Dr. Alasdair D. F. Clarke
I am now working at Informatics, University of Edinburgh. My old email still works for now, and I’ll put a link to my new homepage when I get round to making it!
The Texture Lab,
School of Mathematical and Computer Science,
Heriot-Watt University, Edinburgh, EH14 4AS
adfc1 at hw dot ac dot uk
+44 (0)131 451 4166
My main research interests involve developing computational models for human visual perception. For my PhD I investigated human search strategies and modelled the results using an LNL-based model. Further information can be found here. During my Post-Doc I have investigated the ability of human observers to detect information in the phase spectrum; illumination perception; and symmetry perception in patterns.
Perception of the Frequency and Phase Spectra
The Fast Fourier Transform and the associated frequency and phase spectra are frequently used in image processing and computational models of human vision. Both spectra contain important information regarding the appearance of an image, and a large body of work has investigated the effects of manipulating these
spectra on the recognition or classification of image content. Here, we use a novel means of investigating sensitivity to amplitude and phase spectra properties, by using synthetic images of textured surfaces that are broad-band in the frequency domain, and by testing the ability of observers to detect degradations of their spectral content. We directly compare the effects of display time and retinal eccentricity on detection of these two manipulations, by using stimuli matched for difficulty of detection.
How do people perceive tiled patterns? Do human observers discriminate between the 17 different wallpaper groups? What effect does scale have on their judgements? Can we model human judgements using image processing algorithms?
PhD in Computer Science, Heriot-Watt University. June 2006 – June 2010: with the Texture Lab, School of Mathematics and Computer Science
M.Sc in Transport Planning and Engineering, Napier University, September 2004 – September 2005
M.Math in Pure Mathematics, University of Warwick. September 2000 – June 2004.
Journal and Conference Papers
A. D. F. Clarke, F Halley, A. J. Newell, L. D. Griffin & M. J. Chantler. Perceptual Similarity: A Texture Challenge. Proceedings of the British Machine Vision Conference, 2011.
A. D. F. Clarke, F. Halley, P. R. Green & M. J. Chantler (2011). Similar Symmetries: The role of wallpaper groups in perceptual texture similarity. Symmetry: Special issue on Symmetry Processing in Perception and Art, 3(2), 246-264.
A. D. F. Clarke, P. R. Green & M. J. Chantler (under review). The effects of display time and eccentricity on the detection of amplitude and phase degradations in textured stimuli. .
K. Emrith, M. J. Chantler, P. R. Green & A. D. F. Clarke (2010). Measuring perceived differences in surface texture due to changes in higher order statistics. Journal of the Optical Society of America A, 5(9), 1232-1244.
A. D. F. Clarke, P. R. Green & M. J. Chantler (2009). Modelling visual search on a rough surface. Journal of Vision, 9(4):11.
A. D. F. Clarke, P. R. Green, M. J. Chantler & K. Emrith (2008). Visual search for a target against a 1/fβ continuous textured background. Vision Research, 8(21), 2193-203.
A. D. F. Clarke , P. R. Green & M. J. Chantler (2011). Similar symmetries and the effect of scale i-Perception 2(3) 196. Scottish Vision Group, 25-27th March, Isle of Skye.
A. D. F. Clarke, P. R. Green & M. J. Chantler (2010). Detecting changes to the amplitude and phase spectra of textured stimuli: effects of display time and retinal eccentricity. Applied Vision Association, Christmas Meeting December 2010, Paris, France.
A. D. F. Clarke, P. R. Green & M. J. Chantler (2009). Stochastic Search on a Homogeneous Surface Texture. Applied Vision Association, Easter Meeting March 2009, Birmingham, UK.
A. D. F. Clarke, P. R. Green, K. Emrith & M. J. Chantler (2008). Modelling visual search for a target against a 1/fβ continuous textured background. Perception 37, European Conference on Visual Perception Abstract Supplement, page 3. August 24th-28th, 2008. Utrecht, the Netherlands.
A. D. F. Clarke, P. R. Green, K. Emrith & M. J. Chantler (2007). Visual search for a target against a 1/fβ continuous textured background. Scottish Perception Meeting, December 6th 2007, Stirling, UK.
A. D. F. Clarke (2010). Modelling Visual Search for Surface Defects, Ph.D. Thesis, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK.
Chapter 1: Introduction
Automatic surface defect detection is one of the main applications of computer vision and many different approaches and methods have been put forward. However, the ability of the human vision system to detect surface defects has not been studied in a rigorous way and little effort has been made to investigate how well computer vision algorithms can mimic human behaviour. Hence the aim of this thesis is to bring together relevant work on visual search, saliency, perception and texture discrimination for the purpose of analysing modelling human defect detection.
