There is a trend in manufacturing towards fully automated production facilities in which all operations are integrated by computer based information systems. The current generation of industrial inspection systems lack the necessary flexibility to operate in these environments. AI based Image Understanding systems have the necessary level of generality, achieved through the use of domain specific object models. These models are used to guide early visual processing, and must be supplied to the system. Current theories in cognitive psychology call for a reevaluation of the role of such 'auxiliary' knowledge in early visual processing. Recent work suggests that very general cognitive processes may build up a hierarchical representation of the world. The emphasis is currently on such generic cognitive processes rather than on the use of world knowledge. A novel approach to image processing, in which emphasis is placed on generic low and intermediate level techniques, is proposed in this thesis. This approach, termed the descriptor approach, delays the use of domain specific models until a full description of the image has been produced. A prototype industrial inspection system has been implemented, based on the descriptor approach: the Hierarchical Scene Description (HISD) system. General image features are extracted from images of populated PCBs, and subsequently transformed into a database of prolog facts by an interface subsystem. Finally the intermediate level vision subsystem uses rules to reason about these features, building up a semantic net based description of the scene. HISD successfully builds up hierarchical descriptions of real industrial PCB images in terms of geometric shapes, their coordinates, and spatial relationships between shapes. The results are displayed graphically and are achieved without the use of any object models, thus avoiding the problems of inflexibility and lack of generality associated with more complex model based systems.

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Abstract The purpose of this thesis is to present a case study of the development, implementation and performance analysis of a color-based visual surface inspection method for wood properties. The main contribution of the study is to answer the need of design strategies, performance characterization methods and case studies in the field of automated visual inspection, and especially wood surface inspection. In real time color-based inspection, the complexity of the methods is important. In this study, defect detection and recognition methods based on color histogram percentile features are proposed. The color histogram percentile features were noticed to be able to recognize well wood surface defects with relatively low complexity. A common problem in visual inspection applications is the collection and labelling of training material since human made labellings can be errorneous. Further, the classifiers are relatively static when once trained, thus offering only little possibilities for adjusting classification. In the study, a self-organizing map (SOM) -based approach for classifier user interface in visual surface inspection problems is introduced. The approach relieves the labelling of training material, simplifies retraining, provides an illustrative an intuitive user interface and offers a convenient way of controlling classification. The study is illustrated with four experiments related to the method development and analysis. In the first experiment, a simulator environment is used for determining the relationship of the defect detection and recognition and grading accuracy. The second experiment considers the suitability of different color spaces for wood defect recognition under changing illumination. RGB color space gives the best results compared to grey-level and other color spaces. The third experiment presents the experimental wood surface inspection setup implementing the method developed in this study. Comparative performance analysis results are presented and the difficulties, mainly caused by segmentation of the defects, are discussed. The fourth experiment demonstrates the suitability of the method for parquet sorting and shows the potential of the non-segmenting approach.

Neste trabalho é proposto a técnica da Transformada de Hough para a Inspeção Visual Automática de placas retangulares, visando a determinação de características tais como perímetro, ortogonalidade, centro de massa, área, independente de rotação e translação do objeto dentro da imagem. Os resultados obtidos por essa técnica são avaliados pela comparação com o método dos Momentos, o recurso mais utilizado para a determinação de área e centro de massa para imagens bi-dimensionais. A análise estatística dos dados mostra a robustez da técnica da Transformada Hough para imagens ruidosas. / In this work, is proposed the Hough transform method for the Automated Visual lnspection of rectangular boards. The aim is to extract features such as position, perimeter and area, despite of object translation and rotation. The results obtained by this method are evaluated by comparing them with the method of Moments, the most used method to determine the area and center of mass for bi-dimensional images. Statistical analysis show the robustness of Hough transform for noisy images visual inspection.

Neste trabalho é proposto a técnica da Transformada de Hough para a Inspeção Visual Automática de placas retangulares, visando a determinação de características tais como perímetro, ortogonalidade, centro de massa, área, independente de rotação e translação do objeto dentro da imagem. Os resultados obtidos por essa técnica são avaliados pela comparação com o método dos Momentos, o recurso mais utilizado para a determinação de área e centro de massa para imagens bi-dimensionais. A análise estatística dos dados mostra a robustez da técnica da Transformada Hough para imagens ruidosas. / In this work, is proposed the Hough transform method for the Automated Visual lnspection of rectangular boards. The aim is to extract features such as position, perimeter and area, despite of object translation and rotation. The results obtained by this method are evaluated by comparing them with the method of Moments, the most used method to determine the area and center of mass for bi-dimensional images. Statistical analysis show the robustness of Hough transform for noisy images visual inspection.

