A sampling approach to ultra-fast object location
Davies, E.R.
Real-Time Imaging, 7, no. 4, 339–355 (2001)
This paper presents new work to
determine lowest bounds on the computation needed to locate objects in digital
images. While it was originally applied to the rapid location of cereal grains
for real-time inspection, the concepts are now being found useful as a rigorous
starting point for understanding general visual search, and have an interesting
relationship with the saccades of the human visual system. During 2007 this
generalisation has been published in an Electronics Letter (43(9), 508), and
slightly more fully in VIE 2007.
Design of efficient line segment detectors for cereal grain inspection
Davies, E.R., Bateman, M., Mason, D.R., Chambers, J. and Ridgway, C.
Pattern Recogn. Lett., 24, nos. 1–3, 421–436 (2003)
This paper arose from a grant from the Home-Grown Cereals
Authority to carry out research in conjunction with DEFRA. It presents the
design of sensitive, robust, real-time algorithms for locating insects amongst
cereal grains. The algorithms take the form of efficient bar detectors, and have
been shown to be more effective than detectors based on edge detection. They
generalise to use as streak detectors, and significant interest has been shown
in their use for totally different applications, such as locating veins in human
retinal images. Thus the methodology is highly generic, applying well outside
the area of original motivation.
An analysis of the geometric distortions produced by
median and related image processing filters
Davies, E.R.
invited review in Advances in Imaging and Electron Physics, 126, 93–193 (2003)
This paper encapsulates and significantly extends Roy's work in understanding the exact
extent of the geometric distortions produced by median and related filters
(whose aim is largely to eliminate noise from digital images). While a continuum
approximation gave useful results, later discrete structural analysis eventually
led to essentially exact agreement between theory and experiment, so that the
subject could be regarded as satisfactorily closed, at least for grey-scale
images. This work includes theoretical models and experimental tests of the
shifts produced by the whole family of rank-order filters, and as such has
strong relevance and importance for mathematical morphology.
Mode filters and their effectiveness for processing colour images
Charles, D. and Davies, E.R.
Imaging Science, 52, no. 1, 3–25 (2004)
This paper investigates
the properties of mode filters in grey and colour images, and extends the work
of the previous paper, (a) to the colour domain and (b) to provide detailed explanations of
the performance of mode filters. Somewhat surprisingly, it also shows
astonishing performance when applied to colour images with up to 70% impulse
noise, removing the noise almost completely and amounting to a breakthrough for
the low computation colour mode filter that had been devised in this work.
Hitherto, mode filters seemed to be mainly useful for image enhancement, but
their use for gross noise suppression is entirely new.
Stable bi-level and multi-level thresholding of images using a new global transformation
Davies, E.R.
IET Computer Vision, 2, no. 2, Special Issue on Visual Information Engineering, Valestin, S. (ed.), 60–74 (2008)
This paper describes a new transformation for finding global valleys in 1D distributions, and is particularly useful
for thresholding grey-scale images. It estimates the global significance of all the valleys that are located, and can
therefore cope with multi-mode distributions. One of the main advantages of the resulting global valley method (GVM)
is that it permits partially hidden minima to be reliably located without complicated analysis. Overall, the GVM is
demonstrated to have high sensitivity for the detection of subsidiary minima – including those arising from practically
important image detail (such as defects or contaminants in an automated inspection scenario).
The application of machine vision to food and agriculture: a review
Davies, E.R.
Imaging Science, 57, no. 4, 197–217 (2009)
This paper reviews developments since 2000 in the application of machine vision to food and agriculture. While visible
light frequently provides enough useful information to make sound judgements, X-rays and hyperspectral imaging have
been increasingly valuable. There have also been valuable developments in the use of 3D methods, such as ‘double Hough
transforms’ for the accurate delineation of crop rows, so that ‘precision agriculture’ can be realized, and the use of
sets of visual calibration points so that robot vehicles can determine their exact locations and headings. Overall,
the steady development of useful vision algorithms has been well matched by the capability of today’s computers to
implement them at sufficiently high speeds to make them viable.
Improved line detection algorithm for locating road lane markings
Mastorakis, G. and Davies, E.R.
Electronics Letters, 47, no. 3, 183–184 (2011)
This paper describes an improved line detection algorithm for locating road lane markings. After scanning images to find
mid-points of potential lane markings, they are passed to the line detection algorithm, which selects the line with the
greatest support. After finding each line, the algorithm deletes not only the inliers within a ‘fit tolerance’ distance df
but also the innermost outliers lying within a larger ‘delete tolerance’ distance dd. This procedure is found to increase
robustness because elimination of remanent points near each line prevents them from confusing the algorithm when it
searches for further lines.
Object location using the Hough transform
Davies, E.R.
Chapter 18 in Handbook of Machine Vision, Batchelor, B.G. and Miller, J.W.V. (eds.), Springer Verlag, London,
773–800 (2012)
This chapter finds that inference methods often provide a significantly more robust means for locating complex objects,
the Hough transform being one of the most important examples of such methods. Graph matching also has this property and
demonstrates certain equivalences to the Hough transform technique. In fact, spatial matched filtering is intrinsically the
most sensitive method for locating objects, and this is shown to explain the success of both the Hough transform and graph
matching. Nevertheless, careful algorithm design is needed to reduce the underlying computational difficulties of this
approach.
Computer vision for automatic sorting in the food industry
Davies, E.R.
Chapter 6 in Computer Vision Technology in the Food and Beverage Industries, Sun, D.-W. (ed.), Woodhead Publishing Ltd.,
Cambridge, UK, 150–180 (2012)
This chapter describes how automatic sorting has been carried out increasingly widely in the food industry via the application
of computer vision. This has been ensured by continual developments in image processing and pattern recognition. At the same
time, advances in computer technology have permitted viable implementations to be achieved at lower cost. However, progress is
being held up by the need for tailored development, so future algorithms will have to be made much more trainable than at
present. In addition, recent methodologies such as hyperspectral imaging should become accepted in more application areas –
particularly the assessment of raw fruit and vegetable produce.
The dramatically changing face of computer vision
Davies, E.R.
Chapter 1 in Advanced Methods and Deep Learning in Computer Vision, E.R. Davies and Matthew Turk (eds.), Elsevier, Oxford, UK, 1–91 (2022)
This chapter aims to explain the concepts leading up to the recently evolved deep learning milieu, covering aspects such as image processing, feature detection, object recognition, segmentation and tracking: by providing a useful level of background theory, and an introduction to deep learning, the chapter aims to help prepare readers for the advanced chapters that are to follow.