5 – K-means Implementation

Here I’ve read in an image of a monarch butterfly. And I want to segment this image into a few pieces, just enough to separate the background green scenery and the orange and black butterfly. To perform K-means segmentation, I’m going to focus on one distinguishing feature, the color of each pixel. I’ll need to … Read more

4 – K-means Clustering

One commonly used image segmentation technique is k-means clustering. It’s a machine learning technique that separates an image into segments by clustering or grouping together data points that have similar traits. As an example, let’s look at this image of oranges in a bowl. If I asked k-means to break up this image into two … Read more

3 – Image Contours

Edge detection algorithms are often used to detect the boundaries of objects. But, after performing edge detection you’ll often be left with sets of edges that highlight not only object boundaries but also interesting features and lines. And to do image segmentation, you’ll want only complete closed boundaries that marked distinct areas and objects in … Read more

2 – Corner Detectors

When we built an edge detector, we looked at the difference in intensity between neighboring pixels, and an edge was detected if there was a big and abrupt change in intensity in any one direction- up or down, or left or right, or diagonal. Recall that the change in intensity in an image is also … Read more

1 – Types Of Features

Let’s look at this image of a mountain. This is Mt. Rainier in Washington state. Most features on this and any other image fall into one of three categories: edges, corners, and blobs. Edges we’re already very familiar with, they’re are just areas in an image where the intensity abruptly changes. Also known as areas … Read more