Investigations on skeleton completeness for skeleton-based shape matching

Skeleton is an important shape descriptor for deformable shape matching, because it integrates both geometrical and topological features of a shape. As the skeletonisation process often generates redundant skeleton branches that may seriously disturb the skeleton matching and cause high computational complexity, skeleton pruning is required to remove the inaccurate or redundant branches while preserving the essential topology of the original skeleton. However, pruning approaches normally require manual intervention to produce visually complete skeletons. As different people may have different perceptions for identifying visually complete skeletons, it is unclear how much the accuracy of skeleton-based shape matching is influenced by human selection. Moreover, it is also unclear how skeleton completeness impacts the accuracy of skeleton-based shapematching. We investigate here these two questions in a structured way. In addition, we present experimental evidence to show that it is possible to do automatic skeleton pruning while maintaining the matching accuracy by estimating the approximate pruning power of each shape

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Automated detection of proliferative retinopathy in clinical practice

Timely intervention for diabetic retinopathy (DR) lessens the possibility of blindness and can save considerable costs to health systems. To ensure that interventions are timely and effective requires methods of screening and monitoring pathological changes, including assessing outcomes. Fractal analysis, one method that has been studied for assessing DR, is potentially relevant in today’s world of telemedicine because it provides objective indices from digital images of complex patterns such as are seen in retinal vasculature, which is affected in DR. We introduce here a protocol to distinguish between nonproliferative (NPDR) and proliferative (PDR) changes in retinal vasculature using a fractal analysis method known as local connected dimension (Dconn) analysis. The major finding is that compared to other fractal analysis methods, Dconn analysis better differentiates NPDR from PDR (p = 0.05). In addition, we are the first to show that fractal analysis can be used to differentiate between NPDR and PDR using automated vessel identification. Overall, our results suggest this protocol can complement existing methods by including an automated and objective measure obtainable at a lower level of expertise that experts can then use in screening for and monitoring DR

Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies

In motion analysis and understanding it is important to be able to fit a suitable model or structure to the temporal series of observed data, in order to describe motion patterns in a compact way, and to discriminate between them. In an unsupervised context, i.e., no prior model of the moving object(s) is available, such a structure has to be learned from the data in a bottom-up fashion. In recent times, volumetric approaches in which the motion is captured from a number of cameras and a voxel-set representation of the body is built from the camera views, have gained ground due to attractive features such as inherent view-invariance and robustness to occlusions. Automatic, unsupervised segmentation of moving bodies along entire sequences, in a temporally-coherent and robust way, has the potential to provide a means of constructing a bottom-up model of the moving body, and track motion cues that may be later exploited for motion classification. Spectral methods such as locally linear embedding (LLE) can be useful in this context, as they preserve "protrusions", i.e., high-curvature regions of the 3D volume, of articulated shapes, while improving their separation in a lower dimensional space, making them in this way easier to cluster. In this paper we therefore propose a spectral approach to unsupervised and temporally-coherent body-protrusion segmentation along time sequences. Volumetric shapes are clustered in an embedding space, clusters are propagated in time to ensure coherence, and merged or split to accommodate changes in the body's topology. Experiments on both synthetic and real sequences of dense voxel-set data are shown. This supports the ability of the proposed method to cluster body-parts consistently over time in a totally unsupervised fashion, its robustness to sampling density and shape quality, and its potential for bottom-up model constructionComment: 31 pages, 26 figure

Vision based system for detecting and counting mobility aids in surveillance videos

