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Fourier analysis is often used as a tool to facilitate the extraction of texture information from image data. However, in situations where the texture patch does not entirely fill the region of analysis, information relating to the shape of the patch becomes entwined with its texture content, thus contaminating the Fourier spectrum and corrupting the texture information. We propose the use of a frequency deconvolution algorithm to remove the artefacts introduced by shape components, permitting the Fourier-based analysis of non-rectangular image patches. The algorithm is demostrated on a texture recognition task involving the entire Brodatz album.

An important challenge in mapping image-processing techniques onto applications is the lack of quantitative performance measures. From a systems engineering perspective these are essential if system level requirements are to be decomposed into sub-system requirements which can be understood in terms of algorithm selection and performance optimisation.Nowhere in computer vision is this more evident than in the area of image segmentation. This is a vigorous and innovative research activity, but even after nearly two decades of progress, it remains almost impossible to answer the question "what would the performance of this segmentation algorithm be under these new conditions?" -To begin to address this shortcoming, we have devised a well-principled metric for assessing the relative performance of two segmentation algorithms. This allows meaningful objective comparisons to be made between their outputs. It also estimates the absolute performance of an algorithm given ground truth. Our approach is an information theoretic one. In this paper, we describe the theory and motivation of our method, and present practical results obtained from a range of state of the art segmentation methods. We demonstrate that it is possible to measure the objective performance of these algorithms, and to use the information so gained to provide clues about how their performance might be improved.

The classification of image regions of interest in an image is an important area of research. Generally most investigations concentrate on the optimisation of the constituent parts of the system without regard to the overall performance. This work takes a system centred approach. Using a novel multi-class receiver operating characteristic, which also allows for the inherent uncertainty present, it is shown that the influence of different region based segmentation algorithms on the performance of classification algorithms can be determined. The results generated, using this approach, for an airborne infrared application highlight the non-linear relationship between the constituent algorithms and show quantitatively that the system performance can be strongly class and segmenter/classifier dependent.