Texture Classification by Wavelet Packet Signatures

This correspondence introduces a new approach to characterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty-five natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) reflected a specific scale and orientation sensitivity. Wavelet packet representations for twenty-five natural textures were classified without error by a simple two-layer network classifier. An analyzing function of large regularity (D20) was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments (D6) In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classification without error for the twenty-five textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture

An efficient computational scheme for the two-dimensional overcomplete wavelet transform

2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Prostate cancer recognition in ultrasound images

Our purpose is to aid medical doctors in prostate cancer detection via computer automated analysis of prostatic ultrasound imagery. Absorption of ultrasound signals is different in cancerous areas than in non-cancerous areas. The energy of the signal, the continuity of the signal, the autocorrelation function and frequency domain properties of prostatic ultrasound images are different in normal tissue than in cancerous tissue; This thesis presents an algorithm for automated cancer recognition in prostatic ultrasound imagery. Statistical and morphological based models are employed to classify regions of ultrasound imagery as either cancerous or non-cancerous. Application of our algorithm onto a limited set of cancerous and non-cancerous ultrasound images shows that our method has the ability to recognize cancer in cancerous ultrasound images. Misclassification occurs when cancerous tissue is classified as non-cancerous and noncancerous tissue is classified as cancerous. Occurrences of misclassification have been observed and investigated. (Abstract shortened by UMI.)

Transform texture classification

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1996.Includes bibliographical references (leaves 155-163).by Xiaoou Tang.Ph.D

Face recognition using skin texture

In today's society where information technology is depended upon throughout homes, educational establishments and workplaces the challenge of identity management is ever growing. Advancements in image processing and biometric feature based identification have provided a means for computer software to accurately identify individuals from increasingly vast databases of users. In the quest to improve the performance of such systems in varying environmental conditions skin texture is here proposed as a biometric feature. This thesis presents and discusses a hypothesis for the use of facial skin texture regions taken from 2-dimensional photographs to accurately identify individuals using three classifiers (neural network, support vector machine and linear discriminant). Gabor wavelet filters are primarily used for feature extraction and arc supported in later chapters by the grey-level cooccurrence probability matrix (GLCP) to strengthen the system by providing supplementary high-frequency features. Various fusion techniques for combining these features are presented and their perfonnance is compared including both score and feature fusion and various permutations of each. Based on preliminary results from the BioSecure Multimodal Database (BMDB) , the work presented indicates that isolated texture regions of the human face taken from under the eye may provide sufficient information to discriminately identify an individual with an equal error rate (EER) of under 1% when operating in greyscale. An analysis of the performance of the algorithm against image resolution investigates the systems performance when faced with lower resolution training images and discusses optimal resolutions for classifier training. The system also shows a good degree of robustness when the probe image resolution is reduced indicating that the algorithm provides some level of scale invariance. Scope for future work is laid out and a review of the evaluation is also presented

