
Scale Invariant Corner Features (SIFT)
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When matching points between scenes with large different viewpoints we need to account for varying image size and rotation. SIFT features are a powerful way to achieve this.
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When matching points between scenes with large different viewpoints we need to account for varying image size and rotation. SIFT features are a powerful way to achieve this.
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We use MATLAB and some Toolbox functions to find corresponding points between two images using SURF features.
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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.
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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 […]
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The orientation of a body in 3D can also be described by a single rotation about a particular axis in space.
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The orientation of a body in 3D can also be described by a unit-Quaternion, an unusual but very useful mathematical object. In the MATLAB example starting at 3:48 I use the Quaternion class. For Toolbox version 10 (2017) please use UnitQuaternion instead.
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We resume our analysis of the 6-link robot Jacobian and focus on the rotational velocity part.
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We learn how to describe the orientation of an object by a 3×3 rotation matrix which has some special properties.
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The orientation of a body in 3D can also be described by two vectors, often called the approach and orientation vectors.
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We learn how to describe the 3D pose of an object by a 4×4 homogeneous transformation matrix which has a special structure.