The 2D shape structure dataset: A user annotated open access database

Carlier, Aurélie; Leonard, Kathryn; Hahmann, Stéfanie; Morin, Géraldine; Collins, Misha

doi:10.1016/j.cag.2016.05.009

Cited by 21 publications

(20 citation statements)

References 29 publications

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“…However, for volume preserving deformation, the proposed decomposition is robust and gives a consistent labeling of the shape parts as long as the topology is preserved. As expected, the generalization of WEDF to 3D leads to an analysis of the shape that seems consistent with perception, as shown in 2D in [5,33]. Future work will include a user study to confirm the perceptual coherence.…”

Section: Discussionsupporting

confidence: 66%

“…Another example is WEDF [5] (Weighted Extended Distance Function), which defines a measure for 2D sub-parts saliency. It has recently been validated through perceptual studies about cognitively meaningful shape importance [33] and used for 2D shape decomposition and similarity detection [6].…”

Section: Preliminariesmentioning

confidence: 99%

“…Also, the shape parts that are similar (legs, fins or antennas) are assigned to the same hierarchy level. The proposed segmentation then gives not only a partition of the shape, but also assigns a level of salience to each feature that is global on the shape, and known to be consistent with perception in 2D [5,33]. Fig.…”

Section: Validationmentioning

confidence: 99%

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A salience measure for 3D shape decomposition and sub-parts classification

et al. 2018

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Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible a A B S T R A C T This paper introduces a measure of significance on a curve skeleton of a 3D piecewise linear shape mesh, allowing the computation of both the shape's parts and their saliency. We begin by reformulating three existing pruning measures into a non-linear PCA along the skeleton. From this PCA, we then derive a volume-based salience measure, the 3D WEDF, that determines the relative importance to the global shape of the shape part associated to a point of the skeleton. First, we provide robust algorithms for computing the 3D WEDF on a curve skeleton, independent on the number of skeleton branches. Then, we cluster the WEDF values to partition the curve skeleton, and coherently map the decomposition to the associated surface mesh. Thus, we develop an unsupervised hierarchical decomposition of the mesh faces into visually meaningful shape regions that are or-dered according to their degree of perceptual salience. The shape analysis tools introduced in this paper are important for many applications including shape comparison, editing, and compression.

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Section: Discussionsupporting

confidence: 66%

Section: Preliminariesmentioning

confidence: 99%

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A salience measure for 3D shape decomposition and sub-parts classification

et al. 2018

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“…For both steps, we use a salience measure, the Weighted Extended Distance Function (WEDF) function on the skeleton [20], to determine the relative importance of branches. The WEDF function has been shown to measure relative importance of shape parts in a way that matches human perception [8].…”

Section: Parametric Skeletonsmentioning

confidence: 99%

SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding

Demir¹,

Hahn

Leonard

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Figure 1: SkelNetOn Challenges: Example shapes and corresponding skeletons are demonstrated for the three challenge tracks in pixel (left), point (middle), and parametric domain (right). AbstractWe present SkelNetOn 2019 Challenge and Deep Learning for Geometric Shape Understanding workshop to utilize existing and develop novel deep learning architectures for shape understanding. We observed that unlike traditional segmentation and detection tasks, geometry understanding is still a new area for deep learning techniques. SkelNetOn aims to bring together researchers from different domains to foster learning methods on global shape understanding tasks. We aim to improve and evaluate the state-of-theart shape understanding approaches, and to serve as reference benchmarks for future research. Similar to other challenges in computer vision [6,22], SkelNetOn proposes three datasets and corresponding evaluation methodologies; all coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2019 conference. In this paper, we describe and analyze characteristics of datasets, define the evaluation criteria of the public competitions, and provide baselines for each task.Computer vision approaches have shown tremendous progress toward understanding shapes from various data formats, especially since entering the deep learning era. Although detection, recognition, and segmentation approaches achieve highly accurate results, there has been rel-arXiv:1903.09233v3 [cs.CV]

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“…Unlike 3D modelling, it is necessary to build various 2D models to anticipate different conceptions or situations. Carlier, Leonard, Hahmann, Morin, and Collins [42] have done a study to collect information about how people decompose the structure of shapes. Their study covers 1200 shapes in 70 shape classes, and 2861 participants with 41,953 annotations have been collected in which at least 24 annotations per shape.…”

Section: Deconstruction and Construction Of Compoundsmentioning

confidence: 99%

Metaphorical Parametric Model for Brand Mark Design: Towards a universal model of computational visual communication design

Ng¹,

Choy²

2019

International Conferences on Distributed Multimedia Systems

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Visual communication design involved with two aspects of visual representation, namely, information and expression. The idea of building a computational model for visual communication design has been developed for many years. However, this objective has only been partially fulfilled as most of the solutions are focused on showing information rather than expression. Lakoff and Johnson have claimed that "human thought processes are largely metaphorical." Thus, a computational model for visual communication based on the concept of conceptual metaphor is proposed. As brand mark design is considered as a challenging design type that demands the representation of both information and expression, the first attempt of the development of this new model is targeted for this specific type of design. In this paper, the Metaphorical Parametric Model (MPM) is proposed as a computational model to represent brand mark design. The MPM is a type of model which represents the semantic structure rather than the synthetic structure of conceptual components. The components of an MPM is assembled based on the concepts of metaphorical relations which could be signified by spatial relations between the related objects. All objects and relations defined in the MPM are parametrically controllable which could be generated as different visual forms to represent both information and a wide range of metaphorical expressions. Keywords -visual language; visual communication design; conceptual metaphor; metaphorical expression;According to these authors, conceptual metaphors are different from the linguistic ones but specific cognitive concepts embedded in our bodily experience. By learning from the interactions with the environment, infant gains experience which structures the mind and formulates the "image schema". Johnson [19] explained that the image schema is not an abstract, finite proposition, concept or concrete image but an evolving By changing the relations between the elements using Right Of, Part Of, Overlap and Top Of instead of Inside. The semantic meaning will change and signify different metaphorical expressions.

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The 2D shape structure dataset: A user annotated open access database

Cited by 21 publications

References 29 publications

A salience measure for 3D shape decomposition and sub-parts classification

A salience measure for 3D shape decomposition and sub-parts classification

SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding

Metaphorical Parametric Model for Brand Mark Design: Towards a universal model of computational visual communication design

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