Análisis de texturas mediante el histograma de frecuencias de elementos conexo

El Análisis de Texturas, y más concretamente, la segmentación de texturas es uno de los campos de mayor interés dentro de la Visión Artificial. La segmentación de una escena del mundo real es casi imposible llevarla a cabo sin poder segmentar los objetos texturizados que en ella se encuentran. Este proceso es más crítico cuando se pretende aplicar en sistemas de inspección automática en entornos industriales. Un error en esta fase se propagará en subsiguientes etapas y provocará una degeneración completa del sistema. Por este motivo, la inspección de productos industriales juega un papel muy importante en los procesos de producción, en los que cada vez más aumenta la demanda de calidad en un entorno de fuerte competitividad. El principal problema con el que nos enfrentamos en el Análisis de Texturas es el de encontrar la mejor representación de textura. Uno de los inconvenientes más significativos dentro de la caracterización e identificación de texturas es que la mayoría de sus descripciones son subjetivas y difusas. La mayor parte de las características utilizadas para su descripción dependen muy estrechamente del problema que se intenta resolver y desgraciadamente no existe un esquema estandarizado. Sobre esta problemática se centra la principal aportación de este trabajo. Hemos desarrollado un novedoso concepto al que hemos llamado: Histograma de Frecuencias de Elementos Conexos (HFEC). Se puede definir el HFEC de una imagen como una aproximación a la función de densidad de un suceso o evento aleatorio denominado “elemento conexo”. Este suceso no sólo representa la distribución de los niveles de gris de la textura, sino también la dependencia espacial que existe entre ellos. El trabajo se compone principalmente de tres partes. La primera parte del trabajo se centra en la descripción de esta novedosa herramienta, así como en el estudio de sus propiedades. Se presentan los parámetros que definen el HFEC: el Nivel de Conectividad y el Parámetro Morfológico. Mediante un estudio de sensibilidad de estos parámetros se demuestra como el Histograma de niveles de gris de una imagen es un caso particular de la configuración de un HFEC. En una segunda parte se presenta una arquitectura para el diseño de Sistemas de Reconocimiento Automático de Formas basados en la representación de un HFEC. Dicha arquitectura se divide en tres fases: (1) Extracción de características, (2) Selección de características y (3) Clasificación. En la primera fase, como su nombre indica, se extraen las características del HFEC que entran en juego en nuestro problema de reconocimiento y cuyo fin es la conversión del HFEC en características que representen, idealmente, la información condensada y más importante de un HFEC dado. En esta fase, además de las herramientas clásicas como la transformada de Fourier, se ha utilizado una herramienta matemática cuyo uso está siendo bastante extenso y exitoso en sistemas de reconocimientos de formas como es la transformada Wavelet. El objetivo principal de un HFEC es el de caracterizar la región de una textura para su posterior clasificación. El HFEC de una textura natural es una función no estacionaria y, por lo tanto, es deseable para su estudio poder trabajar con una representación espacio / escalar (frecuencia) de la forma que nos lo permite el análisis Wavelet. En la fase de selección de características se identifican el menor número de características que mejor identifican un HFEC dado con la mínima redundancia posible. Para ello se ha implementado un procedimiento estadístico que tiene en cuenta la información de dispersión entre/intra clases a la hora de seleccionar una característica. En la tercera y última fase se asigna una categoría de textura a un HFEC específico de acuerdo con las características seleccionadas en la etapa anterior. Hemos elegido como clasificador un tipo de redes neuronales como el modelo de perceptrón multicapa con propagación hacia delante (feedforward neural network), asociado al algoritmo de ajuste denominado Retropropagación del Gradiente (Back-Propagation). Por último, se presenta la aplicación de esta arquitectura y el HFEC a un sistema real de inspección automática de madera. El problema que se pretende resolver es el de la detección de defectos en objetos de madera. La inspección de estos objetos se realiza bajo condiciones de un entorno industrial y existe una gran variabilidad en la apariencia de los mismos. Parte del desarrollo expuesto es fruto del trabajo de investigación realizado durante más de seis años en un proyecto de colaboración entre el Departamento de Inteligencia Artificial de la Facultad de Informática de la Universidad Politécnica de Madrid y una empresa privada (por motivos de confidencialidad evitamos el nombre). A la hora de escribir estas líneas existen seis plantas industriales (tres en España, dos en Francia y una en Portugal) utilizando el sistema de inspección automática desarrollado en este proyecto. Textures Analysis, or more precisely, texture segmentation, is one of the most critical issues in computer vision. There are virtually no scenes taken from the real world that can be analyzed automatically without segmenting the texture elements present in the corresponding digital images. Focusing on the automatic quality inspection of industrial products, the results attained in the segmentation phase are critical for the performance of the whole computer-based vision system. On the other hand, because of ever-increasing competitiveness in business and industry, quality inspection has become a key issue in any production process. The main problem that we face ourselves in Textures Analysis is finding the best texture representation. One of the most significant drawbacks within texture characterization and identification is that most of the descriptions are subjective and vague. Although the description, selection and classification of texture features are crucial for automatic object inspection, there is, unfortunately, no universally accepted standard for such a strategic endeavor. Most of the texture features used in practice depend very closely on the application domain and, furthermore, there is a disturbing lack of agreement even in the terminology used by the different authors. For any domain-specific application, the problem of selecting the best set of texture features, aside from being crucial, is more an art than a science. The main contribution of this thesis centers on these problems. We have developed a novel concept that we have called: Frequency Histogram of Connected Elements (FHCE). The FHCE can be defined as an approximation to the probability density function of a random event called “Connected Element”. The FHCE represents the frequency of occurrence of a random event, which not only describes the texture’s gray-level distribution, but also the existing spatial dependence within the texture. The thesis is composed of three parts. The first part centers on the description of this novel concept, as well as on the study of its main properties. The parameters that define the so-called “Connected Element” are introduced: the Connectivity Level and the Morphology Parameter. A sensibility study of these parameters shows that the gray-level histogram of a digital image is a particular case of the FHCE. The second part introduces an architecture for the design of Automatic Recognition Systems based on the representation of an FHCE. This architecture is divided into three phases: (1) characteristics extraction, (2) characteristics selection and (3) classification. In the first phase, the characteristics of the FHCE that come into play in our recognition problem are extracted. The purpose is to convert the FHCE into characteristics that represent the condensed information of a given FHCE. In this phase, in addition to classic tools, such as the Fourier transform, we used a widely used and highly successful mathematical tool for recognition systems: the Wavelet transform. The main objective of an FHCE is to characterize the region of a texture for its subsequent classification. The FHCE of a natural texture is a non-stationary function and, therefore, it is desirable for its study to be able to work with a space/scalar (frequency) representation, as the Wavelet analysis does. In the characteristics selection phase, the smallest number of characteristics that best identify an FHCE, with the minimum possible redundancy, are identified. For this reason, a statistical procedure was implemented that takes into account the dispersion information between/within classes in order to select a characteristic. In the third and last phase, the FHCE is assigned to a texture category according to the characteristics selected in the previous stage. A feed-forward multilayer perceptron, trained with the back-propagation algorithm, is the specific ANN classifier applied for the detection and recognition of textures in digital images. Finally, this architecture and the FHCE are applied to a real automatic wood inspection system. The problem that is to be solved is the detection of defects in wooden objects. These objects are inspected under industrial environment conditions. Part of the development presented is the result of investigative work carried out over more than six years in a collaborative project between the Departamento de Inteligencia Artificial of the Facultad de Informática of the Universidad Politécnica de Madrid and a private company (to remain unnamed for confidentiality reasons). At the time of this writing, there are six industrial plants (three in Spain, two in France and one in Portugal) using the automatic inspection system developed in this project

