Formulation of Pattern Recognition Framework - Analysis and Detection of Tyre Cracks Utilizing Integrated Texture Features and Ensemble Learning Methods

For a safe drive with a vehicle and better tyre life, it is important to regularly monitor the tyre damages to diagnose its condition and chose appropri- ate solution. This paper proposes a framework based on pattern recognition utilizing the strength of texture attributes and ensemble learning to detect the damages on the tyre surfaces. In this paper, a concatenation of the statistical and edge response based texture features derived from Gray Level Co-occurrence Matrix and Local directional pattern are proposed to describe and represent the tyre surface characteristics and their variations due to any damages. The derived fea- tures are provided to train machine learning algorithms using ensemble learning methods for a better under- standing to discriminate the tyre surfaces into normal or damaged. The experiments of tyre surface classifica- tion were conducted on the tyre surface images acquired from Kaggle tyre dataset. The results demonstrated the ability of the combined texture features and ensemble learning methods in effectively analysing the tyre sur- faces and discriminate them with better performance provided by adaboost and histogram gradient boosting methods

Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop

Variational methods and its applications to computer vision

Many computer vision applications such as image segmentation can be formulated in a ''variational'' way as energy minimization problems. Unfortunately, the computational task of minimizing these energies is usually difficult as it generally involves non convex functions in a space with thousands of dimensions and often the associated combinatorial problems are NP-hard to solve. Furthermore, they are ill-posed inverse problems and therefore are extremely sensitive to perturbations (e.g. noise). For this reason in order to compute a physically reliable approximation from given noisy data, it is necessary to incorporate into the mathematical model appropriate regularizations that require complex computations. The main aim of this work is to describe variational segmentation methods that are particularly effective for curvilinear structures. Due to their complex geometry, classical regularization techniques cannot be adopted because they lead to the loss of most of low contrasted details. In contrast, the proposed method not only better preserves curvilinear structures, but also reconnects some parts that may have been disconnected by noise. Moreover, it can be easily extensible to graphs and successfully applied to different types of data such as medical imagery (i.e. vessels, hearth coronaries etc), material samples (i.e. concrete) and satellite signals (i.e. streets, rivers etc.). In particular, we will show results and performances about an implementation targeting new generation of High Performance Computing (HPC) architectures where different types of coprocessors cooperate. The involved dataset consists of approximately 200 images of cracks, captured in three different tunnels by a robotic machine designed for the European ROBO-SPECT project.Open Acces

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

A generic framework for colour texture segmentation

This thesis proposes a novel method to combine the colour and the texture for colour texture segmentation. The objective of this research work is to derive a framework for colour texture segmentation and to determine the contribution of colour in colour texture analysis. The colour texture processing is based on the feature extraction from colour-textured images. The texture features were obtained from the luminance plane along with the colour features from the chrominance planes. Based on the above mentioned approach, a method was developed for colour texture segmentation. The proposed method unifies colour and texture features to solve the colour texture segmentation problem. Two of the grey scale texture analysis techniques, Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) based filter approach were extended to colour images. An unsupervised fc-means clustering was used to cluster pixels in the chrominance planes. Non-parametric test was used to test the similarity between colour texture regions. An unsupervised texture segmentation method was followed to obtain the segmented image. The evaluation of the segmentation was based on the ROC curves. A quantitative estimation of colour and texture performance in segmentation was presented. The use of different colour spaces was also investigated in this study. The proposed method was tested using different mosaic and natural images obtained from VisTex and other predominant image database used in computer vision. The applications for the proposed colour texture segmentation method are, Irish Script On Screen (ISOS) images for the segmentation of the colour textured regions in the document, skin cancer images to identify the diseased area, and Sediment Profile Imagery (SPI) to segment underwater images. The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicated that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient

Semantic models of scenes and objects for service and industrial robotics

What may seem straightforward for the human perception system is still challenging for robots. Automatically segmenting the elements with highest relevance or salience, i.e. the semantics, is non-trivial given the high level of variability in the world and the limits of vision sensors. This stands up when multiple ambiguous sources of information are available, which is the case when dealing with moving robots. This thesis leverages on the availability of contextual cues and multiple points of view to make the segmentation task easier. Four robotic applications will be presented, two designed for service robotics and two for an industrial context. Semantic models of indoor environments will be built enriching geometric reconstructions with semantic information about objects, structural elements and humans. Our approach leverages on the importance of context, the availability of multiple source of information, as well as multiple view points showing with extensive experiments on several datasets that these are all crucial elements to boost state-of-the-art performances. Furthermore, moving to applications with robots analyzing object surfaces instead of their surroundings, semantic models of Carbon Fiber Reinforced Polymers will be built augmenting geometric models with accurate measurements of superficial fiber orientations, and inner defects invisible to the human-eye. We succeeded in reaching an industrial grade accuracy making these models useful for autonomous quality inspection and process optimization. In all applications, special attention will be paid towards fast methods suitable for real robots like the two prototypes presented in this thesis

Exergy-based Planning and Thermography-based Monitoring for energy efficient buildings - Progress Report (KIT Scientific Reports ; 7632)

Designing and monitoring energy efficiency of buildings is vital since they account for up to 40% of end-use energy. In this study, exergy analysis is investigated as a life cycle design tool to strike a balance between thermodynamic efficiency of energy conversion and economic and environmental costs of construction. Quantitative geo-referenced thermography is proposed for monitoring and quantitative assessment via continued simulation and parameter estimation during the operating phase

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens