Automated image inspection using wavelet decomposition and fuzzy rule-based classifier

A general purpose image inspecting system has been developed for automatic flaw detection in industrial applications. The system has a general purpose image understanding architecture that performs local feature extraction and supervised classification. Local features of an image are extracted from the compactly supported wavelet transform of the image. The features extracted from the wavelet transform provide local harmonic analysis and multi-resolution representation of the image. Image segmentation is achieved by classifying image pixels based on features extracted within a local area near each pixel. The supervised classifier used in the segmentation process is a fuzzy rule-based classifier which is established from the training data. The fuzzy rule base that is used to control the performance of the classifier is optimized by combining similar training data into the same rule. Therefore an optimization is achieved for the established rule base to provide the maximum amount of information with the minimum amount of rules. The experimental results show that the features extracted from the wavelet decomposition give contextual information for the test images. The optimized fuzzy rule-based classifier gives the best performance in both the training and the classification stages. Flaws in the test images are detected automatically by the computer

2D partially occluded object recognition using curve moment invariants

Master'sMASTER OF ENGINEERIN

Analysis of the image moments sensitivity for the application in pattern recognition problems

Momenti slike su numerički deskriptori koji sadrže informaciju o svojstvima invarijantnim na translaciju, rotaciju, promjenu skale i neke oblike distorzije, a njihova analiza je jedna od metoda koje se često koriste pri analizi slika i raspoznavanju uzoraka. U okviru ove radnje razvijeni su algoritmi za računanje geometrijskih, Legendreovih, Zernikeovih, Fourier – Mellinovih te tri tipa Fourier – Jacobijevih momenata, kao i iz njih definiranih invarijanti slike u programskom jeziku MatLab uz rješavanje inverznog problema rekonstrukcije početnog ulaza. Za sve tipove momenata osim najjednostavnijih geometrijskih definirani su vektori osjetljivosti na rotaciju i promjenu skale čije su komponente oni članovi skupa koji nose značajnije informacije o ulaznoj slici. Primjenom novih deskriptora na klasifikaciju rukom pisanih slova i identifikacijskih fotografija osoba pokazano je da je relevantna informacija o ulazu na taj način sačuvana, a njihov je izračun znatno brži i jednostavniji uz zadržanu sposobnost jednoznačnog raspoznavanja uzoraka. Korištenjem momenata slike i vektora osjetljivosti analizirani su znakovi s dvaju glagoljskih spomenika te utvrđeno postojanje mješavine znakova trokutastog i okruglog modela glagoljice. Metoda je primijenjena i na klasifikaciju tragova puzanja ličinki mutanata vinske mušice za potrebe proučavanja odgovora živčanog sustava na različite podražaje.Image moments are numerical descriptors invariant to translation, rotation, change of scale and some types of image distortion and their analysis is one of the most often used methods in image processing and pattern recognition. In this work, algorithms for calculation of geometric, Legendre, Zernike, Fourier – Mellin and three types of Fourier – Jacobi moments were implemented in MatLab. Hu's, affine and blur invariants were also obtained as well as inverse problem of input image reconstruction solved. For each type of image moments exept geometric ones the set of sensitivity vectors for rotation and scale were defined. Their components are those image moments which describe more important features of the input image. These new descriptors were applied for classification of handwritten letters and identifying personal photos. It was shown that the process of such descriptor calculation is much faster and simpler while preserving all the relevant information about input image. Using this method, the signs carved in two glagolitic inscriptions were analyzed and the mixture of triangular and round glagolitic letters found. The method was also applied to classification of the mutant fruit fly larvae crawling trails which is needed in studying responses of the nervous system to different stimuli

Analysis of the image moments sensitivity for the application in pattern recognition problems