Automatic surveillance video analysis is popular among computer vision researchers due to its wide range of applications that require automated systems. Automated systems are to replace manual analysis of videos which is tiresome, expensive, and time-consuming. Image and video processing techniques are often used in the design of automatic detection and monitoring systems. Compared with normal indoor videos, outdoor surveillance videos are often difficult to process due to the uncontrolled environment, camera angle, and varying lighting and weather conditions. This research aims to contribute to the computer vision field by proposing an object detection and tracking algorithm that can handle multi-object and multi-class scenarios. The problem is solved by developing an application to count disabled pedestrians in surveillance videos by automatically detecting and tracking mobility aids and pedestrians. The application demonstrates that the proposed ideas achieve the desired outcomes. There are extensive studies on pedestrian detection and gait analysis in the computer vision field, but limited work is carried out on identifying disabled pedestrians or mobility aids. Detection of mobility aids in videos is challenging since the disabled person often occludes mobility aids and visibility of mobility aid depends on the direction of the walk with respect to the camera. For example, a walking stick is visible most times in front-on view while it is occluded when it happens to be on the walker's rear side. Furthermore, people use various mobility aids and their make and type changes with time as technology advances. The system should detect the majority of mobility aids to report reliable counting data. The literature review revealed that no system exists for detecting disabled pedestrians or mobility aids in surveillance videos. A lack of annotated image data containing mobility aids is also an obstacle to developing a machine-learning-based solution to detect mobility aids. In the first part of this thesis, we explored moving pedestrians' video data to extract the gait signals using manual and automated procedures. Manual extraction involved marking the pedestrians' head and leg locations and analysing those signals in the time domain. Analysis of stride length and velocity features indicate an abnormality if a walker is physically disabled. The automated system is built by combining the \acrshort{yolo} object detector, GMM based foreground modelling and star skeletonisation in a pipeline to extract the gait signal. The automated system failed to recognise a disabled person from its gait due to poor localisation by \acrshort{yolo}, incorrect segmentation and silhouette extraction due to moving backgrounds and shadows. The automated gait analysis approach failed due to various factors including environmental constraints, viewing angle, occlusions, shadows, imperfections in foreground modelling, object segmentation and silhouette extraction. In the later part of this thesis, we developed a CNN based approach to detect mobility aids and pedestrians. The task of identifying and counting disabled pedestrians in surveillance videos is divided into three sub-tasks: mobility aid and person detection, tracking and data association of detected objects, and counting healthy and disabled pedestrians. A modern object detector called YOLO, an improved data association algorithm (SORT), and a new pairing approach are applied to complete the three sub-tasks. Improvement of the SORT algorithm and introducing a pairing approach are notable contributions to the computer vision field. The SORT algorithm is strictly one class and without an object counting feature. SORT is enhanced to be multi-class and able to track accelerating or temporarily occluded objects. The pairing strategy associates a mobility aid with the nearest pedestrian and monitors them over time to see if the pair is reliable. A reliable pair represents a disabled pedestrian and counting reliable pairs calculates the number of disabled people in the video. The thesis also introduces an image database that was gathered as part of this study. The dataset comprises 5819 images belonging to eight different object classes, including five mobility aids, pedestrians, cars, and bicycles. The dataset was needed to train a CNN that can detect mobility aids in videos. The proposed mobility aid counting system is evaluated on a range of surveillance videos collected from outdoors with real-world scenarios. The results prove that the proposed solution offers a satisfactory performance in picking mobility aids from outdoor surveillance videos. The counting accuracy of 94% on test videos meets the design goals set by the advocacy group that need this application. Most test videos had objects from multiple classes in them. The system detected five mobility aids (wheelchair, crutch, walking stick, walking frame and mobility scooter), pedestrians and two distractors (car and bicycle). The training system on distractors' classes was to ensure the system can distinguish objects that are similar to mobility aids from mobility aids. In some cases, the convolutional neural network reports a mobility aid with an incorrect type. For example, the shape of crutch and stick are very much alike, and therefore, the system confuses one with the other. However, it does not affect the final counts as the aim was to get the overall counts of mobility aids (of any type) and determining the exact type of mobility aid is optional

Towards parameter-less 3D mesh segmentation

This thesis focuses on the 3D mesh segmentation process. The research demonstrated how the process can be done in a parameterless approach which allows full automation with accurate results. Applications of this research include, but not limited to, 3D search engines, 3D character animation, robotics environment recognition, and augmented reality

Colour morphology and its approaches

Mathematical morphology was first applied to binary images and readily extended to grey-level images. In extending mathematical morphology to colour it is difficult to define a suitable unambiguous ordering. We present two complete ordering schemes based on colour difference and similarity ordering for colour morphology. A novel colour difference formula is first introduced. This colour difference formula is based on colour extrema derived from a simple physical model of image formation and avoids the more arbitrary mathematical and perceptual definitions previously reported. Moreover, we define similarity criteria as the basis for mathematical morphology that can be used with flat and non-flat structuring elements. The proposed orderings meet the properties of mathematical morphology, and provide a harmonised approach for binary, grey-level and colour morphology. A comparison of ordering schemes for dilation, erosion, opening, closing and filtering operator shows the colour difference-based ordering presented here to be at least as good as other ordering schemes and better than some of the well principled, previously reported methods in not generating artefacts and reducing image noise. Additionally, the development of a similarity-based ordering to perform morphological gradient and Hit-or-Miss transforms for colour images is presented