Recent advances in video analytics for rail network surveillance for security, trespass and suicide prevention— a survey

Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered

Statistical Machine Learning for Breast Cancer Detection with Terahertz Imaging

Breast conserving surgery (BCS) is a common breast cancer treatment option, in which the cancerous tissue is excised while leaving most of the healthy breast tissue intact. The lack of in-situ margin evaluation unfortunately results in a re-excision rate of 20-30% for this type of procedure. This study aims to design statistical and machine learning segmentation algorithms for the detection of breast cancer in BCS by using terahertz (THz) imaging. Given the material characterization properties of the non-ionizing radiation in the THz range, we intend to employ the responses from the THz system to identify healthy and cancerous breast tissue in BCS samples. In particular, this dissertation covers the description of four segmentation algorithms for the detection of breast cancer in THz imaging. We first explore the performance of one-dimensional (1D) Gaussian mixture and t-mixture models with Markov chain Monte Carlo (MCMC). Second, we propose a novel low-dimension ordered orthogonal projection (LOOP) algorithm for the dimension reduction of the THz information through a modified Gram-Schmidt process. Once the key features within the THz waveform have been detected by LOOP, the segmentation algorithm employs a multivariate Gaussian mixture model with MCMC and expectation maximization (EM). Third, we explore the spatial information of each pixel within the THz image through a Markov random field (MRF) approach. Finally, we introduce a supervised multinomial probit regression algorithm with polynomial and kernel data representations. For evaluation purposes, this study makes use of fresh and formalin-fixed paraffin-embedded (FFPE) heterogeneous human and mice tissue models for the quantitative assessment of the segmentation performance in terms of receiver operating characteristics (ROC) curves. Overall, the experimental results demonstrate that the proposed approaches represent a promising technique for tissue segmentation within THz images of freshly excised breast cancer samples

Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021

As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Diseño CMOS de un sistema de visión “on-chip” para aplicaciones de muy alta velocidad

Falta palabras claveEsta Tesis presenta arquitecturas, circuitos y chips para el diseño de sensores de visión CMOS con procesamiento paralelo embebido. La Tesis reporta dos chips, en concreto: El chip Q-Eye; El chip Eye-RIS_VSoC.. Y dos sistemas de visión construidos con estos chips y otros sistemas “off-chip” adicionales, como FPGAs, en concreto: El sistema Eye-RIS_v1; El sistema Eye-RIS_v2. Estos chips y sistemas están concebidos para ejecutar tareas de visión a muy alta velocidad y con consumos de potencia moderados. Los sistemas resultantes son, además, compactos y por lo tanto ventajosos en términos del factor SWaP cuando se los compara con arquitecturas convencionales formadas por sensores de imágenes convencionales seguidos de procesadores digitales. La clave de estas ventajas en términos de SWaP y velocidad radica en el uso de sensores-procesadores, en lugar de meros sensores, en la interface de los sistemas de visión. Estos sensores-procesadores embeben procesadores programables de señal-mixta dentro del pixel y son capaces tanto de adquirir imágenes como de pre-procesarlas para extraer características, eliminar información redundante y reducir el número de datos que se transmiten fuera del sensor para su procesamiento ulterior. El núcleo de la tesis es el sensor-procesador Q-Eye, que se usa como interface en los sistemas Eye-RIS. Este sensor-procesador embebe una arquitectura de procesamiento formada por procesadores de señal-mixta distribuidos por pixel. Sus píxeles son por tanto estructuras multi-funcionales complejas. De hecho, son programables, incorporan memorias e interactúan con sus vecinos para realizar una variedad de operaciones, tales como: Convoluciones lineales con máscaras programables; Difusiones controladas por tiempo y nivel de señal, a través de un “grid” resistivo embebido en el plano focal; Aritmética de imágenes; Flujo de programación dependiente de la señal; Conversión entre los dominios de datos: imagen en escala de grises e imagen binaria; Operaciones lógicas en imágenes binarias; Operaciones morfológicas en imágenes binarias. etc. Con respecto a otros píxeles multi-función y sensores-procesadores anteriores, el Q-Eye reporta entre otras las siguientes ventajas: Mayor calidad de la imagen y mejores prestaciones de las funcionalidades embebidas en el chip; Mayor velocidad de operación y mejor gestión de la energía disponible; Mayor versatilidad para integración en sistemas de visión industrial. De hecho, los sistemas Eye-RIS son los primeros sistemas de visión industriales dotados de las siguientes características: Procesamiento paralelo distribuido y progresivo; Procesadores de señal-mixta fiables, robustos y con errores controlados; Programabilidad distribuida. La Tesis incluye descripciones detalladas de la arquitectura y los circuitos usados en el pixel del Q-Eye, del propio chip Q-Eye y de los sistemas de visión construidos en base a este chip. Se incluyen también ejemplos de los distintos chips en operaciónThis Thesis presents architectures, circuits and chips for the implementation of CMOS VISION SENSORS with embedded parallel processing. The Thesis reports two chips, namely: Q-eye chip; Eye-RIS_VSoC chip, and two vision systems realized by using these chips and some additional “off-chip” circuitry, such as FPGAs. These vision systems are: Eye-RIS_v1 system; Eye-RIS_v2 system. The chips and systems reported in the Thesis are conceived to perform vision tasks at very high speed and with moderate power consumption. The proposed vision systems are also compact and advantageous in terms of SWaP factors as compared with conventional architectures consisting of standard image sensor followed by digital processors. The key of these advantages in terms of SWaP and speed lies in the use of sensors-processors, rather than mere sensors, in the front-end interface of vision systems. These sensors-processors embed mixed-signal programmable processors inside the pixel. Therefore, they are able to acquire images and process them to extract the features, removing the redundant information and reducing the data throughput for later processing. The core of the Thesis is the sensor-processor Q-Eye, which is used as front-end in the Eye-RIS systems. This sensor-processor embeds a processing architecture composed by mixed-signal processors distributed per pixel. Then, its pixels are complex multi-functional structures. In fact, they are programmable, incorporate memories and interact with its neighbors in order to carry out a set of operations, including: Linear convolutions with programmable linear masks; Time- and signal-controlled diffusions (by means of an embedded resistive grid); Image arithmetic; Signal-dependent data scheduling; Gray-scale to binary transformation; Logic operation on binary images; Mathematical morphology on binary images, etc. As compared with previous multi-function pixels and sensors-processors, the Q-Eye brings among other the following advantages: Higher image quality and better performances of functionalities embedded on chip; Higher operation speed and better management of energy budget; More versatility for integration in industrial vision systems. In fact, the Eye-RIS systems are the first industrial vision systems equipped with the following characteristics: Parallel distributed and progressive processing; Reliable, robust mixed-signal processors with handled errors; Distributed programmability. This Thesis includes detailed descriptions of architecture and circuits used in the Q-Eye pixel, in the Q-Eye chip itself and in the vision systems developed based on this chip. Also, several examples of chips and systems in operation are presented