Engineering Tripos Part IIB, 4F10: Deep Learning & Structured Data, 2018-19
Leader
Lecturer
Prof M Gales, Dr J M Hernandez-Lobato
Timing and Structure
Michaelmas term. 14 lectures + 2 examples classes. Assessment: 100% exam
Prerequisites
Part IIA Modules 3F1, 3F3 and 3F8 advisable
Aims
The aims of the course are to:
- This module aims to teach the basic concepts of deep learning and forms of structure that can be used for generative and discriminative models. In ad-dition the use of models for classifying structured data, such as speech and language, will be discussed
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the basic principles of pattern classification and deep learning;
- Understand generative and discriminative models for structured data;
- Understand the application of deep-learning to structured data;
- Be able to apply pattern processing techniques to practical applications.
Content
Introduction (1L)
Links with 3F8 and 4F13. General machine learning, examples of struc- tured data, DNA, vision, speech and language processing.
Decision Boundaries and Probability of Error (1L)
Definition of a decision boundary and forms that result from Gaussian class-conditional probability density functions. Calculation of probability of Error.
Graphical Models and Conditional Indpendence (1L)
Graphical models and Bayesian networks. Simple inference examples.
Latent Variable and Sequence Models (2L)
Gaussian mixture models and factor analysis; hidden Markov models and expectation maximisation;
Deep Learning (2L)
Generative and discriminative deep models. Forms of network and acti- vation functions. Convolutional neural networks, mixture-density neural networks. Optimisation approaches (first/second order methods, adaptive learning rates) and initialisation.
Deep Learning for Sequences (2L)
Recurrent neural networks, and long-short-term memory models. Variants of RNN including bidirectional RNNs. Use in generative and discrimina- tive models.
Deep Generative Models (1L)
Auto-encoders and variational extension.
Ensemble Methods (1L)
Ensemble methods: random forests, bagging, boosting and model combination.
Support Vector Machines (2L)
Maximum margin classifiers, handling non-separable data, training SVMs, non-linear SVMs, kernel functions. Links with other kernel methods Gaus- sian Processes, Relevance Vector Machines. Multi-class SVMs and struc- tured SVMs.
Kernels over Structured Data (1L)
Tree kernels, graph kernels, Fisher kernels. Relationship to RNNs.
Booklists
Please see the Booklist for Group F Courses for references for this module.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
Last modified: 24/09/2018 12:29
Engineering Tripos Part IIB, 4F10: Deep Learning & Structured Data, 2022-23
Module leader
Lecturer
Prof M Gales and Dr Miguel Hernandez-Lobato
Timing and Structure
Michaelmas term. 14 lectures + 2 examples classes. Assessment: 100% exam
Prerequisites
Part IIA Modules 3F1, 3F3 and 3F8 advisable
Aims
The aims of the course are to:
- This module aims to teach the basic concepts of deep learning and forms of structure that can be used for generative and discriminative models. In addition, the use of models for classifying structured data, such as speech and language, will be discussed
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the basic principles of pattern classification and deep learning;
- Understand generative and discriminative models for structured data;
- Understand the application of deep-learning to structured data;
- Be able to apply pattern processing techniques to practical applications.
Content
Introduction (1L)
Links with 3F8 and 4F13. General machine learning, examples of structured data, DNA, vision, speech and language processing.
Decision Boundaries and Probability of Error (1L)
Definition of a decision boundary and forms that result from Gaussian class-conditional probability density functions. Calculation of probability of Error.
Graphical Models and Conditional Indpendence (1L)
Graphical models and Bayesian networks. Simple inference examples.
Latent Variable and Sequence Models (3L)
Gaussian mixture models and factor analysis; hidden Markov models and expectation maximisation.
Deep Learning (2L)
Generative and discriminative deep models. Forms of network and activation functions. Convolutional neural networks, mixture-density neural networks. Optimisation approaches (first/second order methods, adaptive learning rates) and initialisation.
Deep Learning for Sequences (2L)
Recurrent neural networks, and long-short-term memory models. Variants of RNN including bidirectional RNNs. Use in generative and discriminative models.
Ensemble Methods (1L)
Ensemble methods: random forests, bagging, boosting and model combination.
