An object-based convolutional neural network (OCNN) for urban land use classification

Urban land use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban land use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban land use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the classification of urban land use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban land use classification using VFSR images. Rather than pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images

A machine vision based automatic optical inspection system for measuring drilling quality of printed circuit boards

In this paper, we develop and put into practice an automatic optical inspection (AOI) system based on machine vision to check the holes on a printed circuit board (PCB). We incorporate the hardware and software. For the hardware part, we combine a PC, the three-axis positioning system, a lighting device, and charge-coupled device cameras. For the software part, we utilize image registration, image segmentation, drill numbering, drill contrast, and defect displays to achieve this system. Results indicated that an accuracy of 5 mu m could be achieved in errors of the PCB holes allowing comparisons to be made. This is significant in inspecting the missing, the multi-hole, and the incorrect location of the holes. However, previous work only focuses on one or other feature of the holes. Our research is able to assess multiple features: missing holes, incorrectly located holes, and excessive holes. Equally, our results could be displayed as a bar chart and target plot. This has not been achieved before. These displays help users to analyze the causes of errors and immediately correct the problems. In addition, this AOI system is valuable for checking a large number of holes and finding out the defective ones on a PCB. Meanwhile, we apply a 0.1-mm image resolution, which is better than others used in industry. We set a detecting standard based on 2-mm diameter of circles to diagnose the quality of the holes within 10 s

Proper shape representation of single figure and multi-figure anatomical objects

Extracting anatomic objects from medical images is an important process in various medical applications. This extraction, called image segmentation, is often realized by deformable models. Among deformable model methods, medial deformable models have the unique advantage of representing not only the object boundary surfaces but also the object interior volume. Based on one medial deformable model called the m-rep, the main goal of this dissertation is to provide proper shape representations of simple anatomical objects of one part and complex anatomical objects of multiple parts in a population. This dissertation focuses on several challenges in the existing medially based deformable model method: 1. how to derive a proper continuous form by interpolating a discrete medial shape representation; 2. how to represent complex objects with several parts and do statistical analysis on them; 3. how to avoid local shape defects, such as folding or creasing, in shapes represented by the deformable model. The proposed methods in this dissertation address these challenges in more detail: 1. An interpolation method for a discrete medial shape model is proposed to guarantee the legality of the interpolated shape. This method is based on the integration of medial shape operators. 2. A medially based representation with hierarchy is proposed to represent complex objects with multiple parts by explicitly modeling interrelations between object parts and modeling smooth transitions between each pair of connected parts. A hierarchical statistical analysis is also proposed for these complex objects. 3. A method to fit a medial model to binary images is proposed to use an explicit legality penalty derived from the medial shape operators. Probability distributions learned from the fitted shape models by the proposed fitting method have proven to yield better image segmentation results

Integrated visual perception architecture for robotic clothes perception and manipulation

