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> Image Processing
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Image Processing
Note:
Whilst every effort is made to keep the syllabus and assessment records correct
for this course, the precise details must be checked with the lecturer(s).
| Code: | 3072
(Also taught as: GV12 Image Processing)
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| Year: | 3 |
| Prerequisites: | Successful completion of years 1 and 2 of the Computer Science, Mathematics and Computer Science or other Physical Science or Engineering programme with sufficient mathematical and programming content. |
| Term: | 1 |
| Taught By: | Gabriel Brostow (100%)
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| Aims: | The first half of this course introduces the digital image, describes the main characteristics of monochrome digital images, how they are represented and how they differ from graphics objects. It covers basic algorithms for image manipulation, characterisation, segmentation and feature extraction in direct space. The second half of the course proceeds to a more formal treatment of image filtering with some indication of the role and implications of Fourier space, and more advanced characterisation and feature detection techniques such as edge and corner detection, together with multiresolution methods, treatment of colour images and template matching techniques. The course allows students to explore a range of practical techniques, by developing their own simple processing functions either in a language such as Java and/or by using library facilities and tools such as MatLab or IDL.
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| Learning Outcomes: | To understand (ie be able to describe, analyse and reason about) how digital images are represented, manipulated, encoded and processed, with emphasis on algorithm design, implementation and performance evaluation.
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Content:
| Introduction to the digital image | Why digital images
The digital camera
Data types and 2d representation of digital images |
| Characteristics of grey-level digital images | Discrete sampling model
Quantisation
Noise processes
Image attributes |
| Segmentation | Thresholding and thresholding algorithms
Performance evaluation and ROC analysis
Connected components labelling
Region growing and region adjacency graph (RAG)
Split and merge algorithms |
| Image Transformations | Grey level transformations
Histogram equalization
Geometric transformations
Affine transformations
Polynomial warps |
| Morphological operation | Erode and dilate as max and min operators on binary images
Open, close, thinning and other transforms
Medial axis transform
Introduction to grey-level morphology |
| Feature Characterisation | Calculation of region properties
Moment features
Boundary coding
Fourier descriptors
Line descriptors from boundary coding and from moments |
| Image filtering | Linear and non-linear filtering operations
Image convolutions
Separable convolutions
Sub-sampling and interpolation as convolution operations |
| Edge detection | Alternative approaches
Edge enhancement by differentiation
Effect of noise, edge detection and Canny implementation
Edge detector performance evaluation |
| Corner detection | Image structure tensor
Relationship to image auto-correlation
Characterisation and Harris corner detector
Sub-pixel accuracy and performance evaluation |
| Colour images | Representations of colour in digital images
Colour metrics
Pixel-wise (point) operations
Colour invariants and Finlayson colour constancy algorithm |
| Template matching | Similarity and dissimilarity matching metrics
L2 metric and relationship to cross-correlation
Image search and multi-resolution algorithms
2D object detection, recognition, location |
Method of Instruction:
Lecture presentations with associated class coursework and laboratory sessions. There are 4 pieces of coursework, all weighted equally.
Assessment:
The course has the following assessment components:
- Written Examination (2.5 hours, 80%)
- Coursework Section (4 pieces, 20%)
To pass this course, students must:
- Obtain an overall pass mark of 40% for all sections combined
The examination rubric is:
Choice of 3 questions from six, at least one from each of two sections. All questions carry equal marks.Resources:
N.Efford, Digital Image Processing, Addison Wesley 2000, ISBN 0-201-59623-7
M Sonka, V Hlavac and R Boyle, Image Processing, Analysis and Machine Vision, PWS 1999, ISBN 0-534-95393-X
W K Pratt, Digital Image Processing, John Wiley and Sons, 1991, ISBN 0-471-85766-1
R Jain, R Kasturi and B G Schunck, Machine Vision, McGraw-Hill, 1995, ISBN 0-07-113407-7
Copy of lecture notes/overheads, Coursework assignments, Guidance notes for courseworks
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