Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

An investigation into common challenges of 3D scene understanding in visual surveillance

Nowadays, video surveillance systems are ubiquitous. Most installations simply consist of CCTV cameras connected to a central control room and rely on human operators to interpret what they see on the screen in order to, for example, detect a crime (either during or after an event). Some modern computer vision systems aim to automate the process, at least to some degree, and various algorithms have been somewhat successful in certain limited areas. However, such systems remain inefficient in general circumstances and present real challenges yet to be solved. These challenges include the ability to recognise and ultimately predict and prevent abnormal behaviour or even reliably recognise objects, for example in order to detect left luggage or suspicious objects. This thesis first aims to study the state-of-the-art and identify the major challenges and possible requirements of future automated and semi-automated CCTV technology in the field. This thesis presents the application of a suite of 2D and highly novel 3D methodologies that go some way to overcome current limitations.The methods presented here are based on the analysis of object features directly extracted from the geometry of the scene and start with a consideration of mainly existing techniques, such as the use of lines, vanishing points (VPs) and planes, applied to real scenes. Then, an investigation is presented into the use of richer 2.5D/3D surface normal data. In all cases the aim is to combine both 2D and 3D data to obtain a better understanding of the scene, aimed ultimately at capturing what is happening within the scene in order to be able to move towards automated scene analysis. Although this thesis focuses on the widespread application of video surveillance, an example case of the railway station environment is used to represent typical real-world challenges, where the principles can be readily extended elsewhere, such as to airports, motorways, the households, shopping malls etc. The context of this research work, together with an overall presentation of existing methods used in video surveillance and their challenges are described in chapter 1.Common computer vision techniques such as VP detection, camera calibration, 3D reconstruction, segmentation etc., can be applied in an effort to extract meaning to video surveillance applications. According to the literature, these methods have been well researched and their use will be assessed in the context of current surveillance requirements in chapter 2. While existing techniques can perform well in some contexts, such as an architectural environment composed of simple geometrical elements, their robustness and performance in feature extraction and object recognition tasks is not sufficient to solve the key challenges encountered in general video surveillance context. This is largely due to issues such as variable lighting, weather conditions, and shadows and in general complexity of the real-world environment. Chapter 3 presents the research and contribution on those topics – methods to extract optimal features for a specific CCTV application – as well as their strengths and weaknesses to highlight that the proposed algorithm obtains better results than most due to its specific design.The comparison of current surveillance systems and methods from the literature has shown that 2D data are however almost constantly used for many applications. Indeed, industrial systems as well as the research community have been improving intensively 2D feature extraction methods since image analysis and Scene understanding has been of interest. The constant progress on 2D feature extraction methods throughout the years makes it almost effortless nowadays due to a large variety of techniques. Moreover, even if 2D data do not allow solving all challenges in video surveillance or other applications, they are still used as starting stages towards scene understanding and image analysis. Chapter 4 will then explore 2D feature extraction via vanishing point detection and segmentation methods. A combination of most common techniques and a novel approach will be then proposed to extract vanishing points from video surveillance environments. Moreover, segmentation techniques will be explored in the aim to determine how they can be used to complement vanishing point detection and lead towards 3D data extraction and analysis. In spite of the contribution above, 2D data is insufficient for all but the simplest applications aimed at obtaining an understanding of a scene, where the aim is for a robust detection of, say, left luggage or abnormal behaviour; without significant a priori information about the scene geometry. Therefore, more information is required in order to be able to design a more automated and intelligent algorithm to obtain richer information from the scene geometry and so a better understanding of what is happening within. This can be overcome by the use of 3D data (in addition to 2D data) allowing opportunity for object “classification” and from this to infer a map of functionality, describing feasible and unfeasible object functionality in a given environment. Chapter 5 presents how 3D data can be beneficial for this task and the various solutions investigated to recover 3D data, as well as some preliminary work towards plane extraction.It is apparent that VPs and planes give useful information about a scene’s perspective and can assist in 3D data recovery within a scene. However, neither VPs nor plane detection techniques alone allow the recovery of more complex generic object shapes - for example composed of spheres, cylinders etc - and any simple model will suffer in the presence of non-Manhattan features, e.g. introduced by the presence of an escalator. For this reason, a novel photometric stereo-based surface normal retrieval methodology is introduced to capture the 3D geometry of the whole scene or part of it. Chapter 6 describes how photometric stereo allows recovery of 3D information in order to obtain a better understanding of a scene, as well as also partially overcoming some current surveillance challenges, such as difficulty in resolving fine detail, particularly at large standoff distances, and in isolating and recognising more complex objects in real scenes. Here items of interest may be obscured by complex environmental factors that are subject to rapid change, making, for example, the detection of suspicious objects and behaviour highly problematic. Here innovative use is made of an untapped latent capability offered within modern surveillance environments to introduce a form of environmental structuring to good advantage in order to achieve a richer form of data acquisition. This chapter also goes on to explore the novel application of photometric stereo in such diverse applications, how our algorithm can be incorporated into an existing surveillance system and considers a typical real commercial application.One of the most important aspects of this research work is its application. Indeed, while most of the research literature has been based on relatively simple structured environments, the approach here has been designed to be applied to real surveillance environments, such as railway stations, airports, waiting rooms, etc, and where surveillance cameras may be fixed or in the future form part of a mobile robotic free roaming surveillance device, that must continually reinterpret its changing environment. So, as mentioned previously, while the main focus has been to apply this algorithm to railway station environments, the work has been approached in a way that allows adaptation to many other applications, such as autonomous robotics, and in motorway, shopping centre, street and home environments. All of these applications require a better understanding of the scene for security or safety purposes. Finally, chapter 7 presents a global conclusion and what will be achieved in the future