Support Vector Machines (2L)
Maximum margin classifiers, handling non-separable data, training SVMs, non-linear SVMs, kernel functions. Multi-class SVMs.
Kernels over Structured Data (1L)
String kernels, graph kernels and Fisher kernels.
Booklists
Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
Last modified: 24/05/2022 12:53
Engineering Tripos Part IIB, 4F10: Deep Learning & Structured Data, 2021-22
Module leader
Lecturer
Timing and Structure
Michaelmas term. 14 lectures + 2 examples classes. Assessment: 100% exam
Prerequisites
Part IIA Modules 3F1, 3F3 and 3F8 advisable
Aims
The aims of the course are to:
- This module aims to teach the basic concepts of deep learning and forms of structure that can be used for generative and discriminative models. In addition, the use of models for classifying structured data, such as speech and language, will be discussed
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the basic principles of pattern classification and deep learning;
- Understand generative and discriminative models for structured data;
- Understand the application of deep-learning to structured data;
- Be able to apply pattern processing techniques to practical applications.
Content
Introduction (1L)
Links with 3F8 and 4F13. General machine learning, examples of structured data, DNA, vision, speech and language processing.
Decision Boundaries and Probability of Error (1L)
Definition of a decision boundary and forms that result from Gaussian class-conditional probability density functions. Calculation of probability of Error.
Graphical Models and Conditional Indpendence (1L)
Graphical models and Bayesian networks. Simple inference examples.
Latent Variable and Sequence Models (3L)
Gaussian mixture models and factor analysis; hidden Markov models and expectation maximisation.
Deep Learning (2L)
Generative and discriminative deep models. Forms of network and activation functions. Convolutional neural networks, mixture-density neural networks. Optimisation approaches (first/second order methods, adaptive learning rates) and initialisation.
Deep Learning for Sequences (2L)
Recurrent neural networks, and long-short-term memory models. Variants of RNN including bidirectional RNNs. Use in generative and discriminative models.
Ensemble Methods (1L)
Ensemble methods: random forests, bagging, boosting and model combination.
Support Vector Machines (2L)
Maximum margin classifiers, handling non-separable data, training SVMs, non-linear SVMs, kernel functions. Multi-class SVMs.
Kernels over Structured Data (1L)
String kernels, graph kernels and Fisher kernels.
Booklists
Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
Last modified: 20/05/2021 07:49
Engineering Tripos Part IIB, 4F10: Deep Learning & Structured Data, 2020-21
Module leader
Lecturer
Prof M Gales & Dr J M Hernandez-Lobato
Timing and Structure
Michaelmas term. 14 lectures + 2 examples classes. Assessment: 100% exam
Prerequisites
Part IIA Modules 3F1, 3F3 and 3F8 advisable
Aims
The aims of the course are to:
- This module aims to teach the basic concepts of deep learning and forms of structure that can be used for generative and discriminative models. In addition, the use of models for classifying structured data, such as speech and language, will be discussed
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the basic principles of pattern classification and deep learning;
- Understand generative and discriminative models for structured data;
- Understand the application of deep-learning to structured data;
- Be able to apply pattern processing techniques to practical applications.
Content
Introduction (1L)
Links with 3F8 and 4F13. General machine learning, examples of structured data, DNA, vision, speech and language processing.
Decision Boundaries and Probability of Error (1L)
Definition of a decision boundary and forms that result from Gaussian class-conditional probability density functions. Calculation of probability of Error.
Graphical Models and Conditional Indpendence (1L)
Graphical models and Bayesian networks. Simple inference examples.
Latent Variable and Sequence Models (3L)
Gaussian mixture models and factor analysis; hidden Markov models and expectation maximisation.
Deep Learning (2L)
Generative and discriminative deep models. Forms of network and activation functions. Convolutional neural networks, mixture-density neural networks. Optimisation approaches (first/second order methods, adaptive learning rates) and initialisation.
Deep Learning for Sequences (2L)
Recurrent neural networks, and long-short-term memory models. Variants of RNN including bidirectional RNNs. Use in generative and discriminative models.
Ensemble Methods (1L)
Ensemble methods: random forests, bagging, boosting and model combination.
Support Vector Machines (2L)
Maximum margin classifiers, handling non-separable data, training SVMs, non-linear SVMs, kernel functions. Multi-class SVMs.
