Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

3D-POLY: A Robot Vision System for Recognizing Objects in Occluded Environments

The two factors that determine the time complexity associated with model-driven interpretation of range maps are: I) the particular strategy used for the generation of object hypotheses; and 2) the manner in which both the model and the sensed data are organized, data organization being a primary determinant of the efficiency of verification of a given hypothesis. In this report, we present 3D-POLY, a working system for recognizing objects in the presence of occlusion and against cluttered backgrounds. The time complexity of this system is only O(n2) for single object recognition, where n is the number of features on the object. The most novel aspect of this system is the manner in which the feature data are organized for the models. We use a data structure called the feature sphere for the purpose. We will present efficient algorithms for assigning a feature to its proper place on a feature sphere, and for extracting the neighbors of a given feature from the feature sphere representation. For hypothesis generation, we use local feature sets, a notion similar to those used before us by Bolles, Shirai and others. The combination of the feature sphere idea for streamlining verification and the local feature sets for hypothesis generation results in a system whose time complexity has a polynomial bound. In addition to recognizing objects in occluded environments, 3D-POLY also possesses model learning capability. Model learning consists of looking at a model object from different views and integrating the resulting information. The 3D-POLY system also contains utilities for range image segmentation and classification of scene surfaces

Analyse de forme appliquée à des modèles CAO B-Rep pour extraire des symétries locales et globales

Symmetry properties of objects described as B-Rep CAD models are analyzed locally as well as globally through an approach of type divide-and-conquer. The boundary of the object is defined using canonical surfaces frequently used when shaping mechanical components. Then, the first phase consists in generating maximal faces and edges that are independent from the object modelling process but that preserve its symmetry properties. These faces and edges form infinite sets of points that are processed globally. The second phase is the division one that creates candidate symmetry planes and axes attached to the previous maximal edges and faces. Finally, comes the propagation step of these candidate symmetry planes and axes forming the conquer phase that determines the local as well as the global symmetries of the object while characterizing its asymmetric areas.Les propriétés de symétrie d'un objet représenté sous la forme d'un modèle B-Rep CAO sont analysées localement et globalement à travers une approche de type diviser pour conquérir. La surface frontière de l'objet est décrite à partir de surfaces canoniques fréquemment utilisées dans les formes de composants mécaniques. La première phase de l'analyse consiste en la génération de faces et d'arêtes maximales indépendantes du processus de modélisation de l'objet mais préservant ses propriétés de symétrie. Ces faces et arêtes constituent des ensembles infinis de points traités globalement. La seconde phase est l'étape de division consistant en la création de plan et axes de symétrie de candidats pour les faces et arêtes maximales générées précédemment. Enfin, suit l'étape de propagation de ces plans et axes de symétrie représentant la phase de conquête et déterminant les propriétés de symétrie locales et globales de l'objet et caractérisant ses zones non-symétriques

Dust in the centres of galaxies: observations and modelling of dust in the nucleus of NGC1068 and in the galactic centre

As the “archetypal” Seyfert II galaxy, NGC1068 is one o f the best-studied dusty objects in the sky. Yet — in comm on with Seyfert galaxies in general — the properties of its dust are not at all well-known. To remedy this situation I have modelled the IR polarisation and 3.4/rm dust absorption feature observed in the nucleus of NGC1068 and com pared the results with new and existing spectroscopy and polarimetry. I find that to reproduce the observed polarisation, the aligned dust grains in this AGN must be larger than those seen in the Galactic diffuse ISM and molecular clouds. I am also led to conclude that the polarising grains in NG C1068 do not take the form of silicate cores with organic mantles, a model which has been widely used to describe dust in the diffuse ISM of our Galaxy. The organic fraction of the dust instead exists as some form of small, nonpolarising grain, and its 3.4/rm feature shows that it is chemically very similar to carbonaceous dust in other, very different environments. The interpretation of this is not yet clear, but it is consistent with a common formation site and mechanism for the carrier of the band seen in many environments, which is then resistant to processing in the ISM . I have also applied these models to a Galactic centre line of sight, again finding that the core-mantle dust grain model is unlikely to be a valid representation of this dust

