9,578 research outputs found
Densely Supervised Grasp Detector (DSGD)
This paper presents Densely Supervised Grasp Detector (DSGD), a deep learning
framework which combines CNN structures with layer-wise feature fusion and
produces grasps and their confidence scores at different levels of the image
hierarchy (i.e., global-, region-, and pixel-levels). % Specifically, at the
global-level, DSGD uses the entire image information to predict a grasp. At the
region-level, DSGD uses a region proposal network to identify salient regions
in the image and predicts a grasp for each salient region. At the pixel-level,
DSGD uses a fully convolutional network and predicts a grasp and its confidence
at every pixel. % During inference, DSGD selects the most confident grasp as
the output. This selection from hierarchically generated grasp candidates
overcomes limitations of the individual models. % DSGD outperforms
state-of-the-art methods on the Cornell grasp dataset in terms of grasp
accuracy. % Evaluation on a multi-object dataset and real-world robotic
grasping experiments show that DSGD produces highly stable grasps on a set of
unseen objects in new environments. It achieves 97% grasp detection accuracy
and 90% robotic grasping success rate with real-time inference speed
Hierarchical image simplification and segmentation based on Mumford-Shah-salient level line selection
Hierarchies, such as the tree of shapes, are popular representations for
image simplification and segmentation thanks to their multiscale structures.
Selecting meaningful level lines (boundaries of shapes) yields to simplify
image while preserving intact salient structures. Many image simplification and
segmentation methods are driven by the optimization of an energy functional,
for instance the celebrated Mumford-Shah functional. In this paper, we propose
an efficient approach to hierarchical image simplification and segmentation
based on the minimization of the piecewise-constant Mumford-Shah functional.
This method conforms to the current trend that consists in producing
hierarchical results rather than a unique partition. Contrary to classical
approaches which compute optimal hierarchical segmentations from an input
hierarchy of segmentations, we rely on the tree of shapes, a unique and
well-defined representation equivalent to the image. Simply put, we compute for
each level line of the image an attribute function that characterizes its
persistence under the energy minimization. Then we stack the level lines from
meaningless ones to salient ones through a saliency map based on extinction
values defined on the tree-based shape space. Qualitative illustrations and
quantitative evaluation on Weizmann segmentation evaluation database
demonstrate the state-of-the-art performance of our method.Comment: Pattern Recognition Letters, Elsevier, 201
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
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
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