This thesis proposes a generic visual perception architecture for robotic clothes perception and manipulation. This proposed architecture is fully integrated with a stereo vision system and a dual-arm robot and is able to perform a number of autonomous laundering tasks. Clothes perception and manipulation is a novel research topic in robotics and has experienced rapid development in recent years. Compared to the task of perceiving and manipulating rigid objects, clothes perception and manipulation poses a greater challenge. This can be attributed to two reasons: firstly, deformable clothing requires precise (high-acuity) visual perception and dexterous manipulation; secondly, as clothing approximates a non-rigid 2-manifold in 3-space, that can adopt a quasi-infinite configuration space, the potential variability in the appearance of clothing items makes them difficult to understand, identify uniquely, and interact with by machine. From an applications perspective, and as part of EU CloPeMa project, the integrated visual perception architecture refines a pre-existing clothing manipulation pipeline by completing pre-wash clothes (category) sorting (using single-shot or interactive perception for garment categorisation and manipulation) and post-wash dual-arm flattening. To the best of the author’s knowledge, as investigated in this thesis, the autonomous clothing perception and manipulation solutions presented here were first proposed and reported by the author. All of the reported robot demonstrations in this work follow a perception-manipulation method- ology where visual and tactile feedback (in the form of surface wrinkledness captured by the high accuracy depth sensor i.e. CloPeMa stereo head or the predictive confidence modelled by Gaussian Processing) serve as the halting criteria in the flattening and sorting tasks, respectively. From scientific perspective, the proposed visual perception architecture addresses the above challenges by parsing and grouping 3D clothing configurations hierarchically from low-level curvatures, through mid-level surface shape representations (providing topological descriptions and 3D texture representations), to high-level semantic structures and statistical descriptions. A range of visual features such as Shape Index, Surface Topologies Analysis and Local Binary Patterns have been adapted within this work to parse clothing surfaces and textures and several novel features have been devised, including B-Spline Patches with Locality-Constrained Linear coding, and Topology Spatial Distance to describe and quantify generic landmarks (wrinkles and folds). The essence of this proposed architecture comprises 3D generic surface parsing and interpretation, which is critical to underpinning a number of laundering tasks and has the potential to be extended to other rigid and non-rigid object perception and manipulation tasks. The experimental results presented in this thesis demonstrate that: firstly, the proposed grasp- ing approach achieves on-average 84.7% accuracy; secondly, the proposed flattening approach is able to flatten towels, t-shirts and pants (shorts) within 9 iterations on-average; thirdly, the proposed clothes recognition pipeline can recognise clothes categories from highly wrinkled configurations and advances the state-of-the-art by 36% in terms of classification accuracy, achieving an 83.2% true-positive classification rate when discriminating between five categories of clothes; finally the Gaussian Process based interactive perception approach exhibits a substantial improvement over single-shot perception. Accordingly, this thesis has advanced the state-of-the-art of robot clothes perception and manipulation

Deep learning for land cover and land use classification

Recent advances in sensor technologies have witnessed a vast amount of very fine spatial resolution (VFSR) remotely sensed imagery being collected on a daily basis. These VFSR images present fine spatial details that are spectrally and spatially complicated, thus posing huge challenges in automatic land cover (LC) and land use (LU) classification. Deep learning reignited the pursuit of artificial intelligence towards a general purpose machine to be able to perform any human-related tasks in an automated fashion. This is largely driven by the wave of excitement in deep machine learning to model the high-level abstractions through hierarchical feature representations without human-designed features or rules, which demonstrates great potential in identifying and characterising LC and LU patterns from VFSR imagery. In this thesis, a set of novel deep learning methods are developed for LC and LU image classification based on the deep convolutional neural networks (CNN) as an example. Several difficulties, however, are encountered when trying to apply the standard pixel-wise CNN for LC and LU classification using VFSR images, including geometric distortions, boundary uncertainties and huge computational redundancy. These technical challenges for LC classification were solved either using rule-based decision fusion or through uncertainty modelling using rough set theory. For land use, an object-based CNN method was proposed, in which each segmented object (a group of homogeneous pixels) was sampled and predicted by CNN with both within-object and between-object information. LU was, thus, classified with high accuracy and efficiency. Both LC and LU formulate a hierarchical ontology at the same geographical space, and such representations are modelled by their joint distribution, in which LC and LU are classified simultaneously through iteration. These developed deep learning techniques achieved by far the highest classification accuracy for both LC and LU, up to around 90% accuracy, about 5% higher than the existing deep learning methods, and 10% greater than traditional pixel-based and object-based approaches. This research made a significant contribution in LC and LU classification through deep learning based innovations, and has great potential utility in a wide range of geospatial applications

Computational methods for quantifying surface structure and dynamics with Low-Energy Electron Microscopy

