1,302 research outputs found
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
On reflection symmetry in natural images
Many new symmetry detection algorithms have been recently developed, thanks to an interest revival on computational symmetry for computer graphics and computer vision applications. Notably, in 2013 the IEEE CVPR Conference organized a dedicated workshop and an accompanying symmetry detection competition. In this paper we propose an approach for symmetric object detection that is based both on the computation of a symmetry measure for each pixel and on saliency. The symmetry value is obtained as the energy balance of the even-odd decomposition of a patch w.r.t. each possible axis. The candidate symmetry axes are then identified through the localization of peaks along the direction perpendicular to each considered axis orientation. These found candidate axes are finally evaluated through a confidence measure that also allow removing redundant detected symmetries. The obtained results within the framework adopted in the aforementioned competition show significant performance improvement
PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models
In geometry processing, symmetry is a universal type of high-level structural
information of 3D models and benefits many geometry processing tasks including
shape segmentation, alignment, matching, and completion. Thus it is an
important problem to analyze various symmetry forms of 3D shapes. Planar
reflective symmetry is the most fundamental one. Traditional methods based on
spatial sampling can be time-consuming and may not be able to identify all the
symmetry planes. In this paper, we present a novel learning framework to
automatically discover global planar reflective symmetry of a 3D shape. Our
framework trains an unsupervised 3D convolutional neural network to extract
global model features and then outputs possible global symmetry parameters,
where input shapes are represented using voxels. We introduce a dedicated
symmetry distance loss along with a regularization loss to avoid generating
duplicated symmetry planes. Our network can also identify generalized cylinders
by predicting their rotation axes. We further provide a method to remove
invalid and duplicated planes and axes. We demonstrate that our method is able
to produce reliable and accurate results. Our neural network based method is
hundreds of times faster than the state-of-the-art methods, which are based on
sampling. Our method is also robust even with noisy or incomplete input
surfaces.Comment: Corrected typo
Generalized intrinsic symmetry detection
In this paper, we address the problem of detecting partial symmetries in 3D objects. In contrast to previous work, our algorithm is able to match deformed symmetric parts: We first develop an algorithm for the case of approximately isometric deformations, based on matching graphs of surface feature lines that are annotated with intrinsic geometric properties. The sensitivity to non-isometry is controlled by tolerance parameters for each such annotation. Using large tolerance values for some of these annotations and a robust matching of the graph topology yields a more general symmetry detection algorithm that can detect similarities in structures that have undergone strong deformations. This approach for the first time allows for detecting partial intrinsic as well as more general, non-isometric symmetries. We evaluate the recognition performance of our technique for a number synthetic and real-world scanner data sets
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