A video is just a series of image frames shown in succession. Thus, processing a video is just processing all the image frames one by one. In this activity, we use image processing of the image frames to get the acceleration of a tennis ball rolling over a slide.
Dark images are not at all dark. In fact, the details are captured by the camera but our eyes just do not see it. In this activity we show how we can manipulate the histogram of an image so that our eyes can see the details in the dark.
In my previous blog, I have shown how morphological operations in images works. I have also shown how we can apply it on image segmentation (differentiate cancer cells from normal cells). In this chapter, we try to read and play simple music sheets using image segmentation.
Morphological operation is done on an image in order to change its shape and structure. Its concept is mainly based on set theory and topology. In this activity, we try to familiarize some basic operations and use them to measure size of “cancer cells”.
In simple terms, image segmentation is the process of taking a small part of a whole image. Look at the image an apple below with red and green parts. One may use an imaging software such as Paint to separate the red part from the whole image by using the crop tool.
In the previous blog, I have partly shown some applications of Fourier transform such as edge detection, template matching, etc. Here we show another application which is image enhancement.
The Fourier Transform is a method of converting a signal of dimension X to a signal of dimension 1/X. This method gives way to analyzing signals in terms and their frequencies and provides an easy mathematical manipulation of functions.
Here we discuss the basic implementation of Fourier Transform in Scilab and its applications to images. Continue reading “Fourier Transform and its applications”