In this paper a system for web surface inspection is described.
It has three parts: an image acquisition part, a defect detection
part, and a defect classification part. The self-organizing maps
(SOMs) are used both in defect detection and in defect classification
which makes the system adaptable to different types of surfaces and
defects. Our main focus is on defect classification where a generic
content-based image retrieval (CBIR) system called PicSOM is
utilized. The PicSOM uses tree-structured SOMs (TS-SOMs) and
relevance feedback. It is trained with the feature sets of the
defects in the database. For defect description, features from the
MPEG-7 standard (the homogeneous texture, the color structure, and
the edge histogram) are used and for the shape description our own
shape feature set is applied. Results indicate that the system works
with a high level of success.
In this paper a prototype system is described for the management and content-based retrieval of defect images in huge image databases. This is a real problem in surface inspection applications, since modern inspection systems may produce up to thousands of defect images in a day. We are using a noncommercial, generic content-based image retrieval (CBIR) system called PicSOM that is modified to fit to the special requirements of our application. The system is tested with a small pre-classified database of surface defect images using the MPEG-7 features. The scalability of the system is also examined using a larger database. Results indicate that the system works with a high level of success.
In industrial inspection one of the key areas is detection of defects from textured surfaces. The goal is to differentiate between a good, normal surface texture and a defected surface texture. In this paper this is achieved with a two-class classifier that is taught only with fault-free samples of surface texture. An unsupervised segmentation scheme is formulated where an unknown sample is classified as a defect if it differs enough from the estimated distribution of texture features extracted from fault-free samples. The extension of the self-organizing map (SOM) algorithm, the so-called statistical SOM, is used to estimate the distribution. Different versions of the statistical SOM are introduced and their computational requirements are discussed. The proposed methods are shown to perform well in segmentation of texture surface images with different kinds of defects.
In this paper the performance of two histogram-based texture analysis techniques for surface defect detection is evaluated. These techniques are the co-occurrence matrix method and the local binary pattern method. Both methods yield a set of texture features that are computed form a small image window. The unsupervised segmentation procedure is used in the experiments. It is based on the statistical self-organizing map algorithm that is trained only with fault-free surface samples. Results of experiments with both feature sets are good and there is no clear difference in their performances. The differences are found in their computational requirements where the features of the local binary pattern method are better in several aspects.
In this paper a special type of image segmentation, a two- class segmentation, is considered. Defect detection in quality control applications is a typical two-class problem. The main idea in this paper is to train the two-class classifier with fault-free samples that is an unexpected approach. The reason is that defects are rare and expensive. The proposed defect detection is based on the following idea: an unknown sample is classified as a defect if it differs enough from the estimated prototypes of fault-free samples. The self-organizing map is used to estimate these prototypes. Surface images are used to demonstrate the proposed image segmentation procedure.
A surface inspection problem is divided into three parts, into an image acquisition part, into a defect detection part that is suitable for hardware implementation, and into a defect classification part that is done in a user's terminal. In the defect detection part extraction of texture features is done and potential defect areas are marked. The proposed scheme is taught only with examples of fault-free surface. In the defect classification part features describing the shape and internal structure of defects are extracted and defects are classified into different defect classes. Examples of defects are used to train the classification system. Use of the self-organizing map in defect detection and in defect classification makes the proposed method adaptable to different types of surfaces and to different types of defects. Only reselection of features may be necessary to cope with different surface and defect characteristics. The results of experiments with base paper samples are encouraging.
A new approach to object recognition is proposed. The main concern is on irregular objects which are hard to recognize even for a human. The recognition is based on the contour of an object. The contour is obtained with morphological operators and described with a Freeman chain code. The chain code histogram (CCH) is calculated from the chain code of the contour of an object. For an eight-connected chain code an eight dimensional histogram, which shows the probability of each direction, is obtained. The CCH is a translation and scale invariant shape measure. The CCH gibes only an approximation of the object's shape so that similar objects can be grouped together. The discriminatory power of the CCH is demonstrated on machine-printed text and on true irregular objects. In both cases it is noted that similar objects are grouped together. The results of experiments are good. It has been shown that similar objects are grouped together with the proposed method. However, the sensitivity to small rotations limits the generality of the method.
A new operational system to interpret satellite images is represented. The described method is adaptive. It is trained by examples. In the reported application a combination of textural and spectral measures is used as a feature vector. The adaptation or learning of the extracted feature vectors occurs by a self-organizing process. As a result a topological feature map is generated. The map is identified by known samples, examples of clouds. The map is used later on as a code book for cloud classification. The obtained verification results are good. The represented method is general in the sense that by reselecting features it can be applied to new problems.