Medical image analysis methods for anatomical surface reconstruction using tracked 3D ultrasound

The thesis focuses on a study of techniques for acquisition and reconstruction of surface data from anatomical objects by means of tracked 3D ultrasound. In the context of the work two experimental scanning systems are developed and tested on both artificial objects and biological tissues. The first system is based on the freehand ultrasound principle and utilizes a conventional 2D ultrasound transducer coupled with an electromechanical 3D position tracker. The main properties and the basic features of this system are discussed. A number of experiments show that its accuracy in the close to ideal conditions reaches 1.2 mm RMS. The second proposed system implements the sequential triggered scanning approach. The system consists of an ultrasound machine, a workstation and a scanning body (a moving tank filled with liquid and a transducer fixation block) that performs transducer positioning and tracking functions. The system is tested on artificial and real bones. The performed experiments illustrate that it provides significantly better accuracy than the freehand ultrasound (about 0.2 mm RMS) and allows acquiring regular data with a good precision. This makes such a system a promising tool for orthopaedic and trauma surgeons during contactless X-ray-free examinations of injured extremities. The second major subject of the thesis concerns development of medical image analysis methods for 3D surface reconstruction and 2D object detection. We introduce a method based on mesh-growing surface reconstruction that is designed for noisy and sparse data received from 3D tracked ultrasound scanners. A series of experiments on synthetic and ultrasound data show an appropriate reconstruction accuracy. The reconstruction error is measured as the averaged distance between the faces of the mesh and the points from the cloud. Dependently on the initial settings of the method the error varies in range 0.04 - 0.2% for artificial data and 0.3 - 0.7 mm for ultrasound bone data. The reconstructed surfaces correctly interpolate the original point clouds and demonstrate proper smoothness. The next significant problem considered in the work is 2D object detection. Although medical object detection is not integrated into the developed scanning systems, it can be used as a possible further extension of the systems for automatic detection of specific anatomical structures. We analyse the existent object detection methods and introduce a modification of the one based on the popular Generalized Hough Transform (GHT). Unlike the original GHT, the developed method is invariant to rotation and uniform scaling, and uses an intuitive two-point parametrization. We propose several implementations of the feature-to-vote conversion function with the corresponding vote analysis principles. Special attention is devoted to a study of the hierarchical vote analysis and its probabilistic properties. We introduce a parameter space subdivision strategy that reduces the probability of vote peak omission, and show that it can be efficiently implemented in practice using the Gumbel probability distribution

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Towards Developing Computer Vision Algorithms and Architectures for Real-world Applications

abstract: Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for object segmentation and feature extraction for objects and actions recognition in video data, and sparse feature selection algorithms for medical image analysis, as well as automated feature extraction using convolutional neural network for blood cancer grading. To detect and classify objects in video, the objects have to be separated from the background, and then the discriminant features are extracted from the region of interest before feeding to a classifier. Effective object segmentation and feature extraction are often application specific, and posing major challenges for object detection and classification tasks. In this dissertation, we address effective object flow based ROI generation algorithm for segmenting moving objects in video data, which can be applied in surveillance and self driving vehicle areas. Optical flow can also be used as features in human action recognition algorithm, and we present using optical flow feature in pre-trained convolutional neural network to improve performance of human action recognition algorithms. Both algorithms outperform the state-of-the-arts at their time. Medical images and videos pose unique challenges for image understanding mainly due to the fact that the tissues and cells are often irregularly shaped, colored, and textured, and hand selecting most discriminant features is often difficult, thus an automated feature selection method is desired. Sparse learning is a technique to extract the most discriminant and representative features from raw visual data. However, sparse learning with \textit{L1} regularization only takes the sparsity in feature dimension into consideration; we improve the algorithm so it selects the type of features as well; less important or noisy feature types are entirely removed from the feature set. We demonstrate this algorithm to analyze the endoscopy images to detect unhealthy abnormalities in esophagus and stomach, such as ulcer and cancer. Besides sparsity constraint, other application specific constraints and prior knowledge may also need to be incorporated in the loss function in sparse learning to obtain the desired results. We demonstrate how to incorporate similar-inhibition constraint, gaze and attention prior in sparse dictionary selection for gastroscopic video summarization that enable intelligent key frame extraction from gastroscopic video data. With recent advancement in multi-layer neural networks, the automatic end-to-end feature learning becomes feasible. Convolutional neural network mimics the mammal visual cortex and can extract most discriminant features automatically from training samples. We present using convolutinal neural network with hierarchical classifier to grade the severity of Follicular Lymphoma, a type of blood cancer, and it reaches 91\% accuracy, on par with analysis by expert pathologists. Developing real world computer vision applications is more than just developing core vision algorithms to extract and understand information from visual data; it is also subject to many practical requirements and constraints, such as hardware and computing infrastructure, cost, robustness to lighting changes and deformation, ease of use and deployment, etc.The general processing pipeline and system architecture for the computer vision based applications share many similar design principles and architecture. We developed common processing components and a generic framework for computer vision application, and a versatile scale adaptive template matching algorithm for object detection. We demonstrate the design principle and best practices by developing and deploying a complete computer vision application in real life, building a multi-channel water level monitoring system, where the techniques and design methodology can be generalized to other real life applications. The general software engineering principles, such as modularity, abstraction, robust to requirement change, generality, etc., are all demonstrated in this research.Dissertation/ThesisDoctoral Dissertation Computer Science 201

An effective trajectory-based algorithm for ball detection and tracking with application to the analysis of broadcast sports video

Ph.DDOCTOR OF PHILOSOPH