Category

## Computer Vision

Category

Edge detection is the process of identify the presence and location of edges by sharp discontinuities of image. Edge detection plays an important role in image processing and helps in solving many complex problems. One useful and easy to implement algorithm is the Prewitt filter. As compared to Sobel, the Prewitt masks are simpler to implement but are very sensitive to noise. Mathematically, the operator uses two 3×3 kernels which are convolved with the original…

The following powerpoint presents a small introduction to sparse representation and dictionary learning as well as some awesome examples found on the internet. PowerPoint Presentation [embeddoc url=”http://www.devcoons.com/wp-content/uploads/2017/07/presentation_1.pptx” viewer=”google”]

Image separation of mixed and overlapped images is a frequent problem in computer vision (image processing). The following Matlab source code is a demonstration of image separation using FastICA algorithm based on kurtosis.

The aim of this article is to detect the edges with a given direction in an image. To that end create a function [ E ] = oriented_edges( I, thr, a, da ) that takes as input a double grayscale image Ι, a threshold value thr, a direction a, and an angle da. The output of the function is a binary image Ε where the pixels that meet the following requirements should have the value 1:…

PCA is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences. Below are the steps of the algorithm:

Step 1 – Initialize the dataset, 6 vectors of 32 sample data

Step 2 – Subtract the mean from each of the data dimensions. The mean subtracted is the average across each dimension. [math]Y= X – (O * Mean(X)) [/math], where [math]O…