Tim J. Atherton

Size invariant circle detection

The Circle Hough Transform (CHT) has become a common method for circle detection in numerous image processing applications. Various modifications to the basic CHT operation have been suggested which include: the inclusion of edge orientation, simultaneous consideration of a range of circle radii, use of a complex accumulator array with the phase proportional to the log of radius, and the implementation of the CHT as filter operations. However, there has also been much work recently on the definition and use of invariance filters for object detection including circles. The contribution of the work presented here is to show that a specific combination of modifications to the CHT is formally equivalent to applying a scale invariant kernel operator. This work brings together these two themes in image processing which have herewith been quite separate. Performance results for applying various forms of CHT filters incorporating some or all of the available modifications, along with results from the invariance kernel, are included. These are in terms of an analysis of the peak width in the output detection array (with and without the presence of noise), and also an analysis of the peak position in terms of increasing noise levels. The results support the equivalence between the specific form of the CHT developed in this work and the invariance kernel.

4D volume rendering with the Shear Warp factorisation

A novel approach for rendering time-varying data based on the Shear-Warp factorisation is presented. Reduction in storage space is achieved by detecting the changed areas within each volume and compressing them. Time-coherence is exploited by detecting and rendering the changes in every volume while spatial-coherence is exploited by utilising a data structure that allows easy volume update and stores information about the empty space within each volume.

An heterogeneous M-SIMD architecture for Kalman filter controlled processing of image sequences

The use of a heterogeneous multiple-SIMD (M-SIMD) architecture with image-based measurements and optimal (Kalman) estimators for the analysis of image sequences is illustrated. The architecture integrates SIMD and MIMD processing paradigms, combining heterogeneity of processor types matched to the computation at each level and operational autonomy within an SIMD array. It is suited to real-time simultaneous data parallel (iconic) and control parallel (numeric) processing.<<ETX>>

Vertical and horizontal disparities from phase

Abstract We apply the notion that phase differences can be used to interpret disparity between a pair of stereoscopic images. Indeed, phase relationships can also be used to obtain both orientation and probabilistic measures towards the computation of edges and corners, as well as the directional instantaneous frequency of an image field. The method of phase differences is shown to be equivalent to a Newton-Raphson root finding iteration through the resolutions of band-pass filtering. The method does, however, suffer from stability problems, and in particular stationary phase and interference. The stability problems associated with this technique are implicitly derived from the mechanism used to interpret disparity, which in general requires an assumption of linear phase and the local instantaneous frequency. We present two techniques. First, we use the centre frequency of the applied band-pass filter to interpret disparity. This interpretation, however, suffers heavily from phase error, and requires considerable damping prior to convergence. Second, we use the derivative of phase to obtain the instantaneous frequency from an image, which is then used to improve the disparity estimate. These ideas are extended into 2D where it is possible to extract both vertical and horizontal disparities.

A hierarchical multiple-SIMD architecture for image analysis

Real-time image analysis requires the use of massively parallel machines. Conventional parallel machines consist of an array of identical processors organized in either single instruction multiple data (SIMD) or multiple instruction multiple data (MIMD) configurations. Machines of this type generally only operate effectively on parts of the image analysis problem. SIMD on the low level processing and MIMD on the high level processing. In this paper we describe the Warwick Pyramid Machine, an architecture consisting of both SIMD and MIMD parts in a multiple-SIMD (MSIMD) organization which can operate effectively at all levels of the image analysis problem.

Multiple motions from instantaneous frequency

The measurement of multiple velocities using phase-based methods is discussed. In particular, phase gradients (instantaneous frequency) from different bandpass channels (quadrature filter outputs) are used to estimate multiple image velocities in a single neighborhood. The approach is similar to that of M. Shizawa and K. Mase (1990) in which nth-order differential operators are required to compute n simultaneous velocity estimates. However, to use instantaneous frequency, the output of each channel must be differentiated only once.<<ETX>>

Performance evaluation of the hierarchical Hough transform on an associative M-SIMD architecture

The application of multiple-single instruction multiple-data (M-SIMD) processing techniques to the problem of finding straight lines in an image is described, and the advantages of using these techniques instead of direct SIMD computation are illustrated by reference to a hierarchical architecture: the Warwick pyramid machine. The advantages over conventional implementations of the Hough transform are discussed, and performance timings for a hierarchical implementation are provided.<<ETX>>

4D volume rendering with the shear warp factorisation: extensions and quantitative results

We present extensions to an approach for rendering time-varying data based on the shear-warp factorisation. The change detection technique is adapted to process Poisson distributed datasets, a modified block differencing technique that decreases the change detection errors is presented along with a modification of the original run-length encoding method which allows better spatial compression for volume with uniform and slowly changing background. Results are presented on datasets originating from optical microscopes which demonstrate the efficiency of the proposed methods.

The Inference of Structure in Images using Multi-local Quadrature Filters.

Two techniques are presented for corner detection. First, a band of filters are applied with equal radial spatial frequency, but different orientation preferences locally in the image domain. From the energy response, a linear Fourier transform is taken to give confidence measures of both “cornerness” and “edgeness. Second, we consider a multi-local spatial separation of filters that lie on a constant radius from a point of interest. This second stage of processing allows a wider classification of image structure. As a result, we infer the presence of line end points, “L”, “T”, “Y” and “X” junctions using epistemic probabilities. The results are indicative of a relationship between Fourier and Spatial domain models of filtering.

Passive Estimation of Range to Objects from Image Sequences

The range and physical size of an object may be determined from a sequence of image size measurements as an object is approached. The inverse image size is linear with the distance travelled by the camera. A recursive (Kalman) estimator is used to give the object range and size. Results are presented for an example image sequence.