Leveraging Unstructured Image Data for Product Quality Improvement

Recently, traditional quality assurance methods, which often require human expertise, have been accompanied by more automated methods that use machine learning technology. These methods offer manufacturers to reduce error rates and, consequently, to increase margins as well. In particular, predictive quality assurance (Pre QA) allows to minimize expenses by feeding back information from product returns and quality checks into the early product development. However, Pre QA requires detailed information about previous quality problems which is not always readily available in a structured form. In this paper, we therefore discuss the potential of leveraging initially unstructured information in the form of images, taken either during quality checks or by customers when returning a product, to the end of product quality improvement. We furthermore show how this might be realized in practice using the case of fashion manufacturing as an example

Impact of object extraction methods on classification performance in surface inspection systems

In surface inspection applications, the main goal is to detect all areas which might contain defects or unacceptable imperfections, and to classify either every single 'suspicious' region or the investigated part as a whole. After an image is acquired by the machine vision hardware, all pixels that deviate from a pre-defined 'ideal' master image are set to a non-zero value, depending on the magnitude of deviation. This procedure leads to so-called "contrast images", in which accumulations of bright pixels may appear, representing potentially defective areas. In this paper, various methods are presented for grouping these bright pixels together into meaningful objects, ranging from classical image processing techniques to machine-learning-based clustering approaches. One important issue here is to find reasonable groupings even for non-connected and widespread objects. In general, these objects correspond either to real faults or to pseudo-errors that do not affect the surface quality at all. The impact of different extraction methods on the accuracy of image classifiers will be studied. The classifiers are trained with feature vectors calculated for the extracted objects found in images labeled by the user and showing surfaces of production items. In our investigation artificially created contrast images will be considered as well as real ones recorded on-line at a CD imprint production and at an egg inspection system. © Springer-Verlag 2009

Single class classifier using FMCD based non-metric distance for timber defect detection

In this work, we propose a robust Mahalanobis one class classifier with Fast Minimum Covariance Determinant estimator (MC-FMCD) for species independent timber defect detection. Having known in timber inspection research that there is a lack of defect samples compared to defect-free samples (imbalanced data), this unsupervised approach applies outlier detection concept with no training samples required. We employ a non-segmenting approach where a timber image will be divided into non-overlapping local regions and the statistical texture features will then be extracted from each of the region. The defect detection works by calculating the Mahalanobis distance (MD) between the features and the distribution average estimate. The distance distribution is approximated using chi-square distribution to determine outlier (defects). The approach is further improved by proposing a robust distribution estimator derived from FMCD algorithm which enhances the defect detection performance. The MC-FMCD is found to perform well in detecting various types of defects across various defect ratios and over multiple timber species. However, blue stain evidently shows poor performance consistently across all timber species. Moreover, the MC-FMCD performs significantly better than the classical MD which confirms that using the robust estimator clearly improved the timber defect detection over using the conventional mean as the average estimator

A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques

In this paper, we systematically review recent advances in surface inspection using computer vision andimage processing techniques, particularly those based on texture analysis methods. The aim is to reviewthe state-of-the-art techniques for the purposes of visual inspection and decision making schemes that areable to discriminate the features extracted from normal and defective regions. This field is so vast that itis impossible to cover all the aspects of visual inspection. This paper focuses on a particular but importantsubset which generally treats visual surface inspection as texture analysis problems. Other topics related tovisual inspection such as imaging system and data acquisition are out of the scope of this survey.The surface defects are loosely separated into two types. One is local textural irregularities which is themain concern for most visual surface inspection applications. The other is global deviation of colour and/ortexture, where local pattern or texture does not exhibit abnormalities. We refer this type of defects as shadeor tonality problem. The second type of defects have been largely neglected until recently, particularly whencolour imaging system has been widely used in visual inspection and where chromatic consistency plays animportant role in quality control. The emphasis of this survey though is still on detecting local abnormalities,given the fact that majority of the reported works are dealing with the first type of defects.The techniques used to inspect textural abnormalities are discussed in four categories, statistical approaches,structural approaches, filter based methods, and model based approaches, with a comprehensivelist of references to some recent works. Due to rising demand and practice of colour texture analysis inapplication to visual inspection, those works that are dealing with colour texture analysis are discussedseparately. It is also worth noting that processing vector-valued data has its unique challenges, which conventionalsurface inspection methods have often ignored or do not encounter.We also compare classification approaches with novelty detection approaches at the decision makingstage. Classification approaches often require supervised training and usually provide better performancethan novelty detection based approaches where training is only carried out on defect-free samples. However,novelty detection is relatively easier to adapt and is particularly desirable when training samples areincomplet

Vision-based neural network classifiers and their applications

A thesis submitted for the degree of Doctor of Philosophy of University of LutonVisual inspection of defects is an important part of quality assurance in many fields of production. It plays a very useful role in industrial applications in order to relieve human inspectors and improve the inspection accuracy and hence increasing productivity. Research has previously been done in defect classification of wood veneers using techniques such as neural networks, and a certain degree of success has been achieved. However, to improve results in tenus of both classification accuracy and running time are necessary if the techniques are to be widely adopted in industry, which has motivated this research. This research presents a method using rough sets based neural network with fuzzy input (RNNFI). Variable precision rough set (VPRS) method is proposed to remove redundant features utilising the characteristics of VPRS for data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable foml for input to an improved BP neural network classifier. The improved BP neural network classifier is improved in three aspects: additional momentum, self-adaptive learning rates and dynamic error segmenting. Finally, to further consummate the classifier, a uniform design CUD) approach is introduced to optimise the key parameters because UD can generate a minimal set of uniform and representative design points scattered within the experiment domain. Optimal factor settings are achieved using a response surface (RSM) model and the nonlinear quadratic programming algorithm (NLPQL). Experiments have shown that the hybrid method is capable of classifying the defects of wood veneers with a fast convergence speed and high classification accuracy, comparing with other methods such as a neural network with fuzzy input and a rough sets based neural network. The research has demonstrated a methodology for visual inspection of defects, especially for situations where there is a large amount of data and a fast running speed is required. It is expected that this method can be applied to automatic visual inspection for production lines of other products such as ceramic tiles and strip steel

