Titlebook: Research in Shape Modeling; Los Angeles, July 20 Kathryn Leonard,Sibel Tari Conference proceedings 2015 The Editor(s) (if applicable) and T

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Skeleton-Based Recognition of Shapes in Images via Longest Path Matching,oi graphs to their minimum spanning trees. This paper serves as a proof of concept for this approach, using images from three shape databases with known segmentability (whale flukes, strawberries, and dancers). Our preliminary results on these images show promise, with both approaches correctly identifying two out of three shapes.

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conference

Identifying Perceptually Salient Features on 2D Shapes,es from this perceptual viewpoint. We discuss the results of each algorithm and compare them with those of the user study, leading to a practical solution for computing hierarchies of salient features on 2D shapes.

commute

A Biomechanical Model of Cortical Folding,ical development. Despite its simplicity, the proposed model can be used to demonstrate the plausibility of tension generating cortical folds, as has been suggested in Van?Essen (Nature 385(6614):313–318, 1997). In addition, this model is used to investigate folding patterns on different domain sizes.

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2364-5733 ). In-depth discussion of shape modeling techniques is supplemented by full-color illustrations demonstrating the results of workshop-developed shape modeling algorithms. It will be the first volume?in Springer‘s AWM series.978-3-319-36263-2978-3-319-16348-2Series ISSN 2364-5733 Series E-ISSN 2364-5741

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Automatic Prior Shape Selection for Image Segmentation,ng results. High vision prior information such as prior shape has been proven to be effective in solving this problem. Most existing shape prior approaches assume known prior shape and segmentation results rely on the selection of prior shape. In this paper, we study how to do simultaneous automatic

rheumatology

A Scalable Fluctuating Distance Field: An Application to Tumor Shape Analysis, order to quantify the tumor shape variations in a follow-up scenario, a shape registration based on a scalable fluctuating shape field is described. In the earlier work of fluctuating distance fields (Tari and Genctav, J Math Imaging Vis 1–18, 2013; Tari, Fluctuating distance fields, parts, three-p

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Part-Aware Distance Fields for Easy Inbetweening in Arbitrary Dimensions,etween shapes is chosen as a toy application. Rather than presenting a highly competitive scheme which continuously morphs one shape into another, our aim is to investigate whether in-betweens may be defined as ordinary averages once a proper shape representation (e.g. a part aware field) is establi