Search Results for: bounding box

For a binary image that contains multiple blobs we must first transform it using connectivity analysis or region labeling. Then we can describe each of the blobs in the scene we first need to transform the image using connectivity analysis. Each of the blobs can then be described in terms of its area, centroid position, […]

If we look at a binary image we can easily see distinct regions, that is, sets of pixels the same color as their neighbours. We call these blobs and they’re an important way of achieving an object rather than pixel view of the scene. We can describe these blobs by their area, centroid position, bounding […]

Let’s recap the important points from the topics we have covered about image features, blobs, connectivity analysis, and blob parameters such as centroid position, area, bounding box, moments, equivalent ellipse, and perimeter.

When it comes to describing a blob we can do more than just area, centroid position and bounding box. By looking at second order moments we can compute an ellipse that has the same moments of inertia as the blob, and we can use its aspect ratio and orientation to describe the shape and orientation […]

The simplest smooth trajectory is a polynomial with boundary conditions on position, velocity and acceleration.

We learn how to describe the orientation of an object by a 3×3 rotation matrix which has some special properties.

Most computers today have a built-in camera. Let’s look at how we can grab images directly from such a camera and put them in the MATLAB workspace.

Taking an average of pixels in a box leads to artefacts such as ringing which we can remedy by taking a weighted average of all the pixels in the box surrounding the input pixel. The set of weights is referred to as a kernel. A common kernel used for image smoothing is the Gaussian kernel.

Another non-linear operation on the pixels in the box around the input pixel is to test whether they match a reference shape. This is a very powerful and useful approach to cleaning up noisy binary images known as mathematical morphology and objects in the image are treated according to their compatibility with a structuring element. […]

So far we have taken a linear combination of pixels in the box around the input pixel, but non-linear operations like sorting and ranking the pixel values also prove to be very useful. We look at the median filter which is much better at removing salt and pepper noise from image than simple smoothing.