Scale Normalization for Isometric Shape Matching

Date

2012-09-01

Author

Sahillioğlu, Yusuf
Yemez, Y.

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

102
views

2
downloads

We address the scale problem inherent to isometric shape correspondence in a combinatorial matching framework. We consider a particular setting of the general correspondence problem where one of the two shapes to be matched is an isometric (or nearly isometric) part of the other up to an arbitrary scale. We resolve the scale ambiguity by finding a coarse matching between shape extremities based on a novel scale-invariant isometric distortion measure. The proposed algorithm also supports (partial) dense matching, that alleviates the symmetric flip problem due to initial coarse sampling. We test the performance of our matching algorithm on several shape datasets in comparison to state of the art. Our method proves useful, not only for partial matching, but also for complete matching of semantically similar hybrid shape pairs whose maximum geodesic distances may not be compatible, a case that would fail most of the conventional isometric shape matchers.

URI

https://hdl.handle.net/11511/97283

Journal

COMPUTER GRAPHICS FORUM

DOI

https://doi.org/10.1111/j.1467-8659.2012.03216.x

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Image segmentation by fusion of low level and domain specific information via Markov Random Fields Karadag, Ozge Oztimur; Yarman Vural, Fatoş Tunay (2014-09-01) We propose a new segmentation method by fusing a set of top-down and bottom-up segmentation maps under the Markov Random Fields (MRF) framework. The bottom-up segmentation maps are obtained by varying the parameters of an unsupervised segmentation method, such as Mean Shift. The top-down segmentation maps are constructed from some priori information, called domain specific information (DSI), received from a domain expert in the form of general properties about the image dataset. The properties are then used...
Multiple Shape Correspondence by Dynamic Programming Sahillioğlu, Yusuf (2014-10-01) We present a multiple shape correspondence method based on dynamic programming, that computes consistent bijective maps between all shape pairs in a given collection of initially unmatched shapes. As a fundamental distinction from previous work, our method aims to explicitly minimize the overall distortion, i.e., the average isometric distortion of the resulting maps over all shape pairs. We cast the problem as optimal path finding on a graph structure where vertices are maps between shape extremities. We e...
Shape recognition with generalized beam angle statistics Tola, OO; Arica, N; Yarman-Vural, F (2004-01-01) In this study, we develop a new shape descriptor and a matching algorithm in order to find a given template shape in an edge detected image without extracting the boundary. The shape descriptor based on Generalized Beam Angle Statistics (GBAS) defines the angles between the lines connecting each boundary point with the rest of the points, as random variable. Then, it assigns a feature vector to each point using the moments of beam angles. The proposed matching algorithm performs shape recognition by matchin...
Coarse-to-Fine Isometric Shape Correspondence by Tracking Symmetric Flips Sahillioğlu, Yusuf; Yemez, Y. (2013-02-01) We address the symmetric flip problem that is inherent to multi-resolution isometric shape matching algorithms. To this effect, we extend our previous work which handles the dense isometric correspondence problem in the original 3D Euclidean space via coarse-to-fine combinatorial matching. The key idea is based on keeping track of all optimal solutions, which may be more than one due to symmetry especially at coarse levels, throughout denser levels of the shape matching process. We compare the resulting den...
Similarity matrix framework for data from union of subspaces Aldroubi, Akram; Sekmen, Ali; Koku, Ahmet Buğra; Cakmak, Ahmet Faruk (2018-09-01) This paper presents a framework for finding similarity matrices for the segmentation of data W = [w(1)...w(N)] subset of R-D drawn from a union U = boolean OR(M)(i=1) S-i, of independent subspaces {S-i}(i=1)(M), of dimensions {d(i)}(i=1)(M). It is shown that any factorization of W = BP, where columns of B form a basis for data W and they also come from U, can be used to produce a similarity matrix Xi w. In other words, Xi w(i, j) not equal 0, when the columns w(i) and w(j) of W come from the same subspace, ...

Citation Formats

Y. Sahillioğlu and Y. Yemez, “Scale Normalization for Isometric Shape Matching,” COMPUTER GRAPHICS FORUM, vol. 31, no. 7, pp. 2233–2240, 2012, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/97283.