Objective. To assess the acceptability of cervical screening using direct visual inspection after acetic acid application followed by immediate cryotherapy for cervical intraepithelial neoplasia among women in rural Guatemala. Materials and Methods. An unselected group of 1,052 women voluntarily registered to undergo cervical screening using direct visual inspection of the cervix after acetic acid application. Women with acetowhite changes consistent with cervical intraepithelial neoplasia were offered immediate cryotherapy. Results. Cervical screening was deferred in 80 (7.6%) registrants, and 18 (1.7%) refused to undergo an examination. Among the 954 registrants screened, 125 (13%) had findings consistent with cervical intraepithelial neoplasia. Cryotherapy was deferred in three patients. A total of 121 (99%) women agreed to immediate cryotherapy. Conclusion. Direct cervical visualization after acetic acid application followed by immediate cryotherapy for acetowhite changes consistent with cervical intraepithelial neoplasia would be a well-accepted method of cervical screening in rural Guatemala.

<p>Manufacturers have begun to adopt advanced digital methods and tools to release higher-quality products more quickly at a lower cost. Model-based technologies and Visual/Vision Inspection System (VIS) are two representative approaches and are trends to facilitate manufacturers’ digitalization. With model-based technologies, manufacturers can define digital product requirements and specifications as the primary data source for all engineering activities during the entire product lifecycle, including design, manufacturing, and inspection, rather than relying on traditional paper-based documents. Moreover, the manufacturers can improve product defect detection efficiency and reduce labor requirements by implementing a VIS without artificial visual inspection in their inspection process. Traditionally, inspectors rely heavily on information from 2D engineering drawings to inspect manufactured products with/without a VIS. This inspection process is out of the model-based context, hindering manufacturers from transiting paper-based to model-based processes.</p> <p>To answer the question of how we can adapt a VIS to support model-based inspection, the author selected a developmental research methodology to address the research gap of model-based visual inspection. An image-based Model-Based Visual Inspection System (MBVIS) framework integrating Model-Based Definition (MBD) and VIS applications was proposed to demonstrate the process of utilizing the Geometric Dimensioning and Tolerancing (GD&T) information from the MBD dataset and driving the VIS to measure physical targets. A plastic bottle with a high dimensional standard obtained from a plastic bottle manufacturer was chosen to evaluate the proposed system’s performance in extracting GD&T information from the MBD dataset and measuring relevant critical bottle dimensions. </p> <p>The interchangeability of the proposed MBVIS with a commercial VIS from the partner bottle company in measuring critical bottle dimensions was proven by conducting the Bland and Altman (B&A) analysis and other statistical analyses in this research. Moreover, two other customized tests were run to understand the effectiveness of the GD&T extraction and measurement result output processes within the MBVIS framework. The results indicated that both processes were reliable in extracting GD&T information and exporting measurement results.</p>

O objetivo deste trabalho é o desenvolvimento de um método dedicado de classificação para placas de madeira utilizadas na fabricação de lápis, utilizando procedimentos de visão computacional. O processo aqui proposto, foi idealizado buscando uma metodologia que pudesse ser realizada com baixa complexidade computacional, ou seja, os cálculos dos algoritmos utilizando apenas operações simples - do tipo soma, subtração, multiplicação e divisão - em imagens em níveis de cinza. A intenção em utilizar apenas as operações básicas citadas, tem o objetivo de tornar o método implementável em arquiteturas com tecnologia VLSI, notadamente em Arquiteturas Sistólicas. O trabalho descreve o ciclo de produção do lápis localizando a etapa onde é proposta a metodologia de classificação das placas de madeira. Nesta etapa, há uma seqüência de procedimentos, descritos ao longo do trabalho, que compreendem a aquisição da imagem das placas, a extração de características das imagens, o processamento dessas características e por fim os algoritmos de classificação. Na etapa de extração de características, buscou-se com a aplicação de um método de Limiar automático que utiliza a entropia de Shannon, extrair informações suficientes para classificar adequadamente as placas de madeiras em diferentes classes, fornecendo dessa forma, um sistema ágil, repetitivo e de baixo custo para aproveitamento da madeira em diferentes produtos finais. / The objective of this work was to develop a dedicated computer vision method for the classification of wooden plates used in pencil manufacturing. The process here proposed was idealized looking for a low computational complexity methodology that could be accomplished in VLSI, as for instance using Systolic Computer Architectures made of logic arrays. The pencil cycle of production is described, locating the stage where the proposed classification methodology should be used. There is a sequence of procedures, along the work, that describe the acquisition, extraction of the characteristics and the processing of the images, and finally the classification algorithms. For the extraction of characteristics of the images, it was used an automatic method for the threshold determination, based on Shannon\'s entropy. The information supplied by the threshold determination method allows classifying the plates in different classes. The analysis of the results showed that the method performs well is repetitive and efficient on the classification and its use can be extended to classifying other final products.