconstrained optimization using either of two step sizes derived from the linear trend of the most recent two iterates.
The regularization parameter plays a critical role in the denoising process. When =, there is no smoothing and the result is the same as minimizing the sum of squares.As , however, the total variation term plays an increasingly strong role, which forces the result to have smaller total variation, at the expense of being less like the input (noisy) signal.
Then the resolution-independent version is rendered as a raster image at the desired resolution. This technique is used by Adobe Illustrator Live Trace, Inkscape , and several recent papers. [ 6 ] Scalable Vector Graphics are well suited to simple geometric images, while photographs do not fare well with vectorization due to their complexity.
Semi-global matching (SGM) is a computer vision algorithm for the estimation of a dense disparity map from a rectified stereo image pair, introduced in 2005 by Heiko Hirschmüller while working at the German Aerospace Center. [1]
In the detection phase of the Viola–Jones object detection framework, a window of the target size is moved over the input image, and for each subsection of the image the Haar-like feature is calculated. This difference is then compared to a learned threshold that separates non-objects from objects.
A major drawback to application of the algorithm is an inherent reduction in overall image contrast produced by the operation. [1] When utilized for image enhancement, the difference of Gaussians algorithm is typically applied when the size ratio of kernel (2) to kernel (1) is 4:1 or 5:1.