Visual servoing is concerned with the motion of points in the world. How can we reliably detect such points using computer vision techniques.
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Light entering our eyes stimulates the photoreceptor cells in the retina of our eye: color sensitive cone cells that we use in normal lighting conditions and monochromatic rod cells we use in low light. The density of these cells varies across the retina, it is high in the fovea, low in the peripheral vision region […]
We live in a three-dimensional world but it’s taken humans a long time to learn how to realistically depict the illusion of depth on a flat surface.
Given two images of a scene taken from slightly different viewpoints, a stereo image pair, it’s possible to determine the disparity for every pixel using template matching. The disparity image is one where the value of each pixel is inversely related to the distance between that point in the scene and the camera.
We use MATLAB and some Toolbox functions to compute an homography and also apply it.
The linear algebra approach we’ve discussed is very well suited to MATLAB implementation. Let’s look at some toolbox functions that can simulate what cameras do. If you are using a more recent version of MVTB, ie. MVTB 4.x then please change>> cam.project(PW ‘Tcam’, transl(0.1, 0, 0)) to >> cam.project(PW ‘pose’, transl(0.1, 0, 0)).
We can describe the relationship between a 3D world point and a 2D image plane point, both expressed in homogeneous coordinates, using a linear transformation – a 3×4 matrix. Then we can extend this to account for an image plane which is a regular grid of discrete pixels.
Let’s recap the basics of homogeneous coordinates to represent points on a plane.
Light field cameras are now commercially available and capture much more information about the rays of light reflected from the scene. This enables us to perform functions like changing the focus of an image after it has been captured.
The pinhole or lensed camera is very similar to our eye, but there are lots of other ways to build a camera.