Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Defect Detection and Classification in Sewer Pipeline Inspection Videos Using Deep Neural Networks

Sewer pipelines as a critical civil infrastructure become a concern for municipalities as they are getting near to the end of their service lives. Meanwhile, new environmental laws and regulations, city expansions, and budget constraints make it harder to maintain these networks. On the other hand, access and inspect sewer pipelines by human-entry based methods are problematic and risky. Current practice for sewer pipeline assessment uses various types of equipment to inspect the condition of pipelines. One of the most used technologies for sewer pipelines inspection is Closed Circuit Television (CCTV). However, application of CCTV method in extensive sewer networks involves certified operators to inspect hours of videos, which is time-consuming, labor-intensive, and error prone. The main objective of this research is to develop a framework for automated defect detection and classification in sewer CCTV inspection videos using computer vision techniques and deep neural networks. This study presents innovative algorithms to deal with the complexity of feature extraction and pattern recognition in sewer inspection videos due to lighting conditions, illumination variations, and unknown patterns of various sewer defects. Therefore, this research includes two main sub-models to first identify and localize anomalies in sewer inspection videos, and in the next phase, detect and classify the defects among the recognized anomalous frames. In the first phase, an innovative approach is proposed for identifying the frames with potential anomalies and localizing them in the pipe segment which is being inspected. The normal and anomalous frames are classified utilizing a one-class support vector machine (OC-SVM). The proposed approach employs 3D Scale Invariant Feature Transform (SIFT) to extract spatio-temporal features and capture scene dynamic statistics in sewer CCTV videos. The OC-SVM is trained by the frame-features which are considered normal, and the outliers to this model are considered abnormal frames. In the next step, the identified anomalous frames are located by recognizing the present text information in them using an end-to-end text recognition approach. The proposed localization approach is performed in two steps, first the text regions are detected using maximally stable extremal regions (MSER) algorithm, then the text characters are recognized using a convolutional neural network (CNN). The performance of the proposed model is tested using videos from real-world sewer inspection reports, where the accuracies of 95% and 86% were achieved for anomaly detection and frame localization, respectively. Identifying the anomalous frames and excluding the normal frames from further analysis could reduce the time and cost of detection. It also ensures the accuracy and quality of assessment by reducing the number of neglected anomalous frames caused by operator error. In the second phase, a defect detection framework is proposed to provide defect detection and classification among the identified anomalous frames. First, a deep Convolutional Neural Network (CNN) which is pre-trained using transfer learning, is used as a feature extractor. In the next step, the remaining convolutional layers of the constructed model are trained by the provided dataset from various types of sewer defects to detect and classify defects in the anomalous frames. The proposed methodology was validated by referencing the ground truth data of a dataset including four defects, and the mAP of 81.3% was achieved. It is expected that the developed model can help sewer inspectors in much faster and more accurate pipeline inspection. The whole framework would decrease the condition assessment time and increase the accuracy of sewer assessment reports

Three-dimensional eddy current pulsed thermography and its applications

Ph. D. Thesis.The measurement and quantification of defects is a challenge for Non-DestructiveTesting and Evaluation (NDT&E). Such challenges include the precise localisation and detection of surface and sub-surface defects, as well as the quantification of such defects. This work first reports a three-dimensional (3D) Eddy Current Pulsed Thermography (ECPT) system via integration with an RGB-D camera. Then, various quantitative measurements and analyses of defects are carried out based on the 3D ECPT system. The ECPT system at Newcastle University has been prooven to be an effective nondestructive testing (NDT) method in surface and sub-surface detection over the past few years. Based on the different numerical or analytical models, it has achieved precise defect detection on the rail tracks, wind turbines, carbon fibre reinforced plastic (CFRP) and so on. The ECPT system has the advantage of fast inspection and a large lift-off range. However, it involves a trade-off between detectable defect size and inspection area compared with other NDT methods. In addition, there are challenges of defect detection in a complex structure. Thus, the quantification of defects gives a higher requirement of the measurement the object geometry information. Furthermore, the analysis of thermal diffusion requires a precise 3D model. For this reason, a 3D ECPT system is proposed that adds each heat pixel with an exact X-Y-Z coordinate. In this work, first, the 3D ECPT system is built. A feature-based automatic calibration of the infrared camera and the RGB-D camera is proposed. Second, the software platform is built. A fast 3D visualization is completed with multi-threading technology and the Point Cloud Library. Lastly, various studies of defect localization, quantification and thermal tomography reconstruction are carried ou