Kernels over Structured Data (1L)
String kernels, graph kernels and Fisher kernels.
Booklists
Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
Last modified: 01/09/2020 10:38
Engineering Tripos Part IIB, 4F10: Deep Learning & Structured Data, 2019-20
Leader
Lecturer
Dr J M Hernandez-Lobato, Prof PC Woodland, Dr I Budvytis
Timing and Structure
Michaelmas term. 14 lectures + 2 examples classes. Assessment: 100% exam
Prerequisites
Part IIA Modules 3F1, 3F3 and 3F8 advisable
Aims
The aims of the course are to:
- This module aims to teach the basic concepts of deep learning and forms of structure that can be used for generative and discriminative models. In addition, the use of models for classifying structured data, such as speech and language, will be discussed
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the basic principles of pattern classification and deep learning;
- Understand generative and discriminative models for structured data;
- Understand the application of deep-learning to structured data;
- Be able to apply pattern processing techniques to practical applications.
Content
Introduction (1L)
Links with 3F8 and 4F13. General machine learning, examples of structured data, DNA, vision, speech and language processing.
Decision Boundaries and Probability of Error (1L)
Definition of a decision boundary and forms that result from Gaussian class-conditional probability density functions. Calculation of probability of Error.
Graphical Models and Conditional Indpendence (1L)
Graphical models and Bayesian networks. Simple inference examples.
Latent Variable and Sequence Models (3L)
Gaussian mixture models and factor analysis; hidden Markov models and expectation maximisation.
Deep Learning (2L)
Generative and discriminative deep models. Forms of network and activation functions. Convolutional neural networks, mixture-density neural networks. Optimisation approaches (first/second order methods, adaptive learning rates) and initialisation.
Deep Learning for Sequences (2L)
Recurrent neural networks, and long-short-term memory models. Variants of RNN including bidirectional RNNs. Use in generative and discriminative models.
Ensemble Methods (1L)
Ensemble methods: random forests, bagging, boosting and model combination.
Support Vector Machines (2L)
Maximum margin classifiers, handling non-separable data, training SVMs, non-linear SVMs, kernel functions. Links with other kernel methods Gaussian Processes, Relevance Vector Machines. Multi-class SVMs and structured SVMs.
Kernels over Structured Data (1L)
Tree kernels, graph kernels, Fisher kernels. Relationship to RNNs.
Booklists
Please see the Booklist for Group F Courses for references for this module.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
Last modified: 03/09/2019 10:16
Engineering Tripos Part IIB, 4F10: Deep Learning & Structured Data, 2025-26
Module leader
Lecturer
Timing and Structure
Michaelmas term. 14 lectures + 2 examples classes. Assessment: 100% exam
Prerequisites
Part IIA Modules 3F1, 3F3 and 3F8 advisable
Aims
The aims of the course are to:
- This module aims to teach the basic concepts of deep learning and forms of structure that can be used for generative and discriminative models. In addition, the use of models for classifying structured data, such as speech and language, will be discussed
Objectives
As specific objectives, by the end of the course students should be able to:
- Understand the basic principles of pattern classification and deep learning;
- Understand generative and discriminative models for structured data;
- Understand the application of deep-learning to structured data;
- Be able to apply pattern processing techniques to practical applications.
Content
Introduction (1L)
Links with 3F8 and 4F13. General machine learning, examples of structured data, DNA, vision, speech and language processing.
Decision Boundaries and Probability of Error (1L)
Definition of a decision boundary and forms that result from Gaussian class-conditional probability density functions. Calculation of probability of Error.
Graphical Models and Conditional Indpendence (1L)
Graphical models and Bayesian networks. Simple inference examples.
Latent Variable and Sequence Models (2L)
Gaussian mixture models and factor analysis; hidden Markov models and expectation maximisation.
Deep Learning (3L)
Generative and discriminative deep models. Forms of network and activation functions. Convolutional neural networks, density neural networks. Optimisation approaches (first/second order methods, adaptive learning rates) and initialisation.
Deep Learning for Sequences (2L)
Recurrent neural networks and variants of RNN including bidirectional RNNs. Transformer architectures including encoder-only, encode-decoder and decoder only models. Aligning large-language models (LLMs) and Chain of Thought (CoT).
