Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Data-driven quantitative photoacoustic tomography

Spatial information about the 3D distribution of blood oxygen saturation (sO2) in vivo is of clinical interest as it encodes important physiological information about tissue health/pathology. Photoacoustic tomography (PAT) is a biomedical imaging modality that, in principle, can be used to acquire this information. Images are formed by illuminating the sample with a laser pulse where, after multiple scattering events, the optical energy is absorbed. A subsequent rise in temperature induces an increase in pressure (the photoacoustic initial pressure p0) that propagates to the sample surface as an acoustic wave. These acoustic waves are detected as pressure time series by sensor arrays and used to reconstruct images of sample’s p0 distribution. This encodes information about the sample’s absorption distribution, and can be used to estimate sO2. However, an ill-posed nonlinear inverse problem stands in the way of acquiring estimates in vivo. Current approaches to solving this problem fall short of being widely and successfully applied to in vivo tissues due to their reliance on simplifying assumptions about the tissue, prior knowledge of its optical properties, or the formulation of a forward model accurately describing image acquisition with a specific imaging system. Here, we investigate the use of data-driven approaches (deep convolutional networks) to solve this problem. Networks only require a dataset of examples to learn a mapping from PAT data to images of the sO2 distribution. We show the results of training a 3D convolutional network to estimate the 3D sO2 distribution within model tissues from 3D multiwavelength simulated images. However, acquiring a realistic training set to enable successful in vivo application is non-trivial given the challenges associated with estimating ground truth sO2 distributions and the current limitations of simulating training data. We suggest/test several methods to 1) acquire more realistic training data or 2) improve network performance in the absence of adequate quantities of realistic training data. For 1) we describe how training data may be acquired from an organ perfusion system and outline a possible design. Separately, we describe how training data may be generated synthetically using a variant of generative adversarial networks called ambientGANs. For 2), we show how the accuracy of networks trained with limited training data can be improved with self-training. We also demonstrate how the domain gap between training and test sets can be minimised with unsupervised domain adaption to improve quantification accuracy. Overall, this thesis clarifies the advantages of data-driven approaches, and suggests concrete steps towards overcoming the challenges with in vivo application

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

“Design, Development and Characterization of a Thermal Sensor Brick System for Modular Robotics

This thesis presents the work on thermal imaging sensor brick (TISB) system for modular robotics. The research demonstrates the design, development and characterization of the TISB system. The TISB system is based on the design philosophy of sensor bricks for modular robotics. In under vehicle surveillance for threat detection, which is a target application of this work we have demonstrated the advantages of the TISB system over purely vision-based systems. We have highlighted the advantages of the TISB system as an illumination invariant threat detection system for detecting hidden threat objects in the undercarriage of a car. We have compared the TISB system to the vision sensor brick system and the mirror on a stick. We have also illustrated the operational capability of the system on the SafeBot under vehicle robot to acquire and transmit the data wirelessly. The early designs of the TISB system, the evolution of the designs and the uniformity achieved while maintaining the modularity in building the different sensor bricks; the visual, the thermal and the range sensor brick is presented as part of this work. Each of these sensor brick systems designed and implemented at the Imaging Robotics and Intelligent Systems (IRIS) laboratory consist of four major blocks: Sensing and Image Acquisition Block, Pre-Processing and Fusion Block, Communication Block, and Power Block. The Sensing and Image Acquisition Block is to capture images or acquire data. The Pre-Processing and Fusion Block is to work on the acquired images or data. The Communication Block is for transferring data between the sensor brick and the remote host computer. The Power Block is to maintain power supply to the entire brick. The modular sensor bricks are self-sufficient plug and play systems. The SafeBot under vehicle robot designed and implemented at the IRIS laboratory has two tracked platforms one on each side with a payload bay area in the middle. Each of these tracked platforms is a mobility brick based on the same design philosophy as the modular sensor bricks. The robot can carry one brick at a time or even multiple bricks at the same time. The contributions of this thesis are: (1) designing and developing the hardware implementation of the TISB system, (2) designing and developing the software for the TISB system, and (3) characterizing the TISB system, where this characterization of the system is the major contribution of this thesis. The analysis of the thermal sensor brick system provides the user and future designers with sufficient information on parameters to be considered to make the right choice for future modifications, the kind of applications the TISB could handle and the load that the different blocks of the TISB system could manage. Under vehicle surveillance for threat detection, perimeter / area surveillance, scouting, and improvised explosive device (IED) detection using a car-mounted system are some of the applications that have been identified for this system