Imagine trying to find a face in a crowd. If we know what the face looks like we could search for it at every possible location — this is the essence of template matching. To make it work we need to describe how similar each area we are checking is to the reference face image […]
Search Results for: image similarity
For a camera moving through the environment we frequently wish to track particular world points from one frame to the next. We’ll do a quick introduction to the very large field of feature detection and matching using Harris corner features.
Let’s look at how light rays reflected from an object can form an image. We use the simple geometry of a pinhole camera to describe how points in a three-dimensional scene are projected on to a two-dimensional image plane.
When we look at an image we discern objects, and these tend to be groups of similar pixels surrounded by a distinctive edge. We look at intensity profiles in images and use spatial operators with kernels such as the Sobel kernel to find the intensity gradients in an image, and from these find edges in […]
One very powerful trick used by humans is binocular vision. The images from each eye are quite similar, but there is a small horizontal shift, a disparity, between them and that shift is a function of the object distance.
A color camera has many similarities to the human eye. Instead of three types of cone cells a uniform silicon sensor uses a pattern of three color filters known as a Bayer filter.
We use MATLAB and some Toolbox functions to create a robot controller that moves a camera so the image matches what we want it to look like. We call this an image-based visual servoing system.
A critical part of a visual servoing system is establishing correspondence between points in the scene observed by the camera, and points in our desired image of the scene.
The relationship between world coordinates, image coordinates and camera spatial velocity is elegantly summed up by a single matrix equation that involves what we call the image Jacobian.
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, […]