Ensemble Methods and Model Distillation (1L)
Deep ensembles, model combination and model distillation approaches.
Support Vector Machines (2L)
Maximum margin classifiers, handling non-separable data, training SVMs, non-linear SVMs, kernel functions. Multi-class SVMs.
Kernels over Structured Data (1L)
String kernels, graph kernels and Fisher kernels.
Booklists
Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
Last modified: 05/06/2025 11:55
Engineering Tripos Part IIB, 4F8: Image Processing & Imaging Coding, 2023-24
Module Leader
Lecturers
Timing and Structure
Lent term. 16 lectures (including examples classes). Assessment: 100% exam
Prerequisites
3F1 assumed; 3F3, 3F7 useful
Aims
The aims of the course are to:
- introduce the key tools for performing sophisticated processing of images by digital hardware
Objectives
As specific objectives, by the end of the course students should be able to:
- understand the main elements of 2-dimensional linear system theory.
- design linear spatial filters for a variety of applications (smoothing etc)
- understand techniques for the restoration and enhancement of degraded images.
- show familiarity with the main characteristics of the human visual system with particular reference to subjective criteria for image data compression.
- understand techniques for image coding using transform methods including the Discrete Cosine Transform (as used in the JPEG coding standard) and overlapped transforms.
- understand methods for coding transform coefficients to provide maximum data compression.
Content
Sophisticated processing of images by digital hardware is now fairly common, and ranges from special effects in video games to satellite image enhancement. Three of the main application areas are video data compression, image enhancement, and scene understanding. This module introduces the key tools for performing these tasks, and shows how these tools can be applied. The module will be split into two courses of 8 lectures each: Image Processing, and Image Coding. Lectures are supported by computer demonstrations. There will be one examples sheet for each of the two 8-lecture sections.
Image Processing (8L, Dr J Lasenby)
This course covers the following topics, relevant to most aspects of image processing:
- Two-dimensional linear system theory, as applied to discretely sampled systems:
- The continuous 2D Fourier transform and its properties
- Digitisation, sampling, aliasing and quantisation
- The discrete 2D Fourier transform (DFT)
- 2D Digital Filters and Filter Design
- Zero phase filters
- Ideal 2D filters: rectangular and bandpass
- Filter design: the window method
- Image Deconvolution
- Deconvolution of noiseless images -- the inverse filter
- The Wiener filter (conventional and Bayesian derivations)
- Maximum Entropy deconvolution
- Image Enhancement
- Contrast enhancement
- Histogram equalisation
- Median filtering
Image Coding (8L, Prof N Kingsbury)
This course concentrates on image and video data compression techniques, and covers the following topics:
- Characteristics of the human visual system which are important for data compression:
- Spatial and temporal frequency sensitivities
- Distortion masking phenomena
- Luminance and colour (chrominance) processing
- 2D block transforms and wavelet transforms:
- Discrete cosine transforms
- Bi-orthogonal and orthonormal wavelet transforms
- Energy compaction properties of transforms for typical images
- Optimal quantisation techniques of coding transform coefficients for maximum data compression
- Huffman coding
- Run-length coding
- JPEG 2-dimensional run-size coding
- Video coding techniques
- Motion analysis
- Motion vector coding
- MPEG coding standards
Booklists
Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
D1
Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.
D4
Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
E4
Understanding of and ability to apply a systems approach to engineering problems.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 30/05/2023 15:31
Engineering Tripos Part IIB, 4F8: Image Processing & Imaging Coding, 2020-21
Module Leader
Lecturers
Timing and Structure
Lent term. 16 lectures (including examples classes). Assessment: 100% exam
Prerequisites
3F1 assumed; 3F3, 3F7 useful
Aims
The aims of the course are to:
- introduce the key tools for performing sophisticated processing of images by digital hardware
Objectives
As specific objectives, by the end of the course students should be able to:
- understand the main elements of 2-dimensional linear system theory.
- design linear spatial filters for a variety of applications (smoothing etc)
- understand techniques for the restoration and enhancement of degraded images.
- show familiarity with the main characteristics of the human visual system with particular reference to subjective criteria for image data compression.
