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| 1.
| [Image processing]
Students understand how images are digitally represented and learn how to perform image processing, e.g. logic and arithmetic operations, convolution, and filtering. |
| 2.
| [Feature extraction]
Students are able to implement low-level edge and corner detection algorithms to discard redundant and preserve useful information. |
| 3.
| [Camera model and calibration]
Students are able to model a projective pinhole camera by applying techniques such as perspective projection, rigid body motion and homogeneous coordinates. Students are able to discover the intrinsic and extrinsic camera parameters using linear least squares on a set of equations. |
| 4.
| [Stereo vision]
Students are able to extract 3D information from images and recover world positions through triangulation. They understand fundamental concepts in multiple view geometry, such as the correspondence problem, the essential/fundamental matrix, and the epipolar geometry. |
Mapping from Course Learning Outcomes to Programme Learning Outcomes
| | PLO a | PLO b | PLO c | PLO d | PLO e | PLO f | PLO g | PLO h | PLO i | PLO j |
| CLO 1 | T,P | | T,P | | | | | | | |
| CLO 2 | T,P | | | | | | | | | T,P |
| CLO 3 | T,P | T,P | | | | | | | | |
| CLO 4 | T,P | | | T,P | | | | | | |
T - Teach, P - Practice
For BEng(CompSc) Programme Learning Outcomes, please refer to
here.
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|
Calendar Entry:
This course introduces the principles, mathematical models and applications of computer vision. Topics include: image processing techniques, feature extraction techniques, imaging models and camera calibration techniques, stereo vision, and motion analysis.
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Detailed Description:
| Image processing |
Mapped to CLOs
|
| Digital image representation, sampling, false contouring, connectivity, distance measures, logic and arithmetic operations, convolution, linear spatial filtering, smoothing and sharpening filters, color models | 1 |
| Feature extraction |
Mapped to CLOs
|
| Image interpretation, image structure, 1D and 2D edge detection, multi-scale edge detection, aperture problem, corner detection | 2 |
| Perspective projection and camera model |
Mapped to CLOs
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| Pinhole camera, vanishing points and lines, full camera model, rigid body motion, coordinate system, CCD imaging, homogeneous coordinates, projection matrix | 3 |
| Affine cameras |
Mapped to CLOs
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| Weak-perspective projection, planar affine imaging, invariants, cross-ratio | 3 |
| Camera calibration |
Mapped to CLOs
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| Singular value decomposition, linear least squares, nonlinear camera calibration, Gram-Schmidt process, QR decomposition, planar scene calibration, calibration from a line, calibration from vanishing points | 3 |
| Stereo Vision |
Mapped to CLOs
|
| Recovery of world position, triangulation, epipolar geometry, essential matrix, fundamental matrix, correspondence problem, dynamic programming, structure recovery, RANSAC, essential matrix decomposition, affine stereo | 4 |
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