91 research outputs found
AI-Enabled Contextual Representations for Image-based Integration in Health and Safety
Recent advancements in the area of Artificial Intelligence (AI) have made it the field of choice for automatically processing and summarizing information in big-data domains such as high-resolution images. This approach, however, is not a one-size-fits-all solution, and must be tailored to each application. Furthermore, each application comes with its own unique set of challenges including technical variations, validation of AI solutions, and contextual information. These challenges are addressed in three human-health and safety related applications: (i) an early warning system of slope failures in open-pit mining operations; (ii) the modeling and characterization of 3D cell culture models imaged with confocal microscopy; and (iii) precision medicine of biomarker discovery from patients with glioblastoma multiforme through digital pathology. The methodologies and results in each of these domains show how tailor-made AI solutions can be used for automatically extracting and summarizing pertinent information from big-data applications for enhanced decision making
Anisotropic osmosis filtering for shadow removal in images
We present an anisotropic extension of the isotropic osmosis model that has
been introduced by Weickert et al.~(Weickert, 2013) for visual computing
applications, and we adapt it specifically to shadow removal applications. We
show that in the integrable setting, linear anisotropic osmosis minimises an
energy that involves a suitable quadratic form which models local directional
structures. In our shadow removal applications we estimate the local structure
via a modified tensor voting approach (Moreno, 2012) and use this information
within an anisotropic diffusion inpainting that resembles edge-enhancing
anisotropic diffusion inpainting (Weickert, 2006, Gali\'c, 2008). Our numerical
scheme combines the nonnegativity preserving stencil of Fehrenbach and Mirebeau
(Fehrenbach, 2014) with an exact time stepping based on highly accurate
polynomial approximations of the matrix exponential. The resulting anisotropic
model is tested on several synthetic and natural images corrupted by constant
shadows. We show that it outperforms isotropic osmosis, since it does not
suffer from blurring artefacts at the shadow boundaries
PDE-based Group Equivariant Convolutional Neural Networks
We present a PDE-based framework that generalizes Group equivariant
Convolutional Neural Networks (G-CNNs). In this framework, a network layer is
seen as a set of PDE-solvers where geometrically meaningful PDE-coefficients
become the layer's trainable weights. Formulating our PDEs on homogeneous
spaces allows these networks to be designed with built-in symmetries such as
rotation in addition to the standard translation equivariance of CNNs.
Having all the desired symmetries included in the design obviates the need to
include them by means of costly techniques such as data augmentation. We will
discuss our PDE-based G-CNNs (PDE-G-CNNs) in a general homogeneous space
setting while also going into the specifics of our primary case of interest:
roto-translation equivariance.
We solve the PDE of interest by a combination of linear group convolutions
and non-linear morphological group convolutions with analytic kernel
approximations that we underpin with formal theorems. Our kernel approximations
allow for fast GPU-implementation of the PDE-solvers, we release our
implementation with this article in the form of the LieTorch extension to
PyTorch, available at https://gitlab.com/bsmetsjr/lietorch . Just like for
linear convolution a morphological convolution is specified by a kernel that we
train in our PDE-G-CNNs. In PDE-G-CNNs we do not use non-linearities such as
max/min-pooling and ReLUs as they are already subsumed by morphological
convolutions.
We present a set of experiments to demonstrate the strength of the proposed
PDE-G-CNNs in increasing the performance of deep learning based imaging
applications with far fewer parameters than traditional CNNs.Comment: 27 pages, 18 figures. v2 changes: - mentioned KerCNNs - added section
Generalization of G-CNNs - clarification that the experiments utilized
automatic differentiation and SGD. v3 changes: - streamlined theoretical
framework - formulation and proof Thm.1 & 2 - expanded experiments. v4
changes: typos in Prop.5 and (20) v5/6 changes: minor revisio
Three-Dimensional Object Registration Using Wavelet Features
Recent developments in shape-based modeling and data acquisition have brought three-dimensional models to the forefront of computer graphics and visualization research. New data acquisition methods are producing large numbers of models in a variety of fields. Three-dimensional registration (alignment) is key to the useful application of such models in areas from automated surface inspection to cancer detection and surgery. The algorithms developed in this research accomplish automatic registration of three-dimensional voxelized models. We employ features in a wavelet transform domain to accomplish registration. The features are extracted in a multi-resolutional format, thus delineating features at various scales for robust and rapid matching. Registration is achieved by using a voting scheme to select peaks in sets of rotation quaternions, then separately identifying translation. The method is robust to occlusion, clutter, and noise. The efficacy of the algorithm is demonstrated through examples from solid modeling and medical imaging applications
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