This thesis focuses on the development of computational approaches to quantify the observations of semiconductor surfaces with Low-Energy Electron Microscopy (LEEM). The real-time surface imaging with LEEM gives us dynamical observations with a suitable temporal and spacial resolution that is difficult to achieve with other microscopy techniques. It allows us to track surface structural dynamics on a nanoscopic scale and characterise them using Computer Vision methods. We monitor the surface of GaAs (001), where we observe a stable coexistence between the (6 x 6) and c(8 x 2) surface phases, as well as a metastable coexistence of those phases during growth. Using the LEEM imaging of the surface dynamics, we are then able to extract previously inaccessible parameters for the two phases. Through Computer Vision methods and computational algorithms, we develop data pipelines that enable the required accuracy and throughput in the analysis of our imaging data. With that, we extract fundamental surface parameters, such as the entropy and stress difference, as well as the step edge energy between the (6 x 6) and c(8 x 2) phases. We use these insights to clarify the long-standing stability question of the (6 x 6) phase and to explain the observed behaviour of the two phases. Furthermore, we use Machine Learning and Deep Learning techniques to develop an approach that streamlines the analysis of the complex and abstract imaging data from the Convergent Beam Low-Energy Electron Diffraction (CBLEED) technique. We show the high accuracy and performance of the developed models in finding surface structural parameters with sub-angstrom accuracy based on CBLEED images

Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.

This dissertation addresses two different problems: 1) coronal loop detection from solar images: and 2) salient contour group extraction from cluttered images. In the first part, we propose two different solutions to the coronal loop detection problem. The first solution is a block-based coronal loop mining method that detects coronal loops from solar images by dividing the solar image into fixed sized blocks, labeling the blocks as Loop or Non-Loop , extracting features from the labeled blocks, and finally training classifiers to generate learning models that can classify new image blocks. The block-based approach achieves 64% accuracy in IO-fold cross validation experiments. To improve the accuracy and scalability, we propose a contour-based coronal loop detection method that extracts contours from cluttered regions, then labels the contours as Loop and Non-Loop , and extracts geometric features from the labeled contours. The contour-based approach achieves 85% accuracy in IO-fold cross validation experiments, which is a 20% increase compared to the block-based approach. In the second part, we propose a method to extract semi-elliptical open curves from cluttered regions. Our method consists of the following steps: obtaining individual smooth contours along with their saliency measures; then starting from the most salient contour, searching for possible grouping options for each contour; and continuing the grouping until an optimum solution is reached. Our work involved the design and development of a complete system for coronal loop mining in solar images, which required the formulation of new Gestalt perceptual rules and a systematic methodology to select and combine them in a fully automated judicious manner using machine learning techniques that eliminate the need to manually set various weight and threshold values to define an effective cost function. After finding salient contour groups, we close the gaps within the contours in each group and perform B-spline fitting to obtain smooth curves. Our methods were successfully applied on cluttered solar images from TRACE and STEREO/SECCHI to discern coronal loops. Aerial road images were also used to demonstrate the applicability of our grouping techniques to other contour-types in other real applications

Finding spectral features for the early identification of biotic stress in plants

