Inspection of surface strain in materials using dense displacement fields

We have developed high density image processing techniques for finding the surface strain of an unprepared sample of material from a sequence of images taken during the application of force from a test rig. Not all motion detection algorithms have suitable functional characteristics for this task, as image sequences are characterised by both short- and long-range displacements, non-rigid deformations, as well as a low signal-to-noise ratio and methodological artefacts. We show how a probability-based motion detection algorithm can be used as a high confidence estimator of the strain tensor characterising the deformation of the material. An important issue discussed is how to minimise the number of image brightness differences that need to be calculated. We give results from three studies: mild steel under axial tension, the formation of kink bands in compressed carbon-fibre composite, and non-homogeneous strain fields in a welded aluminium alloy. Because the algorithm offers increased accuracy near motion contrast boundaries, its application has resulted in new mesomechanical observations

Structural Features Of Cursive Arabic Script

We presentatechnique for extracting structural features from cursive Arabic script. After preprocessing, the skeleton of the binary word image is decomposed into a number of segments in a certain order. Each segmentis transformed into a feature vector. The target features are the curvature of the segment, its length relative to other segment lengths of the same word, the position of the segmentrelative to the centroid of the skeleton, and detailed description of curved segments. The result of this method is used to train the Hidden Markov Model to perform the recognition.

Feature Representation and Signal Classification in Fluorescence In-Situ Hybridization Image Analysis

Fast and accurate analysis of fluorescence in-situ hybridization (FISH) images for signal counting will depend mainly upon two components: a classifier to discriminate between artifacts and valid signals of several fluorophores (colors), and well discriminating features to represent the signals. Our previous work has focused on the first component. To investigate the second component, we evaluate candidate feature sets by illustrating the probability density functions (pdfs) and scatter plots for the features. The analysis provides first insight into dependencies between features, indicates the relative importance of members of a feature set, and helps in identifying sources of potential classification errors. Class separability yielded by different feature subsets is evaluated using the accuracy of several neural network (NN)-based classification strategies, some of them hierarchical, as well as using a feature selection technique making use of a scatter criterion. The complete analysis recommends several intensity and hue features for representing FISH signals. Represented by these features, around 90% of valid signals and artifacts of two fluorophores are correctly classified using the NN. Although applied to cytogenetics, the paper presents a comprehensive, unifying methodology of qualitative and quantitative evaluation of pattern feature representation essential for accurate image classification. This methodology is applicable to many other real-world pattern recognition problems