Fruit Detection and Tree Segmentation for Yield Mapping in Orchards

Accurate information gathering and processing is critical for precision horticulture, as growers aim to optimise their farm management practices. An accurate inventory of the crop that details its spatial distribution along with health and maturity, can help farmers efficiently target processes such as chemical and fertiliser spraying, crop thinning, harvest management, labour planning and marketing. Growers have traditionally obtained this information by using manual sampling techniques, which tend to be labour intensive, spatially sparse, expensive, inaccurate and prone to subjective biases. Recent advances in sensing and automation for field robotics allow for key measurements to be made for individual plants throughout an orchard in a timely and accurate manner. Farmer operated machines or unmanned robotic platforms can be equipped with a range of sensors to capture a detailed representation over large areas. Robust and accurate data processing techniques are therefore required to extract high level information needed by the grower to support precision farming. This thesis focuses on yield mapping in orchards using image and light detection and ranging (LiDAR) data captured using an unmanned ground vehicle (UGV). The contribution is the framework and algorithmic components for orchard mapping and yield estimation that is applicable to different fruit types and orchard configurations. The framework includes detection of fruits in individual images and tracking them over subsequent frames. The fruit counts are then associated to individual trees, which are segmented from image and LiDAR data, resulting in a structured spatial representation of yield. The first contribution of this thesis is the development of a generic and robust fruit detection algorithm. Images captured in the outdoor environment are susceptible to highly variable external factors that lead to significant appearance variations. Specifically in orchards, variability is caused by changes in illumination, target pose, tree types, etc. The proposed techniques address these issues by using state-of-the-art feature learning approaches for image classification, while investigating the utility of orchard domain knowledge for fruit detection. Detection is performed using both pixel-wise classification of images followed instance segmentation, and bounding-box regression approaches. The experimental results illustrate the versatility of complex deep learning approaches over a multitude of fruit types. The second contribution of this thesis is a tree segmentation approach to detect the individual trees that serve as a standard unit for structured orchard information systems. The work focuses on trellised trees, which present unique challenges for segmentation algorithms due to their intertwined nature. LiDAR data are used to segment the trellis face, and to generate proposals for individual trees trunks. Additional trunk proposals are provided using pixel-wise classification of the image data. The multi-modal observations are fine-tuned by modelling trunk locations using a hidden semi-Markov model (HSMM), within which prior knowledge of tree spacing is incorporated. The final component of this thesis addresses the visual occlusion of fruit within geometrically complex canopies by using a multi-view detection and tracking approach. Single image fruit detections are tracked over a sequence of images, and associated to individual trees or farm rows, with the spatial distribution of the fruit counting forming a yield map over the farm. The results show the advantage of using multi-view imagery (instead of single view analysis) for fruit counting and yield mapping. This thesis includes extensive experimentation in almond, apple and mango orchards, with data captured by a UGV spanning a total of 5 hectares of farm area, over 30 km of vehicle traversal and more than 7,000 trees. The validation of the different processes is performed using manual annotations, which includes fruit and tree locations in image and LiDAR data respectively. Additional evaluation of yield mapping is performed by comparison against fruit counts on trees at the farm and counts made by the growers post-harvest. The framework developed in this thesis is demonstrated to be accurate compared to ground truth at all scales of the pipeline, including fruit detection and tree mapping, leading to accurate yield estimation, per tree and per row, for the different crops. Through the multitude of field experiments conducted over multiple seasons and years, the thesis presents key practical insights necessary for commercial development of an information gathering system in orchards

The Role of Knowledge in Visual Shape Representation

This report shows how knowledge about the visual world can be built into a shape representation in the form of a descriptive vocabulary making explicit the important geometrical relationships comprising objects' shapes. Two computational tools are offered: (1) Shapestokens are placed on a Scale-Space Blackboard, (2) Dimensionality-reduction captures deformation classes in configurations of tokens. Knowledge lies in the token types and deformation classes tailored to the constraints and regularities ofparticular shape worlds. A hierarchical shape vocabulary has been implemented supporting several later visual tasks in the two-dimensional shape domain of the dorsal fins of fishes

Representing junctions through asymmetric tensor diffusion

Gradient-based junctions form key features in such applications as object classification, motion segmentation, and image enhancement. Asymmetric junctions arise from the merging of an odd number of contour end-points such as at a 'Y' junction. Without an asymmetric representation of such a structure, it will be identified in the same category as 'X' junctions. This has severe consequences when distinguishing between features in object classification, discerning occlusion from disocclusion in motion segmentation and in properly modeling smoothing boundaries in image enhancement.Current junction analysis methods include convolution, which applies a mask over a sub-region of the image, and diffusion, which propagates gradient information from point-to-point based on a set of rules.A novel method is proposed that results in an improved approximation of the underlying contours, through the use of asymmetric junctions. The method combines the ability to represent asymmetric information, as do a number of convolution methods, with the robustness of local support obtained from diffusion schemes. This work investigates several different design paradigms of the asymmetric tensor diffusion algorithm. The proposed approach proved superior to existing techniques by properly accounting for asymmetric junctions over a wide range of scenarios

Vision systems for a mobile robot based on line detection using the Hough Transform and artificial neural networks.

This project contributes to the problem of mobile robot self-navigation within a rectilinear framework based on visual data. It proposes a number of vision systems based on detection of straight lines in images captured by a robot using the Hough transform and artificial neural networks as core algorithms. The Hough transform is a robust method for detection of basic features (Boyce et al 1987). However, it is so computationally demanding that it is not commonly used in real time applications and applications which utilise anything but small images (Song and Lyu 2005). (Dempsey and McVey 1992) have suggested that this problem might be resolved if the Hough transform were implemented with artificial neural networks. This project investigates the feasibility of systems using these core algorithms, and systems that are hybrids of them. Prior to application of the core algorithms to a captured image, various stages of pre-processing are carried out including resizing for optimum results, edgedetection, and edge thinning using an adaptation of the thinning method of (Park, 2000) proposed by this work. An analysis of the costs and benefits of thinning as part of pre-processing has also been performed. The Hough transform based system, which has been largely successful, has involved a number of new approaches. These include a peak detection scheme; post-processing schemes which find valid sub-lines of lines found by the peak detection process, and establish which high-level features these sub-lines represent; and an appropriate navigation scheme. Two artificial neural network systems were designed based on lines detection and sub-lines detection respectively. The first was able to detect long lines, but not shorter (even though navigationally important) lines, and so was aborted. The second system has two major stages. Networks of stage 1 developed to detect sub-lines in sub-images derived by breaking down the original images, did so passibly well. A network in stage 2 designed to use the results of stage 1 to guide the robots motion did not do so well for most test images. The networks of stage 1, however, have been helpful with development of a hybrid vision system. Suggestions have been made on how this work can be furthered