Images contain many pixels and the normal way to process them is with nested for loops that index each pixel in turn. This is slow and somewhat cumbersome to write. MATLAB has a facility called vectorization that allows us to perform complex matrix operations without any loops.
Search Results for: efficient computation
We learn a method for succinctly describing the structure of a serial-link manipulator in terms of its Denavit-Hartenberg parameters, a widely used notation in robotics.
For a redundant robot the inverse kinematics can be easily solved using a numerical approach.
Let’s recall the key techniques we’ve covered including monadic and dyadic image processing operations and efficient ways to write these in MATLAB using vectorization.
A more efficient trajectory has a trapezoidal velocity profile.
Let’s look at some recent research results that vividly show how information from many 2D images taken from many different locations can be combined to form a detailed 3D model of the world.
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 will compare and contrast the terms image processing, computer vision and robotic vision — they have much in common but there are some subtle but important distinctions. When it comes to interpreting an image we typically try to find and describe regions, lines and interest points.
Visual servoing is concerned with the motion of points in the world. How can we reliably detect such points using computer vision techniques.