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A video is basically made up of many images consecutively shown to a viewer who perceive the movements to be smooth, thus making it into a movie. Note that for humans, we can only perceive it to be smooth if there are more than 15 images (or referred to as frames) shown every second. A video is basically made up of many images consecutively shown to a viewer who percieve the movements to be smooth, thus making it into a movie. Note that for humans, we can only perceive it to be smooth if there are more than 15 images (or referred to as frames) shown every second. Each video has a frame rate for which it is number of successive images shown in a second.  Most cameras has around 30 frames per second (fps), that is 1/30 second for each of the image. From previous experiments, we usually apply only the processing on a single image.  For a video, we can include a different dimension as measurement, that is, time.  Therefore using video, we can extract the dynamics or ki...

Image segmentation is the process in which a region of interest (ROI) is "segmented" or separated from the whole image and can be used for further analysis.  This may sound similar to that of thresholding however we note that thresholding can only be applied to grayscale images, and not colored images.  It has multiple applications such as locating specific objects, removing unecessary parts, and finding boundaries in images [1].  This can also be used to label pixels that display similiar features or visual characteristics which can later be used for further analysis as well. Several features can be extracted from image segmentation: color, texture, and intensity [1].  We note that color has already been used to segment skin regions, hand recognition, and even cells in slides.  It has also been used to analyzed paintings and identify which regions were made in similar manner (texture) or similar paint (color). However, we consider that objects has 3D shadin...

From the previous two activities, several applications of morphological operations were demonstrated.  It was used to pre-process text, as well as play notes (music).  In this this activity, I will try to show using previously used methods (mainly morphological operations) to determine the best estimate of area of a simulated "normal cells". For preview, a "normal cell" has a specific size, while cancerous cells are usually larger, or smaller than the normal cells as shown in the figure below. Image processing is ideal for repetitive process and as such, we note that for us to be able to "see" the cells properly, they must be enlarged (or zoomed in).  this therefore presents the problem that a "whole slide" must be examined instead of just a part.  As such, we divide the large image into subimages where identifying "cancerous cell" will be repeated for each subimage, instead of applying to the whole image. This is much similar to ...