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Applying Median filter to image using MATLAB
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The median filter is a nonlinear digital filtering technique, often used to remove noise . Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image). Median filtering is very widely used in digital image processing
weki123 0000-00-00
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Improved adaptive Gausian mixture model for background subtraction
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Background Subtraction is a technique in which background and foreground are segmented so that we can perform our required algorithms (such as face detection, gender classification etc). The basic approach for background subtraction is to store the background image as the reference image, in which there is no movement and then in every other frame subtract the reference image to extract the alien objects in the scene. The alien objects are captured as foreground and these are used for further applications.
Since the background does not always remain constant (mainly because of light changes or continuous movement of leaves) therefore we need to develop an approach in which we update the background continuously so that minute changes in the background are made part of the background. Stauffer
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Improving the selection of feature points for tracking
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The problem considered here is how to select the feature points (in practice, small image patches are used) in an image from an image sequence, such that they can be tracked adequately further through the sequence. Usually, the tracking is performed by some sort of local search method looking for a similar patch in the next image in the sequence. Therefore, it would be useful if we could estimate "the size of the convergence region" for each image patch. There is a smaller chance of error when calculating the displacement for an image patch with a large convergence region than for an image patch with a small convergence region. Consequently, the size of the convergence region can be used as a proper goodness measure for a feature point. For the standard Kanade-Lucas-Tomasi (KLT) tracking method, we propose a simple and fast way to approximate the convergence region for an image pa
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Recursive unsupervised learning of finite mixture models
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There are two open problems when finite mixture densities are used to model multivariate data: the selection of the number of components and the initialization. In this paper, we propose an online (recursive) algorithm that estimates the parameters of the mixture and that simultaneously selects the number of components. The new algorithm starts with a large number of randomly initialized components. A prior is used as a bias for maximally structured models. A stochastic approximation recursive learning algorithm is proposed to search for the maximum a posteriori (MAP) solution and to discard the irrelevant components
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Data Clustering with Normalized Cuts
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The Normalized Cuts clustering algorithm views the data set
as a graph, where nodes represent data points and edges are weighted according
to the similarity, or “affinity”, between data points. This is the starting point
of many other spectral clustering algorithms . The affinity matrix used in
these algorithms is reminiscent of the Gram matrix that appears in kernel-based
algorithms, such as the Support Vector Machine.
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