- understand techniques for image coding using transform methods including the Discrete Cosine Transform (as used in the JPEG coding standard) and overlapped transforms.
- understand methods for coding transform coefficients to provide maximum data compression.
Content
Sophisticated processing of images by digital hardware is now fairly common, and ranges from special effects in video games to satellite image enhancement. Three of the main application areas are video data compression, image enhancement, and scene understanding. This module introduces the key tools for performing these tasks, and shows how these tools can be applied. The module will be split into two courses of 8 lectures each: Image Processing, and Image Coding. Lectures are supported by computer demonstrations. There will be one examples sheet for each of the two 8-lecture sections.
Image Processing (8L, Dr J Lasenby)
This course covers the following topics, relevant to most aspects of image processing:
- Two-dimensional linear system theory, as applied to discretely sampled systems:
- The continuous 2D Fourier transform and its properties
- Digitisation, sampling, aliasing and quantisation
- The discrete 2D Fourier transform (DFT)
- 2D Digital Filters and Filter Design
- Zero phase filters
- Ideal 2D filters: rectangular and bandpass
- Filter design: the window method
- Image Deconvolution
- Deconvolution of noiseless images -- the inverse filter
- The Wiener filter (conventional and Bayesian derivations)
- Maximum Entropy deconvolution
- Image Enhancement
- Contrast enhancement
- Histogram equalisation
- Median filtering
Image Coding (8L, Prof N Kingsbury)
This course concentrates on image and video data compression techniques, and covers the following topics:
- Characteristics of the human visual system which are important for data compression:
- Spatial and temporal frequency sensitivities
- Distortion masking phenomena
- Luminance and colour (chrominance) processing
- 2D block transforms and wavelet transforms:
- Discrete cosine transforms
- Bi-orthogonal and orthonormal wavelet transforms
- Energy compaction properties of transforms for typical images
- Optimal quantisation techniques of coding transform coefficients for maximum data compression
- Huffman coding
- Run-length coding
- JPEG 2-dimensional run-size coding
- Video coding techniques
- Motion analysis
- Motion vector coding
- MPEG coding standards
Booklists
Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
D1
Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.
D4
Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
E4
Understanding of and ability to apply a systems approach to engineering problems.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 01/09/2020 10:38
Engineering Tripos Part IIB, 4F8: Image Processing & Imaging Coding, 2019-20
Module Leader
Lecturers
Timing and Structure
Lent term. 16 lectures (including examples classes). Assessment: 100% exam
Prerequisites
3F1 assumed; 3F3, 3F7 useful
Aims
The aims of the course are to:
- introduce the key tools for performing sophisticated processing of images by digital hardware
Objectives
As specific objectives, by the end of the course students should be able to:
- understand the main elements of 2-dimensional linear system theory.
- design linear spatial filters for a variety of applications (smoothing etc)
- understand techniques for the restoration and enhancement of degraded images.
- show familiarity with the main characteristics of the human visual system with particular reference to subjective criteria for image data compression.
- understand techniques for image coding using transform methods including the Discrete Cosine Transform (as used in the JPEG coding standard) and overlapped transforms.
- understand methods for coding transform coefficients to provide maximum data compression.
Content
Sophisticated processing of images by digital hardware is now fairly common, and ranges from special effects in video games to satellite image enhancement. Three of the main application areas are video data compression, image enhancement, and scene understanding. This module introduces the key tools for performing these tasks, and shows how these tools can be applied. The module will be split into two courses of 8 lectures each: Image Processing, and Image Coding. Lectures are supported by computer demonstrations. There will be one examples sheet for each of the two 8-lecture sections.
