459 research outputs found
Perimeter detection in sketched drawings of polyhedral shapes
Ponència presentada al STAG17: Smart tools and Applications in Graphics, celebrat a Catania (Itàlia) 11-12 setembre 2017This paper describes a new “envelope” approach for detecting object perimeters in line-drawings vectorised from
sketches of polyhedral objects.
Existing approaches for extracting contours from digital images are unsuitable for Sketch-Based Modelling, as they
calculate where the contour is, but not which elements of the line-drawing belong to it.
In our approach, the perimeter is described in terms of lines and junctions (including intersections and T-junctions)
of the original line drawing
An improved cosmological parameter inference scheme motivated by deep learning
Dark matter cannot be observed directly, but its weak gravitational lensing
slightly distorts the apparent shapes of background galaxies, making weak
lensing one of the most promising probes of cosmology. Several observational
studies have measured the effect, and there are currently running, and planned
efforts to provide even larger, and higher resolution weak lensing maps. Due to
nonlinearities on small scales, the traditional analysis with two-point
statistics does not fully capture all the underlying information. Multiple
inference methods were proposed to extract more details based on higher order
statistics, peak statistics, Minkowski functionals and recently convolutional
neural networks (CNN). Here we present an improved convolutional neural network
that gives significantly better estimates of and
cosmological parameters from simulated convergence maps than the state of art
methods and also is free of systematic bias. We show that the network exploits
information in the gradients around peaks, and with this insight, we construct
a new, easy-to-understand, and robust peak counting algorithm based on the
'steepness' of peaks, instead of their heights. The proposed scheme is even
more accurate than the neural network on high-resolution noiseless maps. With
shape noise and lower resolution its relative advantage deteriorates, but it
remains more accurate than peak counting
Generalized Boundaries from Multiple Image Interpretations
Boundary detection is essential for a variety of computer vision tasks such
as segmentation and recognition. In this paper we propose a unified formulation
and a novel algorithm that are applicable to the detection of different types
of boundaries, such as intensity edges, occlusion boundaries or object category
specific boundaries. Our formulation leads to a simple method with
state-of-the-art performance and significantly lower computational cost than
existing methods. We evaluate our algorithm on different types of boundaries,
from low-level boundaries extracted in natural images, to occlusion boundaries
obtained using motion cues and RGB-D cameras, to boundaries from
soft-segmentation. We also propose a novel method for figure/ground
soft-segmentation that can be used in conjunction with our boundary detection
method and improve its accuracy at almost no extra computational cost
Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene
The goal of this paper is to take a single 2D image of a scene and recover
the 3D structure in terms of a small set of factors: a layout representing the
enclosing surfaces as well as a set of objects represented in terms of shape
and pose. We propose a convolutional neural network-based approach to predict
this representation and benchmark it on a large dataset of indoor scenes. Our
experiments evaluate a number of practical design questions, demonstrate that
we can infer this representation, and quantitatively and qualitatively
demonstrate its merits compared to alternate representations.Comment: Project url with code: https://shubhtuls.github.io/factored3
- …