CATALOG: a system for detection and rendering of internal log defects using computer tomography

This paper describes the design and implementation of a machine vision system CATALOG for detection and classification of some important internal defects in hardwood logs via analysis of computer axial tomography (CT or CAT) images. The defect identification and classification in CATALOG consists of two phases. The first phase comprises of the segmentation of a single CT image slice, which results in the extraction of 2D defect-like regions from the CT image slice. The second phase comprises of the correlation of the 2D defect-like regions across CT image slices in order to establish 3D support. The segmentation algorithm for a single CT image is a complex form of multiple-value thresholding that exploits both, the prior knowledge of the wood structure within the log and the gray-level characteristics of the image. The algorithm for extraction of 2D defect-like regions in a single CT image first locates the pith of the log cross section, groups the pixels in the segmented image on the basis of their connectivity and classifies each 2D region as either a defect-like region or a defect-free region using shape, orientation and morphological features. Each 2D defect-like region is classified as a defect or non-defect via correlation across corresponding 2D defect-like regions in neighboring CT image slices. The 2D defect-like regions with adequate 3D support are labeled as true defects. The current version of CATALOG is capable of 3D reconstruction and rendering of the log and its internal defects from the individual CT image slices. CATALOG is also capable of simulation and rendering of key machining operations such as sawing and veneering on the 3D reconstructions of the logs. The current version of CATALOG is intended as a decision aid for sawyers and machinists in lumber mills and also as an interactive training tool for novice sawyers and machinists. Received: 1 August 1997 / Accepted: 25 August 1999     

Automatic document classification and indexing in high-volume applications

In this paper a system for analysis and automatic indexing of imaged documents for high-volume applications is described. This system, named STRETCH (STorage and RETrieval by Content of imaged documents), is based on an Archiving and Retrieval Engine, which overcomes the bottleneck of document profiling bypassing some limitations of existing pre-defined indexing schemes. The engine exploits a structured document representation and can activate appropriate methods to characterise and automatically index heterogeneous documents with variable layout. The originality of STRETCH lies principally in the possibility for unskilled users to define the indexes relevant to the document domains of their interest by simply presenting visual examples and applying reliable automatic information extraction methods (document classification, flexible reading strategies) to index the documents automatically, thus creating archives as desired. STRETCH offers ease of use and application programming and the ability to dynamically adapt to new types of documents. The system has been tested in two applications in particular, one concerning passive invoices and the other bank documents. In these applications, several classes of documents are involved. The indexing strategy first automatically classifies the document, thus avoiding pre-sorting, then locates and reads the information pertaining to the specific document class. Experimental results are encouraging overall; in particular, document classification results fulfill the requirements of high-volume application. Integration into production lines is under execution. Received March 30, 2000 / Revised June 26, 2001     

Scene change detection techniques for video database systems

Scene change detection (SCD) is one of several fundamental problems in the design of a video database management system (VDBMS). It is the first step towards the automatic segmentation, annotation, and indexing of video data. SCD is also used in other aspects of VDBMS, e.g., hierarchical representation and efficient browsing of the video data. In this paper, we provide a taxonomy that classifies existing SCD algorithms into three categories: full-video-image-based, compressed-video-based, and model-based algorithms. The capabilities and limitations of the SCD algorithms are discussed in detail. The paper also proposes a set of criteria for measuring and comparing the performance of various SCD algorithms. We conclude by discussing some important research directions.     

Human Error and Disturbance Occurrence in Manufacturing Systems (HEDOMS): A Framework and a Toolkit for Practical Analysis

New demands on modern manufacturing systems, such as increased flexibility, higher quality standards, customer responsiveness, and higher innovative capacities, have emphasised the need for higher levels of overall system reliability. Within this context disturbances play a critical role, because of the effects they may have on production or on safety, which are both strong determinants of overall system performance. The main focus of this paper is the reliability of manufacturing personnel and the way in which this interrelates with overall system performance. A framework – Human Error and Disturbance Occurrence in Manufacturing Systems (HEDOMS) – integrates human reliability with overall system performance, relating human error with disturbance occurrence and handling. HEDOMS has been extended into a toolkit to enable the identification of potential for human error and disturbance occurrence in manufacturing systems, as well as the definition of suitable error reduction measures.     

