Texture Model Formulation. Next: Combined Model Formulation Up: Statistical Models of Shape Previous: Shape Model Formulation Subsections To form a complete model of appearance one must not only consider shape. To stress this point observe that shape is only well defined by inferring from knowledge of the pixel neighborhood. One must also consider the information constituted by pixels themselves. In the following a complete scheme for capturing pixel information, using image warping, and modeling pixel variation, using principal component analysis, is described. Definition 3: Texture is the pixel intensities across the object in question (if necessary after a suitable normalization). In the shape case, the actual data capture was straightforward because the landmarks in the shape vector constituted the data itself.
Here m denotes the number of pixel samples over the object surface. And pictorial in the planar case, k=2, in figure 5.1. For a survey on warping techniques refer to Glasbey & Mardia [33]. Into another . Xin Li of WVU. Reproducible Research in Computational Science “It doesn't matter how beautiful your theory is, it doesn't matter how smart you are.
If it doesn't agree with experiment, it's wrong” - Richard Feynman "As a method for finding things out, science lives by its disdain for authority and its reliance on experimentation. " - Chris Quigg Welcome to this site about reproducible research in computational science (including signal processing, computer vision, machine learning and neural computation). This site is intended to share the source codes of the latest advances in various technical fields to the best of my knowledge. Only through Reproducible Research (RR), can we live up to the standard that hard-core science has established since Bacon and Newton. Google Scholar is great but if most papers in computational sciences could be accessed along with their source codes (not just the citation number), the world for scientific researchers will be even better.
July 2009. 7.A Convolution Theorem Figure 1 shows a set of patterns, which illustrates the Fourier transform of the convolution of two images. The last three images can also be thought of as the convolution of the patterns (square, circle, Gaussian) with the two dots in the first image. Recall that the Fourier transform of a convolution is just the the product of the Fourier transforms of the images. Recalling the Fourier transform of a square and a circle, which are shown in the previous activity, Figure 1, indeed, illustrates that the Fourier transform of convolution of two images is just the product of their Fourier transform.
Figures 2, 3 and 4 show the variation in the Fourier transform when the sizes of the patterns are varied. It can be seen that the size of the pattern in the Fourier transform, most obvious with the brightest spot, generally decreases with increasing size of the image pattern. Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. 7.B Fingerprints: Ridge Enhancement Figure 6. CompVision. De-blurring revisited with Wiener filter using OpenCV. Image Filters Using CPU and GPU. Introduction In this article, we are going to investigate a simple common framework for single input, single output image filters. Filters can be implemented in plain C++ running on CPU, using nVidia CUDA running on GPU, using C++ AMP (requires VS 2012 RC) or a mix of these. We are going to capture a webcam input with OpenCV and process the captured frames using implemented image filters. Requirements Patched OpenCV 2.2 for Windows.
Background This article is not about introducing OpenCV, CUDA or C++ AMP. What is Image Processing? Image processing filter represents a mathematical operation applied to an image such as color negation, arbitrary rotation, blurring etc. What is OpenCV? OpenCV is an open-source computer vision library which amongotherthings helps us to capture webcam for this article.
What is CUDA? CUDA is a game-changer in parallel programming, it is a paradigm shifter, it is not multicore computing, it is many-core computing. What is C++ AMP? Using the code Using CUDA in Filters.