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Introduction
Figure 1:(a) The three detected commonalities (white curves) projected on the corresponding distance matrix. In the top left and bottom right, the two corresponding given curves and the tangential angles are also depicted. (b), (c), (d) The point correspondences (gray dotted lines) of the three detected commonalities projected on the two given curves A and B that are drawn with yellow and blue colors, respectively.
In [3], we study the problem of unsupervised detection of geometrically similar fragments (segments) in curves, in the context of boundary matching. The goal is to determine all pairs of sub-curves that are geometrically similar, under local scale invariance. In particular, we aim to locate the existence of a similar section (independent of length and/or orientation) in the second curve, to a section of the first curve, as indicated by the user. The proposed approach is based on a suitable distance matrix of the two given curves. Additionally, a suitable objective function is proposed to capture the trade-off between the similarity of the common sub-sequences and their lengths. The goal of the algorithm is to minimize this objective function via an efficient graph-based approach that capitalizes on Dynamic Time Warping to compare the two subcurves. We apply the proposed technique in the context of geometric matching of coastline pairs. This application is crucial for investigating the forcing factors related to the coastline evolution.
Methodology
The proposed method performs a very efficient graph-based search on the matrix of pairwise distances of frames of the two curves. This search is supported by an objective function that captures the trade off between the similarity of the common curves and their lengths.
Experiments - Downloads
Figure 3: (left) The detected commonality (white curve) projected on the corresponding distance matrix D. (right) The point correspondences (gray dotted lines) of the detected commonality projected on the two given coastlines of Africa and Arabian Peninsula.
You can download the matlab code of methods proposed in [3]
After the paper [3] acceptance, you can download the datasets used in [3]
See the corresponding readme.txt files for more details.
Related Publications
[1] C. Panagiotakis, K. Papoutsakis and A.A. Argyros, A Graph-based Approach for Detecting Common Actions in Motion Capture Data and Videos, Pattern Recognition, Pattern Recognition, Elsevier, vol. 79, pp. 1-11, July 2018.
[2] K. Papoutsakis, C. Panagiotakis and A.A. Argyros, "Temporal Action Co-Segmentation in 3D Motion Capture Data and Videos", In IEEE Computer Vision and Pattern Recognition (CVPR 2017), IEEE, Honolulu, Hawaii, USA, July 2017.
[3] C. Panagiotakis, S. Markaki, E. Kokinou and H. Papadakis, Coastline Matching via a Graph-based Approach, submitted to ICPR, 2022 (under review).
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