Early detection of biotic stress in plants is vital for precision crop protection, but hard to achieve. Prediction of plant diseases or weeds at an early stage has significant influence on the extent and effectiveness of crop protection measures. The precise measure depends on specific weeds and plant diseases and their economic thresholds. Weeds and plant diseases at an early stage, however, are difficult to identify. Non-invasive optical sensors with high resolution are promising for early detection of biotic stress. The data of these sensors, e.g. hyperspectral or fluorescence signatures, contain relevant information about the occurrence of pathogens. Shape parameters, derived from bispectral images, have enormous potential for an early identification of weeds in crops. The analysis of this high dimensional data for an identification of weeds and pathogens as early as possible is demanding as the sensor signal is affected by many influencing factors. Nevertheless, advanced methods of machine learning facilitate the interpretation of these signals. Whereas traditional statistics estimate the posterior probability of the class by probability distribution, machine learning methods provide algorithms for optimising prediction accuracy by the discriminant function. Machine learning methods with robust training algorithms play a key role in handling non-linear classification problems. This thesis presents an approach which integrates modern sensor techniques and advanced machine learning methods for an early detection and differentiation of plant diseases and weeds. Support vector machines (SVMs) equipped with non-linear kernels prove as effective and robust classifiers. Furthermore, it is shown that even a presymptomatic identification based on the combination of spectral vegetation indices is realised. Using well-established data analysis methods of this scientific field, this has not achieved so far. Identifying disease specific features from the underlying original high dimensional sensor data selection is conducted. The high dimensionality of data affords a careful selection of relevant and non-redundant features depending on classification problem and feature properties. In the case of fluorescence signatures an extraction of new features is necessary. In this context modelling of signal noise by an analytical description of the spectral signature improves the accuracy of classification substantially. In the case of weed discrimination accuracy is improved by exploiting the hierarchy of weed species. This thesis outlines the potential of SVMs, feature construction and feature selection for precision crop protection. A problem-specific extraction and selection of relevant features, in combination with task-oriented classification methods, is essential for robust identification of pathogens and weeds as early as possible.Früherkennung von biotischem Pflanzenstress ist für den Präzisionspflanzenschutz wesentlich, aber schwierig zu erreichen. Die Vorhersage von Pflanzenkrankheiten und Unkräutern in einem frühen Entwicklungsstadium hat signifikanten Einfluss auf das Ausmaß und die Effektivität einer Pflanzenschutzmaßnahme. Aufgrund der Abhängigkeit einer Maßnahme von der Art der Pflanzenkrankheit oder des Unkrauts und ihrer ökonomischer Schadschwelle ist eine präzise Identifizierung der Schadursache essentiell, aber gerade im Frühstadium durch die Ähnlichkeit der Schadbilder problematisch. Nicht-invasive optische Sensoren mit hoher Auflösung sind vielversprechend für eine Früherkennung von biotischem Pflanzenstress. Daten dieser Sensoren, beispielsweise Hyperspektral- oder Fluoreszenzspektren, enthalten relevante Informationen über das Auftreten von Pathogenen; Formparameter, abgeleitet aus bispektralen Bildern, zeigen großes Potential für die Früherkennung von Unkräutern in Kulturpflanzen. Die Analyse dieser hochdimensionalen Sensordaten unter Berücksichtigung vielfältiger Faktoren ist eine anspruchsvolle Herausforderung. Moderne Methoden des maschinellen Lernens bieten hier zielführende Möglichkeiten. Während die traditionelle Statistik die a-posteriori Wahrscheinlichkeit der Klasse basierend auf Wahrscheinlichkeitsverteilungen schätzt, verwenden maschinelle Lernverfahren Algorithmen für eine Optimierung der Vorhersagegenauigkeit auf Basis diskriminierender Funktionen. Grundlage zur Bearbeitung dieser nicht-linearen Klassi kationsprobleme sind robuste maschinelle Lernverfahren. Die vorliegende Dissertationsschrift zeigt, dass die Integration moderner Sensortechnik mit fortgeschrittenen Methoden des maschinellen Lernens eine Erkennung und Differenzierung von Pflanzenkrankheiten und Unkräutern ermöglicht. Einen wesentlichen Beitrag für eine effektive und robuste Klassifikation leisten Support Vektor Maschinen (SVMs) mit nicht-linearen Kernels. Weiterhin wird gezeigt, dass SVMs auf Basis spektraler Vegetationsindizes die Detektion von Pflanzenkrankheiten noch vor Auftreten visuell wahrnehmbarer Symptome ermöglichen. Dies wurde mit bekannten Verfahren noch nicht erreicht. Zur Identifikation krankheitsspezifischer Merkmale aus den zugrunde liegenden originären hochdimensionalen Sensordaten wurden Merkmale konstruiert und selektiert. Die Selektion ist sowohl vom Klassifikationsproblem als auch von den Eigenschaften der Merkmale abhängig. Im Fall von Fluoreszenzspektren war eine Extraktion von neuen Merkmalen notwendig. In diesem Zusammenhang trägt die Modellierung des Signalrauschens durch eine analytische Beschreibung der spektralen Signatur zur deutlichen Verbesserung der Klassifikationsgenauigkeit bei. Im Fall der Differenzierung von unterschiedlichen Unkräutern erhöht die Ausnutzung der Hierarchie der Unkrautarten die Genauigkeit signifikant. Diese Arbeit zeigt das Potential von Support Vektor Maschinen, Merkmalskonstruktion und Selektion für den Präzisionspflanzenschutz. Eine problemspezifische Extraktion und Selektion relevanter Merkmale in Verbindung mit sachbezogenen Klassifikationsmethoden ermöglichen eine robuste Identifikation von Pathogenen und Unkräutern zu einem sehr frühen Zeitpunkt