Adaptive Median Binary Patterns for Texture Classification

Abstract-This paper addresses the challenging problem of recognition and classification of textured surfaces under illumination variation, geometric transformations and noisy sensor measurements. We propose a new texture operator, Adaptive Median Binary Patterns (AMBP) that extends our previous Median Binary Patterns (MBP) texture feature. The principal idea of AMBP is to hash small local image patches into a binary pattern texton by fusing MBP and Local Binary Patterns (LBP) operators combined with using self-adaptive analysis window sizes to better capture invariant microstructure information while providing robustness to noise. The AMBP scheme is shown to be an effective mechanism for non-parametric learning of spatially varying image texture statistics. The local distribution of rotation invariant and uniform binary pattern subsets extended with more global joint information are used as the descriptors for robust texture classification. The AMBP is shown to outperform recent binary pattern and filtering-based texture analysis methods on two large texture corpora (CUReT and KTH TIPS2-b) with and without additive noise. The AMBP method is slightly superior to the best techniques in the noiseless case but significantly outperforms other methods in the presence of impulse noise

Joint Adaptive Median Binary Patterns for texture classification

a b s t r a c t This paper addresses the challenging problem of the recognition and classification of textured surfaces given a single instance acquired under unknown pose, scale and illumination conditions. We propose a novel texture descriptor, the Adaptive Median Binary Pattern (AMBP) based on an adaptive analysis window of local patterns. The principal idea of the AMBP is to convert a small local image patch to a binary pattern using adaptive threshold selection that switches between the central pixel value as used in the Local Binary Pattern (LBP) and the median as in Median Binary Pattern (MBP), but within a variable sized analysis window depending on the local microstructure of the texture. The variability of the local adaptive window is included as joint information to increase the discriminative properties. A new multiscale scheme is also proposed in this paper to handle the texture resolution problem. AMBP is evaluated in relation to other recent binary pattern techniques and many other texture analysis methods on three large texture corpora with and without noise added, CUReT, Outex_TC00012 and KTH_TIPS2. Generally, the proposed method performs better than the best state-of-the-art techniques in the noiseless case and significantly outperforms all of them in the presence of impulse noise

Mathematical models for perceived roughness of three-dimensional surface textures

This thesis reports and discusses results from a new methodology for investigating the visually perceived properties of surfaces; by doing so, it also discovers a measurement or estimator for perceived roughness of 1/Fβ noise surfaces. Advanced computer graphics were used to model natural looking surfaces (1/Fβ noise surfaces). These were generated and animated in real-time to enable observers to manipulate dynamically the parameters of the rendered surfaces. A method of adjustment was then employed to investigate the effects of changing the parameters on perceived roughness. From psychophysical experiments, it was found that the two most important parameters related to perceived roughness were the magnitude roll-off factor (β) and RMS height (σ) for this kind of surfaces. From the results of various extra experiments, an estimation method for perceived roughness was developed; this was inspired by common frequency-channel models. The final optimized model or estimator for perceived roughness in 1/Fβ noise surfaces found was based on a FRF model. In this estimator, the first filter has a shape similar to a gaussian function and the RF part is a simple variance estimator. By comparing the results of the estimator with the observed data, it is possible to conclude that the estimator accurately represents perceived roughness for 1/Fβ noise surfaces

Study on Co-occurrence-based Image Feature Analysis and Texture Recognition Employing Diagonal-Crisscross Local Binary Pattern

In this thesis, we focus on several important fields on real-world image texture analysis and recognition. We survey various important features that are suitable for texture analysis. Apart from the issue of variety of features, different types of texture datasets are also discussed in-depth. There is no thorough work covering the important databases and analyzing them in various viewpoints. We persuasively categorize texture databases ? based on many references. In this survey, we put a categorization to split these texture datasets into few basic groups and later put related datasets. Next, we exhaustively analyze eleven second-order statistical features or cues based on co-occurrence matrices to understand image texture surface. These features are exploited to analyze properties of image texture. The features are also categorized based on their angular orientations and their applicability. Finally, we propose a method called diagonal-crisscross local binary pattern (DCLBP) for texture recognition. We also propose two other extensions of the local binary pattern. Compare to the local binary pattern and few other extensions, we achieve that our proposed method performs satisfactorily well in two very challenging benchmark datasets, called the KTH-TIPS (Textures under varying Illumination, Pose and Scale) database, and the USC-SIPI (University of Southern California ? Signal and Image Processing Institute) Rotations Texture dataset.九州工業大学博士学位論文 学位記番号:工博甲第354号　学位授与年月日:平成25年9月27日CHAPTER 1 INTRODUCTION|CHAPTER 2 FEATURES FOR TEXTURE ANALYSIS|CHAPTER 3 IN-DEPTH ANALYSIS OF TEXTURE DATABASES|CHAPTER 4 ANALYSIS OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 5 CATEGORIZATION OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 6 TEXTURE RECOGNITION BASED ON DIAGONAL-CRISSCROSS LOCAL BINARY PATTERN|CHAPTER 7 CONCLUSIONS AND FUTURE WORK九州工業大学平成25年

Acquisition and modeling of material appearance

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 131-143).In computer graphics, the realistic rendering of synthetic scenes requires a precise description of surface geometry, lighting, and material appearance. While 3D geometry scanning and modeling have advanced significantly in recent years, measurement and modeling of accurate material appearance have remained critical challenges. Analytical models are the main tools to describe material appearance in most current applications. They provide compact and smooth approximations to real materials but lack the expressiveness to represent complex materials. Data-driven approaches based on exhaustive measurements are fully general but the measurement process is difficult and the storage requirement is very high. In this thesis, we propose the use of hybrid representations that are more compact and easier to acquire than exhaustive measurement, while preserving much generality of a data-driven approach. To represent complex bidirectional reflectance distribution functions (BRDFs), we present a new method to estimate a general microfacet distribution from measured data. We show that this representation is able to reproduce complex materials that are impossible to model with purely analytical models.(cont.) We also propose a new method that significantly reduces measurement cost and time of the bidirectional texture function (BTF) through a statistical characterization of texture appearance. Our reconstruction method combines naturally aligned images and alignment-insensitive statistics to produce visually plausible results. We demonstrate our acquisition system which is able to capture intricate materials like fabrics in less than ten minutes with commodity equipments. In addition, we present a method to facilitate effective user design in the space of material appearance. We introduce a metric in the space of reflectance which corresponds roughly to perceptual measures. The main idea of our approach is to evaluate reflectance differences in terms of their induced rendered images, instead of the reflectance function itself defined in the angular domains. With rendered images, we show that even a simple computational metric can provide good perceptual spacing and enable intuitive navigation of the reflectance space.by Wai Kit Addy Ngan.Ph.D

Inverse rendering techniques for physically grounded image editing

From a single picture of a scene, people can typically grasp the spatial layout immediately and even make good guesses at materials properties and where light is coming from to illuminate the scene. For example, we can reliably tell which objects occlude others, what an object is made of and its rough shape, regions that are illuminated or in shadow, and so on. It is interesting how little is known about our ability to make these determinations; as such, we are still not able to robustly "teach" computers to make the same high-level observations as people. This document presents algorithms for understanding intrinsic scene properties from single images. The goal of these inverse rendering techniques is to estimate the configurations of scene elements (geometry, materials, luminaires, camera parameters, etc) using only information visible in an image. Such algorithms have applications in robotics and computer graphics. One such application is in physically grounded image editing: photo editing made easier by leveraging knowledge of the physical space. These applications allow sophisticated editing operations to be performed in a matter of seconds, enabling seamless addition, removal, or relocation of objects in images