Caracterización automática de especies de madera mediante técnicas de clasificación de imágenes

Caracterizar un material consiste en determinar los atributos peculiares del mismo de modo que se distinga claramente de los demás. El material en esta tesis va a ser la madera, en forma de chapas fáciles de escanear para así obtener sus propiedades fotométricas y texturales y su dimensión fractal, como peculiaridades de cada una de ellas. El problema consiste en que las diferencias entre especies pueden llegar a ser menores que las existentes dentro de una misma especie o individuo. Para resolverlo y poder identificar la especie a la que pertenece cada una de las muestras se va a recurrir al uso de técnicas de reconocimiento de patrones basadas en la teoría de la decisión. Se propone en este trabajo de investigación el poner las bases para la automatización del proceso de clasificación de maderas, de bajo coste por el uso de un escáner y un ordenador personal junto con paquetes informáticos de dominio público (ImageJ y Weka). Las digitalización de las chapas de madera y el posterior procesado de las mismas para obtener las características predichas a partir de las cuales poder clasificar cada especie forman la base fundamental de esta tesis. Sin embargo, se plantea en este trabajo establecer la influencia que tienen estos mismos parámetros medidos sobre las componentes de color de la imagen, junto con las propiedades multiescala de la madera. Para éstas se han desarrollado los procedimientos de creación de las correspondientes imágenes, basados en la microscopía de contraste por interferencia diferencial y en los patrones periódicos subyacentes a toda superficie. La combinación de todas estas imágenes y su procesado hace que se formen patrones de gran dimensión, que requieren de una reducción de la matriz de datos. Diferentes algoritmos llevan a obtener distintos patrones, con los que se prueban clasificadores lineales, basados en árboles de decisión, con entrenamiento basado en casos, lineales o combinados aleatorios de varios del mismo tipo. Los resultados obtenidos están a la altura de los conseguidos por otros investigadores, si bien éstos utilizan equipos de elevado coste o, en su defecto, complicados procesos. Estos resultados, además, se presentan con un alto grado de fiabilidad ya que en la mayoría de los trabajos revisados el número de especies utilizado es más limitado que en el caso presente

Caracterización automática de especies de madera mediante técnicas de clasificación de imágenes

Caracterizar un material consiste en determinar los atributos peculiares del mismo de modo que se distinga claramente de los demás. El material en esta tesis va a ser la madera, en forma de chapas fáciles de escanear para así obtener sus propiedades fotométricas y texturales y su dimensión fractal, como peculiaridades de cada una de ellas. El problema consiste en que las diferencias entre especies pueden llegar a ser menores que las existentes dentro de una misma especie o individuo. Para resolverlo y poder identificar la especie a la que pertenece cada una de las muestras se va a recurrir al uso de técnicas de reconocimiento de patrones basadas en la teoría de la decisión. Se propone en este trabajo de investigación el poner las bases para la automatización del proceso de clasificación de maderas, de bajo coste por el uso de un escáner y un ordenador personal junto con paquetes informáticos de dominio público (ImageJ y Weka). Las digitalización de las chapas de madera y el posterior procesado de las mismas para obtener las características predichas a partir de las cuales poder clasificar cada especie forman la base fundamental de esta tesis. Sin embargo, se plantea en este trabajo establecer la influencia que tienen estos mismos parámetros medidos sobre las componentes de color de la imagen, junto con las propiedades multiescala de la madera. Para éstas se han desarrollado los procedimientos de creación de las correspondientes imágenes, basados en la microscopía de contraste por interferencia diferencial y en los patrones periódicos subyacentes a toda superficie. La combinación de todas estas imágenes y su procesado hace que se formen patrones de gran dimensión, que requieren de una reducción de la matriz de datos. Diferentes algoritmos llevan a obtener distintos patrones, con los que se prueban clasificadores lineales, basados en árboles de decisión, con entrenamiento basado en casos, lineales o combinados aleatorios de varios del mismo tipo. Los resultados obtenidos están a la altura de los conseguidos por otros investigadores, si bien éstos utilizan equipos de elevado coste o, en su defecto, complicados procesos. Estos resultados, además, se presentan con un alto grado de fiabilidad ya que en la mayoría de los trabajos revisados el número de especies utilizado es más limitado que en el caso presente

Modification of the rotary machining process to improve surface form

Planing and moulding operations carried out within the woodworking industry make extensive use of rotary machining. Cutter-marks are produced on the timber surface which are generally accepted as unavoidable. More noticeable surface defects may be produced by such factors as cutter-head imbalance, and until recently most research has concentrated on removing these defects. When a high quality finish is required, a further machining operation, such as sanding, is often required to remove cutter-marks. What is required, is a modified machining process which combines a surface closer to the ideal fixed knife finish, whilst retaining the flexibility, practicality and cost effectiveness of rotary machining. [Continues.