The New Frontier Issues for Cognition Technology

Cognitive engineering has developed enormously over the last fifteen years. Yet, despite many excellent research projects and publications, its full potential has not been embraced into mainstream system design. This paper will examine the reasons for this failure and argue that the problem is not simply inertia or lack of education. There are strong organisational influences that cause resistance to this particular approach. The discipline itself has characteristics that make it fragile in the modern corporate structure. In addition, the cognitive engineers themselves are not blameless in the equation. They appear to have done exactly what they criticise the engineering community for doing: they have packaged their product in a manner that is not ‘user friendly’ to its target population, not structured to suit its application, and not output in the format required. Suggestions will be made to rectify the situation: a list of actions is proposed for practising cognitive engineers to make their product more likely to enjoy widespread uptake.     

铁轨表面缺陷的视觉检测与识别算法

提出一种铁轨表面缺陷的视觉检测与识别算法。设计铁轨表面缺陷视觉检测与识别系统的总体结构,基于水平投影法提取铁轨表面区域,采用逻辑操作组合检测结果,使用 BP 神经网络进行缺陷分类。实验结果表明,该算法能准确地检测与识别铁轨表面的疤痕和波纹擦伤这2种缺陷,分类正确率分别达到99%和95%。     

A method for inspecting industrial parts surfaces based on an optics model

A new method based on an optics model for highly reliable surface inspection of industrial parts has been developed. This method uses multiple images taken under different camera conditions. Phong's model is employed for surface reflection, and then the albedo and the reflection model parameters are estimated by the least squares method. The developed method has advantages over conventional binarization in that it can easily determine the threshold of product acceptability and cope with changes in light intensity when detecting defects.     

Automatic high-resolution optoelectronic photogrammetric 3D surface geometry acquisition system

A fast, high-resolution, automatic, non-contact 3D surface geometry measuring system using a photogrammetric optoelectronic technique based on lateral-photoeffect diode detectors has been developed. Designed for the acquisition of surface geometries such as machined surfaces, biological surfaces, and deformed parts, the system can be used in design, manufacturing, inspection, and range finding. A laser beam is focused and scanned onto the surface of the object to be measured. Two cameras in stereo positions capture the reflected light from the surface at 10 kHz. Photogrammetric triangulation quickly transforms the pair of 2D signals created by the camera detectors into 3D coordinates of the light spot. Because only one small spot on the object is illuminated at a time, the stereo correspondence problem is solved in real time. The resolution is determined by a 12-bit A/D converter and can be improved up to 25 60025 600 by oversampling. The irregular 3D data can be regularized for use with image-based algorithms. Received: 8 October 1996 / Accepted: 3 February 1997     

Text-based approaches for non-topical image categorization

The rapid expansion of multimedia digital collections brings to the fore the need for classifying not only text documents but their embedded non-textual parts as well. We propose a model for basing classification of multimedia on broad, non-topical features, and show how information on targeted nearby pieces of text can be used to effectively classify photographs on a first such feature, distinguishing between indoor and outdoor images. We examine several variations to a TF*IDF-based approach for this task, empirically analyze their effects, and evaluate our system on a large collection of images from current news newsgroups. In addition, we investigate alternative classification and evaluation methods, and the effects that secondary features have on indoor/outdoor classification. Using density estimation over the raw TF*IDF values, we obtain a classification accuracy of 82%, a number that outperforms baseline estimates and earlier, image-based approaches, at least in the domain of news articles, and that nears the accuracy of humans who perform the same task with access to comparable information. Published online: 22 September 2000     

The Design of Human–Machine Interface for Accident Support in Nuclear Power Plants

As feedback from Three Mile Island No. 2, a large amount of human–machine interface (HMI) design has been proposed to support operators during accidents by presenting information on plant status, some of which is implemented in commercial nuclear power plants (NPPs). However, it has not yet been discussed what role HMI should play under conditions where operators must take action as instructed under emergency operating procedure. Regarding this principal issue, an HMI design is proposed together with specific screen images. The advantage of this design is based on the involvement of an ex-operator in NPP in this paper. The participation of users in the design of HMI has been rare. Accordingly the proposed HMI is comprehensive for average operators and is expected to be acceptable for future implementation in commercial NPPs. The effectiveness of the proposed HMI has been examined in a small experiment.     

