Dr Simon Coupland

Job: Principal Lecturer

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence (CCI)

Address: De Montfort University, The Gateway, Leicester, LE1 9BH

T: +44 (0)116 207 8419

E: simonc@dmu.ac.uk

W: http://www.dmu.ac.uk/cci

 

Personal profile

Simon Coupland is a researcher working in the area of type-2 fuzzy logic.  Simon has worked on the underpinning mathematics of the field making a number of important contributions.  He also works on practical problems in this area including control, decision making and computing with words.

Research group affiliations

Centre for Computational Intelligence

Publications and outputs 

  • A Neural Network for Interpolating Light-Sources
    A Neural Network for Interpolating Light-Sources Colreavy-Donnelly, S.; Kuhn, Stefan; Caraffini, Fabio; O'Connor, S.; Anastassi, Zacharias; Coupland, Simon This study combines two novel deterministic methods with a Convolutional Neural Network to develop a machine learning method that is aware of directionality of light in images. The first method detects shadows in terrestrial images by using a sliding-window algorithm that extracts specific hue and value features in an image. The second method interpolates light-sources by utilising a line-algorithm, which detects the direction of light sources in the image. Both of these methods are single-image solutions and employ deterministic methods to calculate the values from the image alone, without the need for illumination-models. They extract real-time geometry from the light source in an image, rather than mapping an illumination-model onto the image, which are the only models used today. Finally, those outputs are used to train a Convolutional Neural Network. This displays greater accuracy than previous methods for shadow detection and can predict light source-direction and thus orientation accurately, which is a considerable innovation for an unsupervised CNN. It is significantly faster than the deterministic methods. We also present a reference dataset for the problem of shadow and light direction detection. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Real-Time 3D Head Pose Tracking Through 2.5D Constrained Local Models with Local Neural Fields
    Real-Time 3D Head Pose Tracking Through 2.5D Constrained Local Models with Local Neural Fields Ackland, Stephen; Chiclana, Francisco; Istance, Howell; Coupland, Simon Tracking the head in a video stream is a common thread seen within computer vision literature, supplying the research community with a large number of challenging and interesting problems. Head pose estimation from monocular cameras is often considered an extended application after the face tracking task has already been performed. This often involves passing the resultant 2D data through a simpler algorithm that best fits the data to a static 3D model to determine the 3D pose estimate. This work describes the 2.5D Constrained Local Model, combining a deformable 3D shape point model with 2D texture information to provide direct estimation of the pose parameters, avoiding the need for additional optimization strategies. It achieves this through an analytical derivation of a Jacobian matrix describing how changes in the parameters of the model create changes in the shape within the image through a full-perspective camera model. In addition, the model has very low computational complexity and can run in real-time on modern mobile devices such as tablets and laptops. The Point Distribution Model of the face is built in a unique way, so as to minimize the effect of changes in facial expressions on the estimated head pose and hence make the solution more robust. Finally, the texture information is trained via Local Neural Fields (LNFs) a deep learning approach that utilizes small discriminative patches to exploit spatial relationships between the pixels and provide strong peaks at the optimal locations. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Type-2 Fuzzy Elliptic Membership Functions for Modeling Uncertainty