Momenti slike su numerički deskriptori koji sadrže informaciju o svojstvima invarijantnim na translaciju, rotaciju, promjenu skale i neke oblike distorzije, a njihova analiza je jedna od metoda koje se često koriste pri analizi slika i raspoznavanju uzoraka. U okviru ove radnje razvijeni su algoritmi za računanje geometrijskih, Legendreovih, Zernikeovih, Fourier – Mellinovih te tri tipa Fourier – Jacobijevih momenata, kao i iz njih definiranih invarijanti slike u programskom jeziku MatLab uz rješavanje inverznog problema rekonstrukcije početnog ulaza. Za sve tipove momenata osim najjednostavnijih geometrijskih definirani su vektori osjetljivosti na rotaciju i promjenu skale čije su komponente oni članovi skupa koji nose značajnije informacije o ulaznoj slici. Primjenom novih deskriptora na klasifikaciju rukom pisanih slova i identifikacijskih fotografija osoba pokazano je da je relevantna informacija o ulazu na taj način sačuvana, a njihov je izračun znatno brži i jednostavniji uz zadržanu sposobnost jednoznačnog raspoznavanja uzoraka. Korištenjem momenata slike i vektora osjetljivosti analizirani su znakovi s dvaju glagoljskih spomenika te utvrđeno postojanje mješavine znakova trokutastog i okruglog modela glagoljice. Metoda je primijenjena i na klasifikaciju tragova puzanja ličinki mutanata vinske mušice za potrebe proučavanja odgovora živčanog sustava na različite podražaje.Image moments are numerical descriptors invariant to translation, rotation, change of scale and some types of image distortion and their analysis is one of the most often used methods in image processing and pattern recognition. In this work, algorithms for calculation of geometric, Legendre, Zernike, Fourier – Mellin and three types of Fourier – Jacobi moments were implemented in MatLab. Hu's, affine and blur invariants were also obtained as well as inverse problem of input image reconstruction solved. For each type of image moments exept geometric ones the set of sensitivity vectors for rotation and scale were defined. Their components are those image moments which describe more important features of the input image. These new descriptors were applied for classification of handwritten letters and identifying personal photos. It was shown that the process of such descriptor calculation is much faster and simpler while preserving all the relevant information about input image. Using this method, the signs carved in two glagolitic inscriptions were analyzed and the mixture of triangular and round glagolitic letters found. The method was also applied to classification of the mutant fruit fly larvae crawling trails which is needed in studying responses of the nervous system to different stimuli

Biological object representation for identification

This thesis is concerned with the problem of how to represent a biological object for computerised identification. Images of biological objects have been generally characterised by shapes and colour patterns in the biology domain and the pattern recognition domain. Thus, it is necessary to represent the biological object using descriptors for the shape and the colour pattern. The basic requirements which a description method should satisfy are those such as invariance of scale, location and orientation of an object; direct involvement in the identification stage; easy assessment of results. The major task to deal with in this thesis was to develop a shape-description method and a colour-pattern description method which could accommodate all of the basic requirements and could be generally applied in both domains. In the colour-pattern description stage, an important task was to segment a colour image into meaningful segments. The most efficient method for this task is to apply Cluster Analysis. In the image analysis and pattern recognition domains, the majority of approaches to this method have been constrained by the problem of dealing with inordinate amounts of data, i.e. a large number of pixels of an image. In order to directly apply Cluster Analysis to the colour image segmentation, data structure, the Auxiliary Means is developed in this thesis

Automatic recognition and inspection of two-dimensional manufactured components

This thesis presents new developments in the field of recognition and inspection of 2D manufactured components. It discusses the problem of recognition and inspection of such components, which may be either flawed or partially completed. Several new methods are proposed that are designed to be used in the solution of this problem. These methods may be divided into two categories. The first involves the component of interest being processed via a suitable feature extraction scheme. This scheme makes measurements of local geometric features of the component which are, by nature, invariant of the component’s position, orientation and scale. These features are known as local features of the component, because they are calculated for only a portion of the area or outline of the entire component. Global features, which are extracted from the whole outline, are not immediately useful because the contribution of acceptable or unacceptable variations, spurious additions and omissions are all arbitrarily combined together, that is, smoothed over. An algorithm is then used to compare the features extracted from the component with the same type of features extracted from its reference component. Each individual geometric entity of the component may be identified after using this process. The second category concerns itself with the replacement of measured point data, derived from the outline of the component, with substitute geometric entities, such as straight lines and circular arcs. This replacement is necessary because measured point data does not describe a manufactured component in the same way as that of the design specification. Only when such a substitution takes place can a spatial comparison between corresponding individual entities be performed, based on the design specifications. In addition, the relationship between the most widely used invariant moments, and Fourier descriptors, is investigated. Fourier Analysis is often used in image processing and Fourier descriptors are often readily available so, for this reason, it is useful to compute invariant moments by using Fourier descriptors. This thesis is organized as follows: Chapter 1 outlines previous research in this field, the need for current research, and the scope of this work. Chapter 2 is devoted to the new subpolygon method. This method is developed for recognition and inspection of relatively simple manufactured components. Chapter 3 proposes the new line-moment method of feature extraction, which is designed for the more complex manufactured components which may be less conveniently examined by the using the subpolygon method. The simplicity and effectiveness, as well as the applications, of line moments are also demonstrated. In addition, the algorithm designed for matching this type of feature with geometric entities is described. Chapter 4 briefly reviews the method of extracting a component’s global features by applying a Fourier Analysis. Since Fourier descriptors and moment invariants are two important types of extracted invariant features, the major concern of this chapter is the development of a mathematical relationship between the two. Several examples involving the use of this method are included later in the chapter. Chapter 5 proposes a novel algorithm for generating substitute geometries, such as lines and arcs, from measured sample point data, such as digitises or pixels. It enables a final comparison between the geometries of a component based on its design specifications. Errors due to the substitution are then minimised. and the deviations between the substitute geometry and the measured sample points may then be calculated. Chapter 6 concludes the thesis and recommends possible further research