Dominant run-length method for image classification

In this paper, we develop a new run-length texture feature extraction algorithm that significantly improves image classification accuracy over traditional techniques. By directly using part or all of the run-length matrix as a feature vector, much of the texture information is preserved. This approach is made possible by the introduction of a new multi-level dominant eigenvector estimation algorithm. It reduces the computational complexity of the Karhunen-Loeve Transform by several orders of magnitude. Combined with the Bhattacharya distance measure, they form an efficient feature selection algorithm. The advantage of this approach is demonstrated experimentally by the classification of two independent texture data sets. Perfect classification is achieved on the first data set of eight Brodatz textures. The 97% classification accuracy on the second data set of sixteen Vistex images further confirms the effectiveness of the algorithm. Based on the observation that most texture information is contained in the first few columns of the run-length matrix, especially in the first column, we develop a new fast, parallel run-length matrix computation scheme. Comparisons with the co-occurrence and wavelet methods demonstrate that the run-length matrices contain great discriminatory information and that a method of extracting such information is of paramount importance to successful classification.Funding was provided by the Office of Naval Research through Contract No. N00014-93-1-0602

A non-linear decomposition for segmentation of noisy textures

This paper presents firstly the multi-size morphological decomposition (MMD) based on mathematical morphology, which can décompose a textured image into a set of component images according to the size and the gray level of texture primitives in the image . The MMD can give good results for supervised segmentation of textured images while the signal to noise ratio of the image to be segmented is near to that one of the reference images used for learning. Then, this decomposition is generalized by using the rank-order filter for fitting better to noisy textures . Experimental results show that the generalized decomposition is more robust to noises . It can improve the segmentation results when the signal to noise ratio of the image to be segmented is different from that of the reference images .Cet article présente d'abord la décomposition morphologique multi-tailles (DMM) fondée sur la morphologie mathématique, qui permet la décomposition d'une image texturelle en un ensemble d'images composantes suivant la taille et le niveau de gris des primitives texturelles dans l'image. La DMM peut conduire à de bons résultats en segmentation supervisée de textures bruitées lorsque le rapport signal à bruit de l'image à segmenter est proche de celui des images de référence utilisées pour l'apprentissage. Ensuite cette décomposition est généralisée par le filtre d'ordre afin de mieux l'adapter aux textures bruitées. Les résultats expérimentaux montrent que la décomposition généralisée s'avère en effet moins sensible au brui

Local spatial-frequency analysis of images using Wigner-Ville distribution

Local spectrum analysis is an interesting method to extract pertinent features of an image . This paper proposes a new local spectrum analysis method allowing to accurately characterize the local spatial frequency content of an image. It is based on the use of the two-dimensional Wigner-Ville distribution (2D WVD), which permits to separately control spatial and frequential analysis resolutions. The application of this method to texture feature extraction and discrimination is illustrated, and a comparison with the classical 2D spectrogram method is also given.L'analyse spectrale locale est une méthode intéressante pour obtenir des caractéristiques pertinentes d'une image. Cet article propose une nouvelle méthode d'analyse spectrale locale permettant de caractériser de manière précise les propriétés fréquentielles locales d'une image. Cette méthode est basée sur la transformation de Wigner-Ville bidimensionnelle (TWV 2D), qui permet de contrôler, de façon souple, séparement les résolutions spatiale et fréquentielle. L'application de cette technique à la caractérisation d'images de textures est illustrée, et une comparaison de sa performance par rapport à la méthode classique de spectrogramme 2D est également montré