    Type-2 Fuzzy Elliptic Membership Functions for Modeling Uncertainty Kayacan, E; Sarabakha, A; Coupland, Simon; John, Robert, 1955-; Ahmadieh, M.; Khanesar, M. A. Whereas type-1 and type-2 membership functions (MFs) are the core of any fuzzy logic system, there are no performance criteria available to evaluate the goodness or correctness of the fuzzy MFs. In this paper, we make extensive analysis in terms of the capability of type-2 elliptic fuzzy MFs in modeling uncertainty. Having decoupled parameters for its support and width, elliptic MFs are unique amongst existing type-2 fuzzy MFs. In this investigation, the uncertainty distribution along the elliptic MF support is studied, and a detailed analysis is given to compare and contrast its performance with existing type-2 fuzzy MFs. Furthermore, fuzzy arithmetic operations are also investigated, and our finding is that the elliptic MF has similar features to the Gaussian and triangular MFs in addition and multiplication operations. Moreover, we have tested the prediction capability of elliptic MFs using interval type-2 fuzzy logic systems on oil price prediction problem for a data set from 2nd Jan 1985 till 25th April 2016. Throughout the simulation studies, an extreme learning machine is used to train the interval type-2 fuzzy logic system. The prediction results show that, in addition to their various advantages mentioned above, elliptic MFs have comparable prediction results when compared to Gaussian and triangular MFs. Finally, in order to test the performance of fuzzy logic controller with elliptic interval type-2 MFs, extensive real-time experiments are conducted for the 3D trajectory tracking problem of a quadrotor. We believe that the results of this study will open the doors to elliptic MFs’ wider use of real-world identification and control applications as the proposed MF is easy to interpret in addition to its unique features.
  • Interval Type–2 Defuzzification Using Uncertainty Weights
    Interval Type–2 Defuzzification Using Uncertainty Weights Coupland, Simon; Runkler, Thomas; John, Robert, 1955-; Chen, Chao One of the most popular interval type–2 defuzzification methods is the Karnik–Mendel (KM) algorithm. Nie and Tan (NT) have proposed an approximation of the KM method that converts the interval type–2 membership functions to a single type–1 membership function by averaging the upper and lower memberships, and then applies a type–1 centroid defuzzification. In this paper we propose a modification of the NT algorithm which takes into account the uncertainty of the (interval type–2) memberships. We call this method the uncertainty weight (UW) method. Extensive numerical experiments motivated by typical fuzzy controller scenarios compare the KM, NT, and UW methods. The experiments show that (i) in many cases NT can be considered a good approximation of KM with much lower computational complexity, but not for highly unbalanced uncertainties, and (ii) UW yields more reasonable results than KM and NT if more certain decision alternatives should obtain a larger weight than more uncertain alternatives.
  • On Nie-Tan Operator and Type-reduction of Interval Type-2 Fuzzy Sets
    On Nie-Tan Operator and Type-reduction of Interval Type-2 Fuzzy Sets Jiawei, Li; John, Robert, 1955-; Coupland, Simon; Graham Kendall Type-reduction of type-2 fuzzy sets is considered to be a defuzzification bottleneck because of the computational complexity involved in the process of type-reduction. In this research, we prove that the closed-form Nie-Tan operator, which outputs the average of the upper and lower bounds of the footprint of uncertainty, is actually an accurate method for defuzzifing interval type-2 fuzzy sets. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Interval type-2 fuzzy decision making
    Interval type-2 fuzzy decision making Coupland, Simon; Runkler, Thomas; John, Robert, 1955- This paper concerns itself with decision making under uncertainty and the consideration of risk. Type-1 fuzzy logic by its (essentially) crisp nature is limited in modelling decision making as there is no uncertainty in the membership function. We are interested in the role that interval type-2 fuzzy sets might play in enhancing decision making. Previous work by Bellman and Zadeh considered decision making to be based on goals and constraints. They deployed type-1 fuzzy sets. This paper extends this notion to interval type-2 fuzzy sets and presents a new approach to using interval type-2 fuzzy sets in a decision making situation taking into account the risk associated with the decision making. The explicit consideration of risk levels increases the solution space of the decision process and thus enables better decisions. We explain the new approach and provide two examples to show how this new approach works. Full text on Nottingham eprints - http://eprints.nottingham.ac.uk/36609/
  • Adaptive-mutation compact genetic algorithm for dynamic environments
