Technical appendix to locally stationary wavelet fields with application to the modelling and analysis of image texture.

This report is an appendix to the paper Locally stationary wavelet fields with application to the modelling and analysis of image texture, providing proofs for all the major results

Image Texture Representation and Detection

Tato bakalářská práce se zabývá metodami klasifikace textur a implementuje jednoduchý klasifikátor textur, umožňující generování dynamické banky filtrů dle aktuální sady textur. Pro získání příznaků jsou zvoleny Gáborovy filtry společně s Hu momenty. Jako klasifikátor je použit algoritmus SVM.This bachelor's thesis presents methods used for texture classification and implements programme which is able to realize simple classification and which is capable of generating dynamic bank of filters according to actual texture library. For feature extraction is used combination of Gabor filters with Hu-moments. As classifier there is used algorithm SVM.

Texture Classification Based on Complex Network Model with Spatial Information

This paper proposes a method for image texture classification based on a complex network model. Finding relevant and valuable information in an image texture is an essential issue for image classification and remains a challenge. Recently, a complex network model has been used for texture analysis and classification. However, with current analysis methods, important empirical properties of image texture such as spatial information are discarded from consideration. Accordingly, we propose local spatial pattern mapping (LSPM) method for manipulating the spatial information in an image texture with multi-radial distance analysis to capture the texture pattern. In experiments, the feature properties under the traditional complex network model and those with the proposed method are analyzed by using the Brodatz, UIUC, and Outex databases. As results, the proposed method is shown to be effective for texture classification, providing an improved classification rate as compared to the traditional complex network model

Quantification of image texture in X-ray phase-contrast-enhanced projection images of in vivo mouse lungs observed at varied inflation pressures

To date, there are very limited noninvasive, regional assays of in vivo lung microstructure near the alveolar level. It has been suggested that x-ray phase-contrast enhanced imaging reveals information about the air volume of the lung; however, the image texture information in these images remains underutilized. Projection images of in vivo mouse lungs were acquired via a tabletop, propagation-based, X-ray phase-contrast imaging system. Anesthetized mice were mechanically ventilated in an upright position. Consistent with previously published studies, a distinct image texture was observed uniquely within lung regions. Lung regions were automatically identified using supervised machine learning applied to summary measures of the image texture data. It was found that an unsupervised clustering within predefined lung regions colocates with expected differences in anatomy along the cranial-caudal axis in upright mice. It was also found that specifically selected inflation pressures-here, a purposeful surrogate of distinct states of mechanical expansion-can be predicted from the lung image texture alone, that the prediction model itself varies from apex to base and that prediction is accurate regardless of overlap with nonpulmonary structures such as the ribs, mediastinum, and heart. Cross-validation analysis indicated low inter-animal variation in the image texture classifications. Together, these results suggest that the image texture observed in a single X-ray phase-contrast-enhanced projection image could be used across a range of pressure states to study regional variations in regional lung function

Investigation of LANDSAT imagery on correlations between ore deposits and major shield structures in Finland

The author has identified the following significant results. On the central Baltic Shield, the concept of drainage patterns can be extended to smaller scales in which case many cultural features become involved to the spatial patterns influenced by bedrock structure. Features resulting from agriculture activity and timbering often exaggerate the influence of the bedrock on the image texture

Some Numerical Characteristics of Image Texture

Texture classification is one of the basic images processing tasks. In this paper we present some numerical characteristics to the images analysis and processing. It can be used at the solving of images classification problems, their recognition, problems of remote sounding, biomedical images analysis, geological researches