Texture analysis of the radiographic trabecular bone pattern in osteoporosis

Texture is an image property which is difficult to grasp. It can be described as a "homogeneous visual pattern"l. but there exists no formal definition of texture. Intuitively people can discriminate between different textures. referring to visual clues like coarseness, orientation. periodicity, and regularity. Using such concepts, several authors have tried to quantify these aspects of texture'. However. texture encompasses more than these more or less random aspects to which the human eye is sensitive. Therefore, the majority of texture analysis algorithms is based on an image model. in which certain characteristics of the image texture are condensed. Using this image model, texture features can be derived, most of which cannot be related to visual image features. Texture analysis methods are able, in contrast to a human observer, to quantIfy textures objectively. Therefore. texture features can be used for the purpose of characterization, discrimination, and segmentation of textures in. for example, aerial and satellite imagery. Most texture analysis methods have been developed and tested on textures from the collection of texture images in Brodatz' before putting them mto use in a more realistic environment. Since the early seventies, texture analysis methods have also been applied In medical images. For example, Sulton et a!. tried to categorize different stages of pulmonary disease in radiographs4 Since then, the field of application of texture analysis methods in radiology has expanded from chest radiographs to mammograms and bone radiographs. The goal of our study is twofold: in the first place to assess the suitability of different texture analysis methods for usc in radiographs, secondly to select or develop texture features which are able to quantify the changes in the radiographic trabecular pattern occurring in osteoporosis. Osteoporosis is defined as "a disease characterized by low bone mass and microarchitectural changes of bone tissue, leading to enhanced bone fragility and a consequent increase in fracture risk." (WHO, 1994)

Automatic surface defect quantification in 3D

Three-dimensional (3D) non-contact optical methods for surface inspection are of significant interest to many industrial sectors. Many aspects of manufacturing processes have become fully automated resulting in high production volumes. However, this is not necessarily the case for surface defect inspection. Existing human visual analysis of surface defects is qualitative and subject to varying interpretation. Automated 3D non-contact analysis should provide a robust and systematic quantitative approach. However, different 3D optical measurement technologies use different physical principles, interact with surfaces and defects in diverse ways, leading to variation in measurement data. Instrument s native software processing of the data may be non-traceable in nature, leading to significant uncertainty about data quantisation. Sub-millimetric level surface defect artefacts have been created using Rockwell and Vickers hardness testing equipment on various substrates. Four different non-contact surface measurement instruments (Alicona InfiniteFocus G4, Zygo NewView 5000, GFM MikroCAD Lite and Heliotis H3) have been utilized to measure different defect artefacts. The four different 3D optical instruments are evaluated by calibrated step-height created using slipgauges and reference defect artefacts. The experimental results are compared to select the most suitable instrument capable of measuring surface defects in robust manner. This research has identified a need for an automatic tool to quantify surface defect and thus a mathematical solution has been implemented for automatic defect detection and quantification (depth, area and volume) in 3D. A simulated defect softgauge with a known geometry has been developed in order to verify the implemented algorithm and provide mathematical traceability. The implemented algorithm has been identified as a traceable, highly repeatable, and high speed solution to quantify surface defect in 3D. Various industrial components with suspicious features and solder joints on PCB are measured and quantified in order to demonstrate applicability

Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques

Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient's life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CI') and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FO) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CI' modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour OM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multi resolution approaches based on applying the FO as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters' energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician's ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease

Surface-based segmentation of volume data using texture features

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 117-123).by Edward H. Baik.M.Eng

Visual Speech Recognition

In recent years, Visual speech recognition has a more concentration, by researchers, than the past. Because of the leakage of the visual processing of the Arabic vocabularies recognition, we start to search in this field. Audio speech recognition concerned with the acoustic characteristic of the signal, but there are many situations that the audio signal is weak of not exist, and this will be a point in Chapter 2. The visual recognition process focuses on the features extracted from video of the speaker. These features are to be classified using several techniques. The most important feature to be extracted is motion. By segmenting motion of the lips of the speaker, an algorithm has manipulate it in such away to recognize the word which is said. But motion segmentation is not the only problem facing the speech recognition process, segmenting the lips itself is an early step in the speech recognition process, so, to segment lips motion we have to segment lips first, a new approach for lip segmentation is proposed in this thesis. Sometimes, motion feature needs another feature to support in recognition the spoken word. So in our thesis another new algorithm is proposed to use motion segmentation by using the Abstract Difference Image from an image series, supported by correlation for registering images in the image series, to recognize ten words in the Arabic language, the words are from “one” to “ten” in Arabic language. The algorithm also uses the HU-Invariant set of features to describe the Abstract Difference Image, and uses a three different recognition methods to recognize the words. The CLAHE method as a filtering technique is used by our algorithm to manipulate lighting problems. Our algorithm based on extracting the differences details from a series of images to recognize the word, achieved an overall results 55.8%, it is an adequate result for our algorithm when integrated in an audio-visual system

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