We describe the development of a vision system to detect natural events in a low-resolution image stream. The work involves the assessment of algorithmic design decisions to maximise detection reliability. This assessment is carried out by comparing measures and estimates made by the system under development with measures obtained independently. We show that even when these independent measures are themselves noisy, their independence can serve to guide design decisions and allow performance estimates to be made. Although presented here for one particular system design, we believe that such an approach will be applicable to other situations when an image-based system is to be used in the analysis of natural scenes in situations where a precise ground truth is not available.

A novel colour image segmentation routine, based on clustering pixels in colour space using non-parametric density estimation, is described. Although the basic methodology is well known, several important improvements to the previous work in this area are introduced. The density is estimated at a series of knot points in the colour space, and clustering is performed by hill climbing on this density function. The hill climbing is constrained such that no step crosses an intermediate Voronoid cell, ensuring that all salient clusters are detected. Most importantly, the problem of scale selection has been addressed using a statistically motivated approach, by placing the knot points according to an estimate of the noise in the original images, taking full account of error propagation in the algorithm. The algorithm has been evaluated both on synthetic data and in the context of its application in a machine vision system, specifically the calibration of velocity estimates extracted from a novel infrared sensor used in a fall detector. The application of the technique to medical images and texture recognition is also discussed.

This paper discusses advances made to the 3D geometrical model matching system within the TINA machine vision environment over the last 10 years including the recent inclusion of a verification stage. We show how this final step closes the loop on the object location system allowing the theoretical location performance to be attained, eliminating key assumptions used in the existing forward pass algorithms. We explain how the use of a system approach has been crucial to the development of key components and summarise our findings.

Image subtraction is used in many areas of machine vision to identify small changes between equivalent pairs of images. Often only a small subset of the differences will be of interest. In motion analysis only those differences caused by motion are important, and differences due to other sources only serve to complicate interpretation. Simple image subtraction detects all differences regardless of their source, and is therefore problematic to use. Superior techniques, analogous to standard statistical tests, can isolate localised differences due to motion from global differences due, for example, to illumination changes. Four such techniques are described. In particular, we introduce a new non-parametric statistical measure which allows a direct probabilistic interpretation of image differences. We expect this to be applicable to a wide range of image formation processes. Its application to medical images is discussed.

In recent years there has been much progress in the development of facial recognition systems. The FERET series of tests reported the black box performance of several such systems working on stored face images. Much less effort has been spent in studying the behaviour of systems under realistic conditions of use. We describe and analyse the result of a trial of a door access control system based on a model-based approach. The trial consisted of 10 registered users making over 200 accesses during a 2 week period. We describe the internal failure modes and the performance characteristics of the system, identify inter- and intra-person dependencies and make recommendations for future work.

The aim of this paper is to bring together aspects of our recent work in machine vision. We discuss the relative merits of various platforms for the implementation of machine vision algorithms with particular emphasis on the DIP chip, a custom device designed following an in-vestigation into the computational requirements of a range of image manipulation algorithms. We will show: (i) how certain operations may be made easily avail-able in hardware; (ii) that algorithms may be redesigned to take this into account; (iii) that high performance processors other than work-stations are the natural platforms for these algorithms; and that (iv) that such platforms will be necessary to convincingly demonstrate the potential scalability of these algorithms. 1 Algorithms for Machine Vision The range and number of image manipulation algorithms developed over the years

The computational and communication requirements of a wide range of image processing algorithms were analysed and used to specify the design of a general purpose image processing device. The algorithms considered are restricted to image-in and image or feature-out type algorithms. These algorithms are categorised by the demands that they would place on a hardware architecture. We conclude that the major consideration is the off-chip input/output limitation which needs to be able to exploit redundancy in both the data and processor instructions. This paper summarises: the main conclusions of this work, an architecture which we have designed in an attempt to meet the identified constraints and how it differs from previous attempts.

Describes image rectification, explains why it is necessary, how it is performed and proposes a hardware implementation for realtime operation as well as outlining a number of important additional applications that such a module may support, including ground plane obstacle detection.>