New wavelet based space-frequency analysis methods applied to the characterisation of 3-dimensional engineering surface textures.

The aim of this work was to use resources coming from the field of signal and image processing to make progress solving real problems of surface texture characterisation. A measurement apparatus like a microscope gives a representation of a surface textures that can be seen as an image. This is actually an image representing the relief of the surface texture. From the image processing point of view, this problem takes the form of texture analysis. The introduction of the problem as one of texture analysis is presented as well as the proposed solution: a wavelet based method for texture characterisation. Actually, more than a simple wavelet transform, an entire original characterisation method is described. A new tool based on the frequency normalisation of the well-known wavelet transform has been designed for the purpose of this study and is introduced, explained and illustrated in this thesis. This tool allows the drawing of a real space-frequency map of any image and especially textured images. From this representation, which can be compared to music notation, simple parameters are calculated. They give information about texture features on several scales and can be compared to hybrid parameters commonly used in surface roughness characterisation. Finally, these parameters are used to feed a decision-making system. In order to come back to the first motivation of the study, this analysis strategy is applied to real engineered surface characterisation problems. The first application is the discrimination of surface textures, which superficially have similar characteristics according to some standard parameters. The second application is the monitoring of a grinding process. A new approach to the problem of surface texture analysis is introduced. The principle of this new approach, well known in image processing, is not to give an absolute measure of the characteristics of a surface, but to classify textures relative to each other in a space where the distance between them indicates their similarity