1,011 research outputs found
Automated detection of extended sources in radio maps: progress from the SCORPIO survey
Automated source extraction and parameterization represents a crucial
challenge for the next-generation radio interferometer surveys, such as those
performed with the Square Kilometre Array (SKA) and its precursors. In this
paper we present a new algorithm, dubbed CAESAR (Compact And Extended Source
Automated Recognition), to detect and parametrize extended sources in radio
interferometric maps. It is based on a pre-filtering stage, allowing image
denoising, compact source suppression and enhancement of diffuse emission,
followed by an adaptive superpixel clustering stage for final source
segmentation. A parameterization stage provides source flux information and a
wide range of morphology estimators for post-processing analysis. We developed
CAESAR in a modular software library, including also different methods for
local background estimation and image filtering, along with alternative
algorithms for both compact and diffuse source extraction. The method was
applied to real radio continuum data collected at the Australian Telescope
Compact Array (ATCA) within the SCORPIO project, a pathfinder of the ASKAP-EMU
survey. The source reconstruction capabilities were studied over different test
fields in the presence of compact sources, imaging artefacts and diffuse
emission from the Galactic plane and compared with existing algorithms. When
compared to a human-driven analysis, the designed algorithm was found capable
of detecting known target sources and regions of diffuse emission,
outperforming alternative approaches over the considered fields.Comment: 15 pages, 9 figure
A Framework for Symmetric Part Detection in Cluttered Scenes
The role of symmetry in computer vision has waxed and waned in importance
during the evolution of the field from its earliest days. At first figuring
prominently in support of bottom-up indexing, it fell out of favor as shape
gave way to appearance and recognition gave way to detection. With a strong
prior in the form of a target object, the role of the weaker priors offered by
perceptual grouping was greatly diminished. However, as the field returns to
the problem of recognition from a large database, the bottom-up recovery of the
parts that make up the objects in a cluttered scene is critical for their
recognition. The medial axis community has long exploited the ubiquitous
regularity of symmetry as a basis for the decomposition of a closed contour
into medial parts. However, today's recognition systems are faced with
cluttered scenes, and the assumption that a closed contour exists, i.e. that
figure-ground segmentation has been solved, renders much of the medial axis
community's work inapplicable. In this article, we review a computational
framework, previously reported in Lee et al. (2013), Levinshtein et al. (2009,
2013), that bridges the representation power of the medial axis and the need to
recover and group an object's parts in a cluttered scene. Our framework is
rooted in the idea that a maximally inscribed disc, the building block of a
medial axis, can be modeled as a compact superpixel in the image. We evaluate
the method on images of cluttered scenes.Comment: 10 pages, 8 figure
Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
During the last half decade, convolutional neural networks (CNNs) have
triumphed over semantic segmentation, which is one of the core tasks in many
applications such as autonomous driving. However, to train CNNs requires a
considerable amount of data, which is difficult to collect and laborious to
annotate. Recent advances in computer graphics make it possible to train CNNs
on photo-realistic synthetic imagery with computer-generated annotations.
Despite this, the domain mismatch between the real images and the synthetic
data cripples the models' performance. Hence, we propose a curriculum-style
learning approach to minimize the domain gap in urban scenery semantic
segmentation. The curriculum domain adaptation solves easy tasks first to infer
necessary properties about the target domain; in particular, the first task is
to learn global label distributions over images and local distributions over
landmark superpixels. These are easy to estimate because images of urban scenes
have strong idiosyncrasies (e.g., the size and spatial relations of buildings,
streets, cars, etc.). We then train a segmentation network while regularizing
its predictions in the target domain to follow those inferred properties. In
experiments, our method outperforms the baselines on two datasets and two
backbone networks. We also report extensive ablation studies about our
approach.Comment: This is the extended version of the ICCV 2017 paper "Curriculum
Domain Adaptation for Semantic Segmentation of Urban Scenes" with additional
GTA experimen
Machine learning of hierarchical clustering to segment 2D and 3D images
We aim to improve segmentation through the use of machine learning tools
during region agglomeration. We propose an active learning approach for
performing hierarchical agglomerative segmentation from superpixels. Our method
combines multiple features at all scales of the agglomerative process, works
for data with an arbitrary number of dimensions, and scales to very large
datasets. We advocate the use of variation of information to measure
segmentation accuracy, particularly in 3D electron microscopy (EM) images of
neural tissue, and using this metric demonstrate an improvement over competing
algorithms in EM and natural images.Comment: 15 pages, 8 figure
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