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Let’s recap the important points from the topics we have covered in advanced image processing.

Many scenes, particularly of man-made environments, have very dominant lines due to the edges of objects. The Hough transform is a common technique for finding dominant lines, and we ill examine how it works and apply it to a real image.

We use MATLAB and some Toolbox functions to find tomatoes on a bush. We convert the color image to chromaticity coordinates, select the pixels that belong to the tomatoes and the perform blob analysis to find the location of the tomatoes.

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

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 can bring together what we’ve learnt about histograms and monadic operations to implement a technique called histogram normalization which is great at highlighting fine detail in an image.

The orientation of a body in 3D can be described by three angles, examples of which are Euler angles and roll-pitch-yaw angles. Note that in the MATLAB example at 8:24 note that recent versions of the Robotics Toolbox (9.11, 10.x) give a different result: >> rpy2r(0.1,0.2,0.3)ans = 0.9363 -0.2751 0.2184 0.2896 0.9564 -0.0370 -0.1987 0.0978 […]

We learn how to describe the orientation of an object by a 2×2 rotation matrix which has some special properties. Try your hand at some online MATLAB problems. You’ll need to watch all the 2D “Spatial Maths” lessons to complete the problem set.

Is it acceptable that self-driving cars will cause accidents if they kill fewer people than human drivers? When an accident occurs is it a moral or a legal issue?

We learn to compute a trajectory that involves simultaneous smooth motion of many robot joints.