33,671 research outputs found
Introducing Geometry in Active Learning for Image Segmentation
We propose an Active Learning approach to training a segmentation classifier
that exploits geometric priors to streamline the annotation process in 3D image
volumes. To this end, we use these priors not only to select voxels most in
need of annotation but to guarantee that they lie on 2D planar patch, which
makes it much easier to annotate than if they were randomly distributed in the
volume. A simplified version of this approach is effective in natural 2D
images. We evaluated our approach on Electron Microscopy and Magnetic Resonance
image volumes, as well as on natural images. Comparing our approach against
several accepted baselines demonstrates a marked performance increase
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis
Computer vision tasks are difficult because of the large variability in the
data that is induced by changes in light, background, partial occlusion as well
as the varying pose, texture, and shape of objects. Generative approaches to
computer vision allow us to overcome this difficulty by explicitly modeling the
physical image formation process. Using generative object models, the analysis
of an observed image is performed via Bayesian inference of the posterior
distribution. This conceptually simple approach tends to fail in practice
because of several difficulties stemming from sampling the posterior
distribution: high-dimensionality and multi-modality of the posterior
distribution as well as expensive simulation of the rendering process. The main
difficulty of sampling approaches in a computer vision context is choosing the
proposal distribution accurately so that maxima of the posterior are explored
early and the algorithm quickly converges to a valid image interpretation. In
this work, we propose to use a Bayesian Neural Network for estimating an image
dependent proposal distribution. Compared to a standard Gaussian random walk
proposal, this accelerates the sampler in finding regions of the posterior with
high value. In this way, we can significantly reduce the number of samples
needed to perform facial image analysis.Comment: Accepted to the Bayesian Deep Learning Workshop at NeurIPS 201
Simultaneous localization and map-building using active vision
An active approach to sensing can provide the focused measurement capability over a wide field of view which allows correctly formulated Simultaneous Localization and Map-Building (SLAM) to be implemented with vision, permitting repeatable long-term localization using only naturally occurring, automatically-detected features. In this paper, we present the first example of a general system for autonomous localization using active vision, enabled here by a high-performance stereo head, addressing such issues as uncertainty-based measurement selection, automatic map-maintenance, and goal-directed steering. We present varied real-time experiments in a complex environment.Published versio
Convex Hulls under Uncertainty
We study the convex-hull problem in a probabilistic setting, motivated by the
need to handle data uncertainty inherent in many applications, including sensor
databases, location-based services and computer vision. In our framework, the
uncertainty of each input site is described by a probability distribution over
a finite number of possible locations including a \emph{null} location to
account for non-existence of the point. Our results include both exact and
approximation algorithms for computing the probability of a query point lying
inside the convex hull of the input, time-space tradeoffs for the membership
queries, a connection between Tukey depth and membership queries, as well as a
new notion of \some-hull that may be a useful representation of uncertain
hulls
Prototype system for supporting the incremental modelling of vague geometric configurations
In this paper the need for Intelligent Computer Aided Design (Int.CAD) to jointly support design and learning assistance is introduced. The paper focuses on presenting and exploring the possibility of realizing learning assistance in Int.CAD by introducing a new concept called Shared Learning. Shared Learning is proposed to empower CAD tools with more useful learning capabilities than that currently available and thereby provide a stronger interaction of learning between a designer and a computer. Controlled computational learning is proposed as a means whereby the Shared Learning concept can be realized. The viability of this new concept is explored by using a system called PERSPECT. PERSPECT is a preliminary numerical design tool aimed at supporting the effective utilization of numerical experiential knowledge in design. After a detailed discussion of PERSPECT's numerical design support, the paper presents the results of an evaluation that focuses on PERSPECT's implementation of controlled computational learning and ability to support a designer's need to learn. The paper then discusses PERSPECT's potential as a tool for supporting the Shared Learning concept by explaining how a designer and PERSPECT can jointly learn. There is still much work to be done before the full potential of Shared Learning can be realized. However, the authors do believe that the concept of Shared Learning may hold the key to truly empowering learning in Int.CAD
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