Texture representation using wavelet filterbanks

Texture analysis is a fundamental issue in image analysis and computer vision. While considerable research has been carried out in the texture analysis domain, problems relating to texture representation have been addressed only partially and active research is continuing. The vast majority of algorithms for texture analysis make either an explicit or implicit assumption that all images are captured under the same measurement conditions, such as orientation and illumination. These assumptions are often unrealistic in many practical applications;This dissertation addresses the viewpoint-invariance problem in texture classification by introducing a rotated wavelet filterbank. The proposed filterbank, in conjunction with a standard wavelet filterbank, provides better freedom of orientation tuning for texture analysis. This allows one to obtain texture features that are invariant with respect to texture rotation and linear grayscale transformation. In this study, energy estimates of channel outputs that are commonly used as texture features in texture classification are transformed into a set of viewpoint-invariant features. Texture properties that have a physical connection with human perception are taken into account in the transformation of the energy estimates;Experiments using natural texture image sets that have been used for evaluating other successful approaches were conducted in order to facilitate comparison. We observe that the proposed feature set outperformed methods proposed by others in the past. A channel selection method is also proposed to minimize the computational complexity and improve performance in a texture segmentation algorithm. Results demonstrating the validity of the approach are presented using experimental ultrasound tendon images

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