Wood inspection with non-supervised clustering

Abstract. The appearance of sawn timber has huge natural variations that the human inspector easily compensates for mentally when determining the types of defects and the grade of each board. However, for automatic wood inspection systems these variations are a major source for complication. This makes it difficult to use textbook methodologies for visual inspection. These methodologies generally aim at systems that are trained in a supervised manner with samples of defects and good material, but selecting and labeling the samples is an error-prone process that limits the accuracy that can be achieved. We present a non-supervised clustering-based approach for detecting and recognizing defects in lumber boards. A key idea is to employ a self-organizing map (SOM) for discriminating between sound wood and defects. Human involvement needed for training is minimal. The approach has been tested with color images of lumber boards, and the achieved false detection and error escape rates are low. The approach also provides a self-intuitive visual user interface. Received: 16 December 2000 / Accepted: 8 December 2001 Correspondence to: O. Silvén     

ICADA: Intelligent computer aided defect analysis for castings

An intelligent computer aided defect analysis (ICADA) system, based on artificial intelligence techniques, has been developed to identify design, process or material parameters which could be responsible for the occurrence of defective castings in a manufacturing campaign. The data on defective castings for a particular time frame, which is an input to the ICADA system, has been analysed. It was observed that a large proportion, i.e. 50–80% of all the defective castings produced in a foundry, have two, three or four types of defects occurring above a threshold proportion, say 10%. Also, a large number of defect types are either not found at all or found in a very small proportion, with a threshold value below 2%. An important feature of the ICADA system is the recognition of this pattern in the analysis. Thirty casting defect types and a large number of causes numbering between 50 and 70 for each, as identified in the AFS analysis of casting defects—the standard reference source for a casting process—constituted the foundation for building the knowledge base. Scientific rationale underlying the formation of a defect during the casting process was identified and 38 metacauses were coded. Process, material and design parameters which contribute to the metacauses were systematically examined and 112 were identified as rootcauses. The interconnections between defects, metacauses and rootcauses were represented as a three tier structured graph and the handling of uncertainty in the occurrence of events such as defects, metacauses and rootcauses was achieved by Bayesian analysis. The hill climbing search technique, associated with forward reasoning, was employed to recognize one or several root causes.     

Concurrency control in hierarchical multidatabase systems

Over the past decade, significant research has been done towards developing transaction management algorithms for multidatabase systems. Most of this work assumes a monolithic architecture of the multidatabase system with a single software module that follows a single transaction management algorithm to ensure the consistency of data stored in the local databases. This monolithic architecture is not appropriate in a multidatabase environment where the system spans multiple different organizations that are distributed over various geographically distant locations. In this paper, we propose an alternative multidatabase transaction management architecture, where the system is hierarchical in nature. Hierarchical architecture has consequences on the design of transaction management algorithms. An implication of the architecture is that the transaction management algorithms followed by a multidatabase system must be composable– that is, it must be possible to incorporate individual multidatabase systems as elements in a larger multidatabase system. We present a hierarchical architecture for a multidatabase environment and develop techniques for concurrency control in such systems. Edited by R. Sacks-Davis. Received June 27, 1994 / Accepted September 26, 1995     

An automatic assessment scheme for steel quality inspection

This paper presents an automatic system for steel quality assessment, by measuring textural properties of carbide distributions. In current steel inspection, specially etched and polished steel specimen surfaces are classified manually under a light microscope, by comparisons with a standard chart. This procedure is basically two-dimensional, reflecting the size of the carbide agglomerations and their directional distribution. To capture these textural properties in terms of image fea tures, we first apply a rich set of image-processing operations, including mathematical morphology, multi-channel Gabor filtering, and the computation of texture measures with automatic scale selection in linear scale-space. Then, a feature selector is applied to a 40-dimensional feature space, and a classification scheme is defined, which on a sample set of more than 400 images has classification performance values comparable to those of human metallographers. Finally, a fully automatic inspection system is designed, which actively selects the most salient carbide structure on the specimen surface for subsequent classification. The feasibility of the overall approach for future use in the production process is demonstrated by a prototype system. It is also shown how the presented classification scheme allows for the definition of a new reference chart in terms of quantitative measures. Received: 16 February 1999 / Accepted: 29 March 2000     

Specifying and Analysing System-Level Inter-Component Interfaces

In control systems, the interfaces between software and its embedding environment are a major source of costly errors. For example, Lutz reported that 20–35% of the safety-related errors discovered during integration and system testing of two spacecraft were related to the interfaces between the software and the embedding hardware. Also, the software’s operating environment is likely to change over time, further complicating the issues related to system-level inter-component communication. In this paper we discuss a formal approach to the specification and analysis of inter-component communication using a revised version of RSML (Requirements State Machine Language). The formalism allows rigorous specification of the physical aspects of the inter-component communication and forces encapsulation of communication-related properties in well-defined and easy-to-read interface specifications. This enables us both to analyse a system design to detect incompatibilities between connected components and to use the interface specifications as safety kernels to enforce safety constraints.     

Model-based detection,segmentation, and classification for image analysis using on-line shape learning

Detection, segmentation, and classification of specific objects are the key building blocks of a computer vision system for image analysis. This paper presents a unified model-based approach to these three tasks. It is based on using unsupervised learning to find a set of templates specific to the objects being outlined by the user. The templates are formed by averaging the shapes that belong to a particular cluster, and are used to guide a probabilistic search through the space of possible objects. The main difference from previously reported methods is the use of on-line learning, ideal for highly repetitive tasks. This results in faster and more accurate object detection, as system performance improves with continued use. Further, the information gained through clustering and user feedback is used to classify the objects for problems in which shape is relevant to the classification. The effectiveness of the resulting system is demonstrated in two applications: a medical diagnosis task using cytological images, and a vehicle recognition task. Received: 5 November 2000 / Accepted: 29 June 2001 Correspondence to: K.-M. Lee     

Investigating the Work of Industrial Schedulers through Field Study

The study of planning and scheduling from a human factors perspective has re-emerged as an important area for research in contemporary manufacturing enterprises. Improved decision support systems are needed that optimally integrate people with computer-based systems in order to increase productivity and responsiveness. However, there is a dearth of knowledge on the reality of the human factors of planning and scheduling practice across manufacturing industry. This paper reviews previous studies of scheduling practice and demonstrates the lack of clearly reported and justified methods to study scheduling behaviour and to interpret and represent the findings. Field study is proposed as the most appropriate approach for carrying out studies of scheduling in practice and various methods used in scheduling field studies are specified. Particular emphasis is given to the investigation of the nature of planning and scheduling practice in the context of a functionally, spatially and temporally complex environment.     

Building a color classification system for textured and hue homogeneous surfaces: system calibration and algorithm

We present a system for classifying the color aspect of textured surfaces having a nearly constant hue (such as wooden boards, textiles, wallpaper, etc.). The system is designed to compensate for small fluctuations (over time) of the light source and for inhomogeneous illumination conditions (shading correction). This is an important feature because even in industrial environments where the lighting conditions are controlled, a constant and homogeneous illumination cannot be guaranteed. Together with an appropriate camera calibration (which includes a periodic update), our approach offers a robust system which is able to “distinguish” (i.e., classify correctly) between surface classes which exhibit visually barely perceptible color variations. In particular, our approach is based on relative (not absolute) color measurements. In this paper, we outline the classification algorithm while focusing in detail on the camera calibration and a method for compensating for fluctuations of the light source. Received: 1 September 1998 / Accepted: 16 March 2000