Genetic learning based texture surface inspection

This paper presents a novel approach of visual inspection for texture surface defects. It is based on the measure of texture energy acquired by a kind if high performance 2D detection mask, which is learned by genetic algorithms. Experimental results of texture defect inspection on textile images are presented to illustrate the merit and feasibility of the proposed method.<br /

Tracking system using texture cue based on wavelet transform

This paper presents an approach for tracking objects whose principal discriminate characteristic is its texture. The presented system extracts texture features based on the wavelet transform and uses a fuzzy grammar classifier. The feature vector consists of 6 characteristics extracted from the wavelet detail images. The overall system was integrated on the platform developed by sony – AIBO robot. This application ensures a real time tracking approach and can be parameterized in order to be flexible in face of different types of texture

Wavelet-based texture segmentation of titanium based alloy lamellar microstructure: application to images from optical microscope and X-ray microtomography

A texture segmentation algorithm which combines grey level intensity from 2 images after discrete wavelet transform and variance has been applied on 2D images to segment lamellar colonies in (α+β) titanium alloy Ti6A14V. Images were acquired using both optical microscope and X-ray tomography. The results are satisfying for the former technique and encouraging for the latter one. Possible extension of the method to volumetric data is presented

Texture cue based tracking system using wavelet transform and a fuzzy grammar

This paper addresses a system for fast object tracking based on texture cues, by using the wavelet transform and a fuzzy grammar classifier. The method is based on wavelet type features. The feature vector consists of 6 characteristics extracted from the wavelet detail images for each colour component. These texture characteristics automatically generate a fuzzy rule using a fuzzy inference classifier based on a fuzzy grammar. A learning phase is required for each texture but only uses one sample. This 2D tracking system of textured objects in image sequences is demonstrated on a robotic application using the platform developed by Sony – AIBO robot. The application ensures a real time tracking approach and can be parameterized in order to be flexible in face of different types of textures

Unsupervised texture segmentation of images using tuned matched Gabor filters

In this correspondence, we propose a novel method for efficient image analysis that uses tuned matched Gabor filters. The algorithmic determination of the parameters of the Gabor filters is based on the analysis of spectral feature contrasts obtained from iterative computation of pyramidal Gabor transforms with progressive dyadic decrease of elementary cell sizes. The method requires no a priori knowledge of the analyzed image so that the analysis is unsupervised. Computer simulations applied to different classes of texture illustrate the matching property of the tuned Gabor filters derived using our determination algorithm

Combining multiple Iris matchers using advanced fusion techniques to enhance Iris matching performance

M.Phil. (Electrical And Electronic Engineering)The enormous increase in technology advancement and the need to secure information e ectively has led to the development and implementation of iris image acquisition technologies for automated iris recognition systems. The iris biometric is gaining popularity and is becoming a reliable and a robust modality for future biometric security. Its wide application can be extended to biometric security areas such as national ID cards, banking systems such as ATM, e-commerce, biometric passports but not applicable in forensic investigations. Iris recognition has gained valuable attention in biometric research due to the uniqueness of its textures and its high recognition rates when employed on high biometric security areas. Identity veri cation for individuals becomes a challenging task when it has to be automated with a high accuracy and robustness against spoo ng attacks and repudiation. Current recognition systems are highly a ected by noise as a result of segmentation failure, and this noise factors increase the biometric error rates such as; the FAR and the FRR. This dissertation reports an investigation of score level fusion methods which can be used to enhance iris matching performance. The fusion methods implemented in this project includes, simple sum rule, weighted sum rule fusion, minimum score and an adaptive weighted sum rule. The proposed approach uses an adaptive fusion which maps feature quality scores with the matcher. The fused scores were generated from four various iris matchers namely; the NHD matcher, the WED matcher, the WHD matcher and the POC matcher. To ensure homogeneity of matching scores before fusion, raw scores were normalized using the tanh-estimators method, because it is e cient and robust against outliers. The results were tested against two publicly available databases; namely, CASIA and UBIRIS using two statistical and biometric system measurements namely the AUC and the EER. The results of these two measures gives the AUC = 99:36% for CASIA left images, the AUC = 99:18% for CASIA right images, the AUC = 99:59% for UBIRIS database and the Equal Error Rate (EER) of 0.041 for CASIA left images, the EER = 0:087 for CASIA right images and with the EER = 0:038 for UBIRIS images

Study of degradation processes in engineering materials using X-ray (micro)tomography and dedicated volumetric image processing and analysis.

This dissertation presents new experimental and computing methodologies to study different degradation processes in engineering materials. This has been done thanks to the unique use of X-ray tomography and the development of new image processing and analysis strategies.Niniejsza rozprawa habilitacyjna przedstawia nowe metody eksperymentalne i obliczeniowe stosowane do badania stopnia degradacji materiałów konstrukcyjnych. Zadania te zostały zrealizowane przez zastosowanie tomografii rentgenowskiej i nowych algorytmów analizy i przetwarzania obrazów

Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification

This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.Esta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes