2,920 research outputs found
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Symbiotic deep learning for medical image analysis with applications in real-time diagnosis for fetal ultrasound screening
The last hundred years have seen a monumental rise in the power and capability of machines to
perform intelligent tasks in the stead of previously human operators. This rise is not expected
to slow down any time soon and what this means for society and humanity as a whole remains
to be seen. The overwhelming notion is that with the right goals in mind, the growing influence
of machines on our every day tasks will enable humanity to give more attention to the truly
groundbreaking challenges that we all face together. This will usher in a new age of human
machine collaboration in which humans and machines may work side by side to achieve greater
heights for all of humanity. Intelligent systems are useful in isolation, but the true benefits of
intelligent systems come to the fore in complex systems where the interaction between humans
and machines can be made seamless, and it is this goal of symbiosis between human and machine
that may democratise complex knowledge, which motivates this thesis. In the recent past, datadriven
methods have come to the fore and now represent the state-of-the-art in many different
fields. Alongside the shift from rule-based towards data-driven methods we have also seen a
shift in how humans interact with these technologies. Human computer interaction is changing
in response to data-driven methods and new techniques must be developed to enable the same
symbiosis between man and machine for data-driven methods as for previous formula-driven
technology.
We address five key challenges which need to be overcome for data-driven human-in-the-loop
computing to reach maturity. These are (1) the ’Categorisation Challenge’ where we examine
existing work and form a taxonomy of the different methods being utilised for data-driven
human-in-the-loop computing; (2) the ’Confidence Challenge’, where data-driven methods must
communicate interpretable beliefs in how confident their predictions are; (3) the ’Complexity
Challenge’ where the aim of reasoned communication becomes increasingly important as the
complexity of tasks and methods to solve also increases; (4) the ’Classification Challenge’ in
which we look at how complex methods can be separated in order to provide greater reasoning
in complex classification tasks; and finally (5) the ’Curation Challenge’ where we challenge the
assumptions around bottleneck creation for the development of supervised learning methods.Open Acces
Learning Distributions of Functions on a Continuous Time Domain
This work presents several contributions on the topic of learning representations of function spaces, as well as on learning the dynamics of glioma growth as a particular instance thereof. We begin with two preparatory efforts, showing how expert knowledge can be leveraged efficiently in an interactive segmentation context, and presenting a proof of concept for inferring non-deterministic glioma growth patterns purely from data. The remainder of our work builds upon the framework of Neural Processes. We show how these models represent function spaces and discover that they can implicitly decompose the space into different frequency components, not unlike a Fourier transform. In this context we derive an upper bound on the maximum signal frequency Neural Processes can represent and show how to
control the learned representations to only contain certain frequencies. We continue with an improvement of a more recent addition to the Neural Process family called ConvCNP, which we combine with a Gaussian Process to make it non-deterministic and to improve generalization. Finally, we show how to perform segmentation in the Neural Process framework by extending a typical segmentation
architecture with spatio-temporal attention. The resulting model can interpolate complex spatial variations of segmentations over time and, applied to glioma growth, it is able to represent multiple temporally consistent growth trajectories, exhibiting realistic and diverse spatial
growth patterns
Visual object category discovery in images and videos
textThe current trend in visual recognition research is to place a strict division between the supervised and unsupervised learning paradigms, which is problematic for two main reasons. On the one hand, supervised methods require training data for each and every category that the system learns; training data may not always be available and is expensive to obtain. On the other hand, unsupervised methods must determine the optimal visual cues and distance metrics that distinguish one category from another to group images into semantically meaningful categories; however, for unlabeled data, these are unknown a priori.
I propose a visual category discovery framework that transcends the two paradigms and learns accurate models with few labeled exemplars. The main insight is to automatically focus on the prevalent objects in images and videos, and learn models from them for category grouping, segmentation, and summarization.
To implement this idea, I first present a context-aware category discovery framework that discovers novel categories by leveraging context from previously learned categories. I devise a novel object-graph descriptor to model the interaction between a set of known categories and the unknown to-be-discovered categories, and group regions that have similar appearance and similar object-graphs. I then present a collective segmentation framework that simultaneously discovers the segmentations and groupings of objects by leveraging the shared patterns in the unlabeled image collection. It discovers an ensemble of representative instances for each unknown category, and builds top-down models from them to refine the segmentation of the remaining instances. Finally, building on these techniques, I show how to produce compact visual summaries for first-person egocentric videos that focus on the important people and objects. The system leverages novel egocentric and high-level saliency features to predict important regions in the video, and produces a concise visual summary that is driven by those regions.
I compare against existing state-of-the-art methods for category discovery and segmentation on several challenging benchmark datasets. I demonstrate that we can discover visual concepts more accurately by focusing on the prevalent objects in images and videos, and show clear advantages of departing from the status quo division between the supervised and unsupervised learning paradigms. The main impact of my thesis is that it lays the groundwork for building large-scale visual discovery systems that can automatically discover visual concepts with minimal human supervision.Electrical and Computer Engineerin
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