Finding Contours can be useful in creating masks for images and to segment and extract features from an image. Its used by Meteorologists to understand weather maps and for analysis and forecasts. The following post describes how contours could be identified in an image.
Implementation would be based on EmguCV the popular .NET ported version of OpenCV. Even though OpenCV is built in C++, EmguCV provides a wrapper which manages to invoke the same OpenCV libraries.By use of the EmguCV wrapper it provides all the necessary functions as well as data types of OpenCV in a .NET compliant library making it easier to implement.
Almost all images and photographs we come across are colored images. But for simplicity of algorithms most image processing algorithms are based on gray images (single channel). Even though colors play an important role in giving humans a true feel for images when it comes to details gray images and color images preserve the same amount of luminance and variation terms of details. Therefore even though the color information is lost, by converting an image to grayscale the details are still preserved.
Image processing is one of those areas I find a lot of interest. Editing, making enhancements to photos and merging multiple images into a single image can be one of the most creative past times for designers. However developers too could play around with pixels as designers do, and this in fact is known as image processing. It deals with manipulating pixels in an image using code.
Image processing branches out into many research areas such as medical, astronomy, chemistry. Mostly enhancing images for better clarity, extracting out hidden details in an image, enhancing segments of an image are typical uses of Image processing.
This post deals with using HTML5 in the context of image processing. With the improvement of modern browsers now it is possible to perform complex image processing algorithms simply with the context of a web browser.
When it comes to Image processing grayscale images are used to reduce the complexity of processing 3 separate R,G,B channels. Since gray images preserve the same luminosity of the color images, gray image processing algorithms can easily be ported to color images.