A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques

In this paper, we systematically review recent advances in surface inspection using computer vision andimage processing techniques, particularly those based on texture analysis methods. The aim is to reviewthe state-of-the-art techniques for the purposes of visual inspection and decision making schemes that areable to discriminate the features extracted from normal and defective regions. This field is so vast that itis impossible to cover all the aspects of visual inspection. This paper focuses on a particular but importantsubset which generally treats visual surface inspection as texture analysis problems. Other topics related tovisual inspection such as imaging system and data acquisition are out of the scope of this survey.The surface defects are loosely separated into two types. One is local textural irregularities which is themain concern for most visual surface inspection applications. The other is global deviation of colour and/ortexture, where local pattern or texture does not exhibit abnormalities. We refer this type of defects as shadeor tonality problem. The second type of defects have been largely neglected until recently, particularly whencolour imaging system has been widely used in visual inspection and where chromatic consistency plays animportant role in quality control. The emphasis of this survey though is still on detecting local abnormalities,given the fact that majority of the reported works are dealing with the first type of defects.The techniques used to inspect textural abnormalities are discussed in four categories, statistical approaches,structural approaches, filter based methods, and model based approaches, with a comprehensivelist of references to some recent works. Due to rising demand and practice of colour texture analysis inapplication to visual inspection, those works that are dealing with colour texture analysis are discussedseparately. It is also worth noting that processing vector-valued data has its unique challenges, which conventionalsurface inspection methods have often ignored or do not encounter.We also compare classification approaches with novelty detection approaches at the decision makingstage. Classification approaches often require supervised training and usually provide better performancethan novelty detection based approaches where training is only carried out on defect-free samples. However,novelty detection is relatively easier to adapt and is particularly desirable when training samples areincomplet

Sparse Coding Based Feature Representation Method for Remote Sensing Images

In this dissertation, we study sparse coding based feature representation method for the classification of multispectral and hyperspectral images (HSI). The existing feature representation systems based on the sparse signal model are computationally expensive, requiring to solve a convex optimization problem to learn a dictionary. A sparse coding feature representation framework for the classification of HSI is presented that alleviates the complexity of sparse coding through sub-band construction, dictionary learning, and encoding steps. In the framework, we construct the dictionary based upon the extracted sub-bands from the spectral representation of a pixel. In the encoding step, we utilize a soft threshold function to obtain sparse feature representations for HSI. Experimental results showed that a randomly selected dictionary could be as effective as a dictionary learned from optimization. The new representation usually has a very high dimensionality requiring a lot of computational resources. In addition, the spatial information of the HSI data has not been included in the representation. Thus, we modify the framework by incorporating the spatial information of the HSI pixels and reducing the dimension of the new sparse representations. The enhanced model, called sparse coding based dense feature representation (SC-DFR), is integrated with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) classifiers to discriminate different types of land cover. We evaluated the proposed algorithm on three well known HSI datasets and compared our method to four recently developed classification methods: SVM, CKSVM, simultaneous orthogonal matching pursuit (SOMP) and image fusion and recursive filtering (IFRF). The results from the experiments showed that the proposed method can achieve better overall and average classification accuracies with a much more compact representation leading to more efficient sparse models for HSI classification. To further verify the power of the new feature representation method, we applied it to a pan-sharpened image to detect seafloor scars in shallow waters. Propeller scars are formed when boat propellers strike and break apart seagrass beds, resulting in habitat loss. We developed a robust identification system by incorporating morphological filters to detect and map the scars. Our results showed that the proposed method can be implemented on a regular basis to monitor changes in habitat characteristics of coastal waters

Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a phenomenal mechanism to analyze diversified land cover in remotely sensed hyperspectral images. In the field of remote sensing, HSI classification has been an established research topic, and herein, the inherent primary challenges are (i) curse of dimensionality and (ii) insufficient samples pool during training. Given a set of observations with known class labels, the basic goal of hyperspectral image classification is to assign a class label to each pixel. This chapter discusses the recent progress in the classification of HS images in the aspects of Kernel-based methods, supervised and unsupervised classifiers, classification based on sparse representation, and spectral-spatial classification. Further, the classification methods based on machine learning and the future directions are discussed

A robust framework for medical image segmentation through adaptable class-specific representation

Medical image segmentation is an increasingly important component in virtual pathology, diagnostic imaging and computer-assisted surgery. Better hardware for image acquisition and a variety of advanced visualisation methods have paved the way for the development of computer based tools for medical image analysis and interpretation. The routine use of medical imaging scans of multiple modalities has been growing over the last decades and data sets such as the Visible Human Project have introduced a new modality in the form of colour cryo section data. These developments have given rise to an increasing need for better automatic and semiautomatic segmentation methods. The work presented in this thesis concerns the development of a new framework for robust semi-automatic segmentation of medical imaging data of multiple modalities. Following the specification of a set of conceptual and technical requirements, the framework known as ACSR (Adaptable Class-Specific Representation) is developed in the first case for 2D colour cryo section segmentation. This is achieved through the development of a novel algorithm for adaptable class-specific sampling of point neighbourhoods, known as the PGA (Path Growing Algorithm), combined with Learning Vector Quantization. The framework is extended to accommodate 3D volume segmentation of cryo section data and subsequently segmentation of single and multi-channel greyscale MRl data. For the latter the issues of inhomogeneity and noise are specifically addressed. Evaluation is based on comparison with previously published results on standard simulated and real data sets, using visual presentation, ground truth comparison and human observer experiments. ACSR provides the user with a simple and intuitive visual initialisation process followed by a fully automatic segmentation. Results on both cryo section and MRI data compare favourably to existing methods, demonstrating robustness both to common artefacts and multiple user initialisations. Further developments into specific clinical applications are discussed in the future work section

A Relaxation Scheme for Mesh Locality in Computer Vision.

Parallel processing has been considered as the key to build computer systems of the future and has become a mainstream subject in Computer Science. Computer Vision applications are computationally intensive that require parallel approaches to exploit the intrinsic parallelism. This research addresses this problem for low-level and intermediate-level vision problems. The contributions of this dissertation are a unified scheme based on probabilistic relaxation labeling that captures localities of image data and the ability of using this scheme to develop efficient parallel algorithms for Computer Vision problems. We begin with investigating the problem of skeletonization. The technique of pattern match that exhausts all the possible interaction patterns between a pixel and its neighboring pixels captures the locality of this problem, and leads to an efficient One-pass Parallel Asymmetric Thinning Algorithm (OPATA\sb8). The use of 8-distance in this algorithm, or chessboard distance, not only improves the quality of the resulting skeletons, but also improves the efficiency of the computation. This new algorithm plays an important role in a hierarchical route planning system to extract high level typological information of cross-country mobility maps which greatly speeds up the route searching over large areas. We generalize the neighborhood interaction description method to include more complicated applications such as edge detection and image restoration. The proposed probabilistic relaxation labeling scheme exploit parallelism by discovering local interactions in neighboring areas and by describing them effectively. The proposed scheme consists of a transformation function and a dictionary construction method. The non-linear transformation function is derived from Markov Random Field theory. It efficiently combines evidences from neighborhood interactions. The dictionary construction method provides an efficient way to encode these localities. A case study applies the scheme to the problem of edge detection. The relaxation step of this edge-detection algorithm greatly reduces noise effects, gets better edge localization such as line ends and corners, and plays a crucial rule in refining edge outputs. The experiments on both synthetic and natural images show that our algorithm converges quickly, and is robust in noisy environment

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

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