Machine vision techniques for inspection of dry-fibre composite preforms in the aerospace industry

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents the results of a three year investigation into machine vision techniques for in-process automated inspection of dry-fibre composite preforms. Efficient texture analysis based techniques have been developed, tested, and implemented in a prototype robotic assembly cell. Industrial constraints have been considered in the development of all the algorithms described. A single channel texture analysis model is described which can successfully segment images containing only a few textures. The model is based on convolution of the image with small kernels optimised for the task, and is elegant in the sense that it is computationally simple and easily realisable in low cost hardware. A new convolution kernel optimisation algorithm is described. It is demonstrated that convolution kernels can also be optimised to perform as edge operators in simple textured images. A novel boundary refinement algorithm is described which reduces the inspection errors inherent in texture based boundary estimates. The algorithm takes the form of a local search, using the texture estimate as a guiding template, and selects edge points by maximising a merit function. Optimum parameters for the merit function are obtained using multiple training images in conjunction with simple function optimisation algorithms.This study is funded by the Engineering and Physical Sciences Research Council (EPSRC) and Dowty Aerospace Propellers Ltd

A Self-Organizing Neural System for Learning to Recognize Textured Scenes

A self-organizing ARTEX model is developed to categorize and classify textured image regions. ARTEX specializes the FACADE model of how the visual cortex sees, and the ART model of how temporal and prefrontal cortices interact with the hippocampal system to learn visual recognition categories and their names. FACADE processing generates a vector of boundary and surface properties, notably texture and brightness properties, by utilizing multi-scale filtering, competition, and diffusive filling-in. Its context-sensitive local measures of textured scenes can be used to recognize scenic properties that gradually change across space, as well a.s abrupt texture boundaries. ART incrementally learns recognition categories that classify FACADE output vectors, class names of these categories, and their probabilities. Top-down expectations within ART encode learned prototypes that pay attention to expected visual features. When novel visual information creates a poor match with the best existing category prototype, a memory search selects a new category with which classify the novel data. ARTEX is compared with psychophysical data, and is benchmarked on classification of natural textures and synthetic aperture radar images. It outperforms state-of-the-art systems that use rule-based, backpropagation, and K-nearest neighbor classifiers.Defense Advanced Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

Machine learning methods for discriminating natural targets in seabed imagery

The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems. These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of ≈ 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation. Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world sonar mosaic imagery. A number of technical challenges arose and these were surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation of pockmark and Sabellaria discrimination is feasible within this framework

Gray-level Texture Characterization Based on a New Adaptive

In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texture characterization. The filter coefficients are updated with the Least Mean Square (LMS) algorithm. The proposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. The main advantage of the new nonlinear exponential adaptive 2-D filter is the reduced number of coefficients used to characterize the nonlinear parametric models of images regarding the 2-D second-order Volterra model. Whatever the degree of the non-linearity, the problem results in the same number of coefficients as in the linear case. The characterization efficiency of the proposed exponential model is compared to the one provided by both 2-D linear and Volterra filters and the cooccurrence matrix method. The comparison is based on two criteria usually used to evaluate the features discriminating ability and the class quantification. Extensive experiments proved that the exponential model coefficients give better results in texture discrimination than several other parametric features even in a noisy context