Image Processing (8L, Dr J Lasenby)
This course covers the following topics, relevant to most aspects of image processing:
- Two-dimensional linear system theory, as applied to discretely sampled systems:
- The continuous 2D Fourier transform and its properties
- Digitisation, sampling, aliasing and quantisation
- The discrete 2D Fourier transform (DFT)
- 2D Digital Filters and Filter Design
- Zero phase filters
- Ideal 2D filters: rectangular and bandpass
- Filter design: the window method
- Image Deconvolution
- Deconvolution of noiseless images -- the inverse filter
- The Wiener filter (conventional and Bayesian derivations)
- Maximum Entropy deconvolution
- Image Enhancement
- Contrast enhancement
- Histogram equalisation
- Median filtering
Image Coding (8L, Prof N Kingsbury)
This course concentrates on image and video data compression techniques, and covers the following topics:
- Characteristics of the human visual system which are important for data compression:
- Spatial and temporal frequency sensitivities
- Distortion masking phenomena
- Luminance and colour (chrominance) processing
- 2D block transforms and wavelet transforms:
- Discrete cosine transforms
- Bi-orthogonal and orthonormal wavelet transforms
- Energy compaction properties of transforms for typical images
- Optimal quantisation techniques of coding transform coefficients for maximum data compression
- Huffman coding
- Run-length coding
- JPEG 2-dimensional run-size coding
- Video coding techniques
- Motion analysis
- Motion vector coding
- MPEG coding standards
Booklists
Please see the Booklist for Group F Courses for references for this module.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
D1
Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.
D4
Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
E4
Understanding of and ability to apply a systems approach to engineering problems.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 28/05/2019 15:11
Engineering Tripos Part IIB, 4F8: Image Processing & Imaging Coding, 2022-23
Module Leader
Lecturers
Timing and Structure
Lent term. 16 lectures (including examples classes). Assessment: 100% exam
Prerequisites
3F1 assumed; 3F3, 3F7 useful
Aims
The aims of the course are to:
- introduce the key tools for performing sophisticated processing of images by digital hardware
Objectives
As specific objectives, by the end of the course students should be able to:
- understand the main elements of 2-dimensional linear system theory.
- design linear spatial filters for a variety of applications (smoothing etc)
- understand techniques for the restoration and enhancement of degraded images.
- show familiarity with the main characteristics of the human visual system with particular reference to subjective criteria for image data compression.
- understand techniques for image coding using transform methods including the Discrete Cosine Transform (as used in the JPEG coding standard) and overlapped transforms.
- understand methods for coding transform coefficients to provide maximum data compression.
Content
Sophisticated processing of images by digital hardware is now fairly common, and ranges from special effects in video games to satellite image enhancement. Three of the main application areas are video data compression, image enhancement, and scene understanding. This module introduces the key tools for performing these tasks, and shows how these tools can be applied. The module will be split into two courses of 8 lectures each: Image Processing, and Image Coding. Lectures are supported by computer demonstrations. There will be one examples sheet for each of the two 8-lecture sections.
Image Processing (8L, Dr J Lasenby)
This course covers the following topics, relevant to most aspects of image processing:
- Two-dimensional linear system theory, as applied to discretely sampled systems:
- The continuous 2D Fourier transform and its properties
- Digitisation, sampling, aliasing and quantisation
- The discrete 2D Fourier transform (DFT)
- 2D Digital Filters and Filter Design
- Zero phase filters
- Ideal 2D filters: rectangular and bandpass
- Filter design: the window method
- Image Deconvolution
- Deconvolution of noiseless images -- the inverse filter
- The Wiener filter (conventional and Bayesian derivations)
- Maximum Entropy deconvolution
- Image Enhancement
- Contrast enhancement
- Histogram equalisation
- Median filtering
Image Coding (8L, Prof N Kingsbury)
This course concentrates on image and video data compression techniques, and covers the following topics:
- Characteristics of the human visual system which are important for data compression:
- Spatial and temporal frequency sensitivities
- Distortion masking phenomena
- Luminance and colour (chrominance) processing
- 2D block transforms and wavelet transforms:
- Discrete cosine transforms
- Bi-orthogonal and orthonormal wavelet transforms
- Energy compaction properties of transforms for typical images
- Optimal quantisation techniques of coding transform coefficients for maximum data compression
- Huffman coding
- Run-length coding
- JPEG 2-dimensional run-size coding
- Video coding techniques
- Motion analysis
- Motion vector coding
- MPEG coding standards
Booklists
Please refer to the Booklist for Part IIB Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
IA2
Demonstrate creative and innovative ability in the synthesis of solutions and in formulating designs.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
D1
Wide knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations.
D4
Ability to generate an innovative design for products, systems, components or processes to fulfil new needs.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
E4
Understanding of and ability to apply a systems approach to engineering problems.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 24/05/2022 13:12
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