Chapter 2: Lit Review
This chapter contains two literature reviews. Section 2.1 concerns computer vision and reviews some of the different approaches that have been used to tackle the problem of automated surface defect detection. The related problem of texture discrimination is also discussed. The second half of this chapter, Section 2.2, contains a general overview of the processes behind human perception and introduces the field
of visual search.
Chapter 3: Texture Synthesis
Chapter 4: Visual Search for a Defect on a Homogeneous Surface
In this chapter I will explore how human observers perform in a search task involving a target on a homogeneous surface texture. I will investigate how surface and target properties affect our ability to find a defect in a forced choice target absent/present task using the two different surface textures described in the previous chapter. For the 1/f-noise surface textures the effect of varying surface roughness, along with the depth and orientation of the target, will be explored, while for the near-regular surfaces I will vary texton density and the degree of regularity. Altogether, four experiments will be carried out. The use of an eye-tracker will allow for search strategies to be investigated and a computational saliency model [Itti and Koch, 2000,Walther and Koch, 2006] will be run on the experimental stimuli and compared to the results from the psychophysical experiment.
This chapter starts with a discussion of the concept of visual saliency, computed by bottom-up visual processes (Section 4.1). This review is centred around Itti and Koch’s  saliency model and the extent to which it can explain visual search paths. This is followed by a methods section in which the procedures for the experiments in this, and Chapter 6, are given (Section 4.2). The core of this chapter contains a series of four experiments designed to investigate how well human observers can carry out a visual search task on the textural stimuli discussed in the preceding chapter (Section 4.3). Finally, results from the human observers are compared to the performance of the saliency model in Section 4.4.
Chapter 5: Models of Visual Search
In the previous chapter I compared Itti and Koch’s visual saliency algorithm to human performance in a series of visual search experiments involving a defect on an otherwise homogeneous textured surface. While the model proved a good match for the human data when a circular indent was used, it failed to mimic human behaviour when an elongated target was used. In this short chapter I will give a comprehensive review of previous work on modelling visual search before designing my own model in Chapter 6.
Chapter 6: An LNL-Based Search Model
In Chapter 4 I investigated how human performance in a defect detection task varies with surface and target properties such as regularity and orientation. Human performance was also compared to a bottom-up visual saliency algorithm [Itti and Koch, 2000, Walther and Koch, 2006]. The results showed that the model only partially tted the human data: in particular there is a discrepancy between the performance of the model and human observers when searching for an elongated defect (Experiment 3).
Chapter 5 contained a comprehensive review of how the problem of modelling visual search has been tackled in the past and in this chapter I will attempt to construct a search model which can simulate human behaviour in an unsupervised surface defect detection task. The model is based on an LNL-framework (see Section 2.1.2) and will be compared to human performance in a series of experiments, using a target always present design. This will remove the need for considering speed/accuracy trade-offs. Furthermore, it avoids the problem of dening a decision rule for the model for target absent trials. Instead, the model is assumed to nd the defect when it xates on it and only one measure, the number of saccades needed to find the target, needs to be used in order to compare performance between the search model and the human observers.
Chapter 7: Stochastic Search Strategies
In the previous chapter a LNL-based search model was shown to provide a good prediction of the difficulty of finding a defect in a rough surface. The model was tested over a wide range of perceived surface roughnesses and task difficulties and made a similar number of saccades to human observers in all cases. However, the model did a poor job of accounting for human scan-paths and search strategies and only predicted human fixation locations at chance levels. This suggests that signal-to-noise ratio in the activation map generated by the LNL-based model is a good model for human performance, but the local maxima in the activation map do not provide a good predication of fixation locations.
In this chapter I will explore search strategies and how much of a role visual memory has in determining search performance. The first experiment (Section 7.3) will investigate memory using a moving target paradigm [Horowitz andWolfe, 1998]. This is followed by a comparison between human performance and a stochastic search simulation. Unlike the previous model which was concerned with feature extraction, the stochastic search simulation only attempts to model search strategy and saccade choice. The model is outlined in Section 7.4 and uses the results of a signal detection experiment (Section 7.5) for the target detection model. The model is compared with human observers in Section 7.6.
Chapter 8: Conclusions
The motivation behind this thesis was to conduct a rigorous investigation into perceptual defect detection. As discussed in Chapter 2, previous work on defect detection algorithms has neglected comparing human and computer performance. Similarly, the problem of finding an anomaly on a homogeneous surface has received very little attention in the field of visual search. The main contribution of this thesis is to bring together relevant work on visual search, saliency, perception and texture discrimination for the purpose of modelling human defect detection.