Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks

Bilateral filters have wide spread use due to their edge-preserving properties. The common use case is to manually choose a parametric filter type, usually a Gaussian filter. In this paper, we will generalize the parametrization and in particular derive a gradient descent algorithm so the filter parameters can be learned from data. This derivation allows to learn high dimensional linear filters that operate in sparsely populated feature spaces. We build on the permutohedral lattice construction for efficient filtering. The ability to learn more general forms of high-dimensional filters can be used in several diverse applications. First, we demonstrate the use in applications where single filter applications are desired for runtime reasons. Further, we show how this algorithm can be used to learn the pairwise potentials in densely connected conditional random fields and apply these to different image segmentation tasks. Finally, we introduce layers of bilateral filters in CNNs and propose bilateral neural networks for the use of high-dimensional sparse data. This view provides new ways to encode model structure into network architectures. A diverse set of experiments empirically validates the usage of general forms of filters

Depth-adaptive methodologies for 3D image caregorization.

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Image classification is an active topic of computer vision research. This topic deals with the learning of patterns in order to allow efficient classification of visual information. However, most research efforts have focused on 2D image classification. In recent years, advances of 3D imaging enabled the development of applications and provided new research directions. In this thesis, we present methodologies and techniques for image classification using 3D image data. We conducted our research focusing on the attributes and limitations of depth information regarding possible uses. This research led us to the development of depth feature extraction methodologies that contribute to the representation of images thus enhancing the recognition efficiency. We proposed a new classification algorithm that adapts to the need of image representations by implementing a scale-based decision that exploits discriminant parts of representations. Learning from the design of image representation methods, we introduced our own which describes each image by its depicting content providing more discriminative image representation. We also propose a dictionary learning method that exploits the relation of training features by assessing the similarity of features originating from similar context regions. Finally, we present our research on deep learning algorithms combined with data and techniques used in 3D imaging. Our novel methods provide state-of-the-art results, thus contributing to the research of 3D image classificatio

Research Towards High Speed Freeforming

Additive manufacturing (AM) methods are currently utilised for the manufacture of prototypes and low volume, high cost parts. This is because in most cases the high material costs and low volumetric deposition rates of AM parts result in higher per part cost than traditional manufacturing methods. This paper brings together recent research aimed at improving the economics of AM, in particular Extrusion Freeforming (EF). A new class of machine is described called High Speed Additive Manufacturing (HSAM) in which software, hardware and materials advances are aggregated. HSAM could be cost competitive with injection moulding for medium sized medium quantity parts. A general outline for a HSAM machine and supply chain is provided along with future required research

Investigation into adaptive slicing methodologies for additive manufacturing

Adaptive slicing is a methodology used to optimise the trade-off between build-time reduction and geometric accuracy improvement in additive manufacturing (AM). It works by varying decreasing layer thickness in sections of high curvature. However, current adaptive slicing methodologies all face the difficulty of adjusting layer thickness precisely according to the variations of the model’s geometry, thereby limiting the geometric accuracy improvement. This thesis tackles this difficulty by indicating the geometric variations of the model by evaluating the ratio of the volume of each sliced layer’s geometric deviation to the volume of its corresponding region in the digital model. This indication is accomplished because all the topological information of the corresponding region is considered in assessing the geometric deviation (volume) between each sliced layer and its corresponding region. Through having this precise indication to modify each layer thickness, this thesis aims to develop an adaptive slicing that can mitigate geometric inaccuracies (e.g. staircase effect and dimensional deviation) while balancing the build time. This slicing is evaluated using six different test models, compared with three current slicing methodologies (voxelisation-based, cusp height-based, and uniform slicing), and validated through computation and manufacturing. These validations all demonstrate that volume deviation-based slicing optimises the trade-off between build-time reduction and geometric accuracy improvement better than the other existing slicing methodologies. For example, it can reduce the build time by nearly half compared to other existing slicing methodologies assuming a similar degree of printed parts’ geometric accuracy. The improved trade-off optimised by volume deviation-based slicing can directly benefit the AM applications in the aerospace and medical industries. This is because current research has shown geometric inaccuracies are the primary cause of reducing energy efficiency (e.g. turbine blade and wind tunnel testing models) and having failed implants (e.g. hip and cranial implants, dental prostheses). In addition to improving the geometric accuracy of AM-constructed parts, volume deviation-based slicing may also be incorporated with non-planar layer slicing. Non-planar layer slicing is designed to mitigate the mechanical anisotropy of printed parts by using curved-sliced layers. By integrating volume deviation-based slicing with non-planar layer slicing, the thickness of each curved-sliced layer can be adjusted according to the model’s geometric variations and, therefore, has a possibility of reducing the geometric inaccuracies and mechanical anisotropy simultaneously.Open Acces