Geometric and photometric affine invariant image registration

This thesis aims to present a solution to the correspondence problem for the registration of wide-baseline images taken from uncalibrated cameras. We propose an affine invariant descriptor that combines the geometry and photometry of the scene to find correspondences between both views. The geometric affine invariant component of the descriptor is based on the affine arc-length metric, whereas the photometry is analysed by invariant colour moments. A graph structure represents the spatial distribution of the primitive features; i.e. nodes correspond to detected high-curvature points, whereas arcs represent connectivities by extracted contours. After matching, we refine the search for correspondences by using a maximum likelihood robust algorithm. We have evaluated the system over synthetic and real data. The method is endemic to propagation of errors introduced by approximations in the system.BAE SystemsSelex Sensors and Airborne System

Anatomical curve identification

Methods for capturing images in three dimensions are now widely available, with stereo-photogrammetry and laser scanning being two common approaches. In anatomical studies, a number of landmarks are usually identified manually from each of these images and these form the basis of subsequent statistical analysis. However, landmarks express only a very small proportion of the information available from the images. Anatomically defined curves have the advantage of providing a much richer expression of shape. This is explored in the context of identifying the boundary of breasts from an image of the female torso and the boundary of the lips from a facial image. The curves of interest are characterised by ridges or valleys. Key issues in estimation are the ability to navigate across the anatomical surface in three-dimensions, the ability to recognise the relevant boundary and the need to assess the evidence for the presence of the surface feature of interest. The first issue is addressed by the use of principal curves, as an extension of principal components, the second by suitable assessment of curvature and the third by change-point detection. P-spline smoothing is used as an integral part of the methods but adaptations are made to the specific anatomical features of interest. After estimation of the boundary curves, the intermediate surfaces of the anatomical feature of interest can be characterised by surface interpolation. This allows shape variation to be explored using standard methods such as principal components. These tools are applied to a collection of images of women where one breast has been reconstructed after mastectomy and where interest lies in shape differences between the reconstructed and unreconstructed breasts. They are also applied to a collection of lip images where possible differences in shape between males and females are of interest

Bounds for the genus of a normal surface

This paper gives sharp linear bounds on the genus of a normal surface in a triangulated compact, orientable 3--manifold in terms of the quadrilaterals in its cell decomposition---different bounds arise from varying hypotheses on the surface or triangulation. Two applications of these bounds are given. First, the minimal triangulations of the product of a closed surface and the closed interval are determined. Second, an alternative approach to the realisation problem using normal surface theory is shown to be less powerful than its dual method using subcomplexes of polytopes.Comment: 38 pages, 25 figure

Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain. Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive. Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform policy decisions and help direct humanitarian efforts. We propose a novel machine learning approach to extract large-scale socioeconomic indicators from high-resolution satellite imagery. The main challenge is that training data is very scarce, making it difficult to apply modern techniques such as Convolutional Neural Networks (CNN). We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy. We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction. The model learns filters identifying different terrains and man-made structures, including roads, buildings, and farmlands, without any supervision beyond nighttime lights. We demonstrate that these learned features are highly informative for poverty mapping, even approaching the predictive performance of survey data collected in the field.Comment: In Proc. 30th AAAI Conference on Artificial Intelligenc

FAB: Fast Angular Binary Descriptor for Matching Corner Points in Video Imagery

Image matching is a fundamental step in several computer vision applications where the requirement is fast, accurate, and robust matching of images in the presence of different transformations. Detection and more importantly description of low-level image features proved to be a more appropriate choice for this purpose, such as edges, corners, or blobs. Modern descriptors use binary values to store neighbourhood information of feature points for matching because binary descriptors are fast to compute and match. This paper proposes a descriptor called Fast Angular Binary (FAB) descriptor that illustrates the neighbourhood of a corner point using a binary vector. It is different from conventional descriptors because of selecting only the useful neighbourhood of corner point instead of the whole circular area of specific radius. The descriptor uses the angle of corner points to reduce the search space and increase the probability of finding an accurate match using binary descriptor. Experiments show that FAB descriptor’s performance is good, but the calculation and matching time is significantly less than BRIEF, the best known binary descriptor, and AMIE, a descriptor that uses entropy and average intensities of informative part of a corner point for the description

Bayesian data assimilation in shape registration

In this paper we apply a Bayesian framework to the problem of geodesic curve matching. Given a template curve, the geodesic equations provide a mapping from initial conditions\ud for the conjugate momentum onto topologically equivalent shapes. Here, we aim to recover the well defined posterior distribution on the initial momentum which gives rise to observed points on the target curve; this is achieved by explicitly including a reparameterisation in the formulation. Appropriate priors are chosen for the functions which together determine this field and the positions of the observation points, the initial momentum p0 and the reparameterisation vector field v, informed by regularity results about the forward model. Having done this, we illustrate how Maximum Likelihood Estimators (MLEs) can be used to find regions of high posterior density, but also how we can apply recently developed MCMC methods on function spaces to characterise the whole of the posterior density. These illustrative examples also include scenarios where the posterior distribution is multimodal and irregular, leading us to the conclusion that knowledge of a state of global maximal posterior density does not always give us the whole picture, and full posterior sampling can give better quantification of likely states and the overall uncertainty inherent in the problem

Information efficient 3D visual SLAM in unstructured domains

This paper presents a strategy for increasing the efficiency of simultaneous localisation and mapping (SLAM) in unknown and unstructured environments using a vision-based sensory package. Traditional feature-based SLAM, using either the Extended Kalman Filter (EKF) or its dual, the Extended Information Filter (EIF), leads to heavy computational costs while the environment expands and the number of features increases. In this paper we propose an algorithm to reduce computational cost for real-time systems by giving robots the 'intelligence' to select, out of the steadily collected data, the maximally informative observations to be used in the estimation process. We show that, although the actual evaluation of information gain for each frame introduces an additional computational cost, the overall efficiency is significantly increased by keeping the matrix compact. The noticeable advantage of this strategy is that the continuously gathered data is not heuristically segmented prior to be input to the filter. Quite the opposite, the scheme lends itself to be statistically optimal. © 2007 IEEE

Using machine learned features for robot ego-motion estimation through an event-camera

This thesis analyses the advantages offered by event-cameras in ego-motion estimation. Traditional cameras suffer from poor performance in low light conditions or high-speed motion. Event-cameras overcome these limitations by detecting and processing only the changes in the visual scene, offering a higher dynamic range and a lower power consumption. In particular, this thesis analyses a feature detection method based on machine learning that takes advantage of the peculiarities of this type of data, resulting in higher precision and longer feature tracks with respect to handcrafted methods. The inference pipeline is composed of a module repeated twice in sequence, formed by a Squeeze-and-Excite block and a ConvLSTM block with residual connection. It is followed by a final convolutional layer that provides the trajectories of the corners as a sequence of heatmaps. A novel training method is described and evaluated.This thesis analyses the advantages offered by event-cameras in ego-motion estimation. Traditional cameras suffer from poor performance in low light conditions or high-speed motion. Event-cameras overcome these limitations by detecting and processing only the changes in the visual scene, offering a higher dynamic range and a lower power consumption. In particular, this thesis analyses a feature detection method based on machine learning that takes advantage of the peculiarities of this type of data, resulting in higher precision and longer feature tracks with respect to handcrafted methods. The inference pipeline is composed of a module repeated twice in sequence, formed by a Squeeze-and-Excite block and a ConvLSTM block with residual connection. It is followed by a final convolutional layer that provides the trajectories of the corners as a sequence of heatmaps. A novel training method is described and evaluated