    Adaptive-mutation compact genetic algorithm for dynamic environments Gongora, Mario Augusto; Coupland, Simon; Passow, Benjamin N.; Uzor, C. J. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • INNATE: Intelligent Non-invasive Nocturnal epilepsy Assistive TEchnology
    INNATE: Intelligent Non-invasive Nocturnal epilepsy Assistive TEchnology Malekmohamadi, Hossein; Shell, Jethro; Coupland, Simon Epilepsy is a neurological disease that affects the brain and is characterised by repeated seizures. Generalised, focal and unknown are three major types of seizures. Each type has several subgroups. For this reason, seizure detection and classification are expensive and erroneous. Other factors can also affect the detection. For example, patients can have a combination of different seizures or start with one type and finish with another. Nocturnal epilepsy can be prominent in many sufferers of this disease. This displays seizures that occur during the sleep cycle. The nature of such seizures makes the gathering of data and the subsequent detection and classification complex and costly. The current standard for seizure detection is the invasive use of electroencephalogram (EEG) monitoring. Both medical and research communities have expressed a large interest in the detection and classification of seizures automatically and non-invasively. This project proposes the use of 3D computer vision and pattern recognition techniques to detect seizures non-invasively.
  • Type-2 Fuzzy Alpha-Cuts
    Type-2 Fuzzy Alpha-Cuts Coupland, Simon; Hamrawi, Hussam; John, Robert, 1955- Type-2 fuzzy logic systems make use of type-2 fuzzy sets. To be able to deliver useful type-2 fuzzy logic applications we need to be able to perform meaningful operations on these sets. These operations should also be practically tractable. However, type-2 fuzzy sets suffer the shortcoming of being complex by definition. Indeed, the third dimension, which is the source of extra parameters, is in itself the origin of extra computational cost. The quest for a representation that allow practical systems to be implemented is the motivation for our work. In this paper we define the alpha-cut decomposition theorem for type-2 fuzzy sets which is a new representation analogous to the alpha-cut representation of type-1 fuzzy sets and the extension principle. We show that this new decomposition theorem forms a methodology for extending mathematical concepts from crisp sets to type-2 fuzzy sets directly. In the process of developing this theory we also define a generalisation that allows us to extend operations from interval type-2 fuzzy sets or interval valued fuzzy sets to type-2 fuzzy sets. These results will allow for the more applications of type-2 fuzzy sets by expiating the parallelism that the research here affords.
  • Picture Perfect: Computational methods for matching historical photographic records across different collections
    Picture Perfect: Computational methods for matching historical photographic records across different collections Brown, Stephen C.; Coupland, Simon; Croft, David While there is growing consensus among museum professionals and users about the importance of data integration, cross-collection searching remains a significant challenge. This paper describes a novel approach to interrogating different online collections to identify potential matches between them, using fuzzy logic based data mining algorithms. This project was in collaboration with the Centre for Computational Intelligence.

Click here to view a full listing of Simon Coupland's publications and outputs. 

Research interests/expertise

Understanding the performance capabilities of type-2 fuzzy logic.

Improving the computational performance of type-2 fuzzy logic systems.

Assessing other extensions to type-1 fuzzy sets and systems such as triangular type-2 fuzzy sets and non-stationary fuzzy systems.

The application of type-2 fuzzy logic to real-world problems.

Areas of teaching

MSc Computing/IT/ISM Introduction to computer systems.

Occasional lectures to MSc CIR on fuzzy logic, neural networks and recent advances in research.   

Qualifications

PhD in Computer Science

BSc (Hons) Computing

Courses taught

IMAT3404 Mobile Robots

Honours and awards

Joint Winner IEEE CIS Pre-college Education subcommittee Video Competition, 2012.

IEEE Transactions on Fuzzy Systems Outstanding Paper Award, 2009.

British Computer Society Machine Intelligence Award Winner, 2008.

Membership of professional associations and societies

IEEE Member

Externally funded research grants information

FuzzyPhoto, AHRC, 01/11/12 – 31/10/14, CI, Stephen Brown.

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