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Stars1
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LanguageJupyter Notebook
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Wavelet_Transform
A discrete wavelet transform (DWT) is a transform that decomposes a given signal into a number of sets, where each set is a time series of coefficients describing the time evolution of the signal in the corresponding frequency band. Various techniques were proposed like Subband coding, pyramidal coding. Discrete wavelet transforms can be used for image processing. As the resolution of the image increases, it requires a lot of disk space. DWT is used to reduce the size of an image without compromising on quality and hence resolution increases Single Level Discrete Wavelet Transform and Inverse Transform : [ cA , cD ] = dwt( x , wname ) , returns the single-level discrete wavelet transform (DWT) of the vector x using the wavelet specified by wname . The wavelet must be recognized by wavemngr . dwt returns the approximation coefficients vector cA and detail coefficients vector cD of the DWT.Jupyter Notebook
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Edge_Detection_Image
What are edges We can also say that sudden changes of discontinuities in an image are called as edges. Significant transitions in an image are called as edges. Types of edges Generally edges are of three types: Horizontal edges Vertical Edges Diagonal Edges Why detect edges Most of the shape information of an image is enclosed in edges. So first we detect these edges in an image and by using these filters and then by enhancing those areas of image which contains edges, sharpness of the image will increase and image will become clearer. Here are some of the masks for edge detection that we will discuss in the upcoming tutorials. Prewitt Operator Sobel Operator Robinson Compass Masks Krisch Compass Masks Laplacian Operator. Above mentioned all the filters are Linear filters or smoothing filters. Prewitt Operator Prewitt operator is used for detecting edges horizontally and vertically. Sobel Operator The sobel operator is very similar to Prewitt operator. It is also a derivate mask and is used for edge detection. It also calculates edges in both horizontal and vertical direction. Robinson Compass Masks This operator is also known as direction mask. In this operator we take one mask and rotate it in all the 8 compass major directions to calculate edges of each direction. Kirsch Compass Masks Kirsch Compass Mask is also a derivative mask which is used for finding edges. Kirsch mask is also used for calculating edges in all the directions. Laplacian Operator Laplacian Operator is also a derivative operator which is used to find edges in an image. Laplacian is a second order derivative mask. It can be further divided into positive laplacian and negative laplacian. All these masks find edges. Some find horizontally and vertically, some find in one direction only and some find in all the directions. The next concept that comes after this is sharpening which can be done once the edges are extracted from the imageJupyter Notebook
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Ask_Me
Objective-C
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Dice-Game
Fun working with Flutter LayoutsDart
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