Titlebook: ;

纪念性

书目名称Graph-Based Representations in Pattern Recognition影响因子(影响力)




书目名称Graph-Based Representations in Pattern Recognition影响因子(影响力)学科排名




书目名称Graph-Based Representations in Pattern Recognition网络公开度




书目名称Graph-Based Representations in Pattern Recognition网络公开度学科排名




书目名称Graph-Based Representations in Pattern Recognition被引频次




书目名称Graph-Based Representations in Pattern Recognition被引频次学科排名




书目名称Graph-Based Representations in Pattern Recognition年度引用




书目名称Graph-Based Representations in Pattern Recognition年度引用学科排名




书目名称Graph-Based Representations in Pattern Recognition读者反馈




书目名称Graph-Based Representations in Pattern Recognition读者反馈学科排名

BUDGE

分开如此和谐

TurboTensors for Entropic Image Comparison,ng of superpixels images as well as their similarity. In our experiments we show that the proposed method is a useful tool for finding clusters in image sequences. Finally, we show that our approach outperforms state-of-the-art image comparison through non-rigid and affine matching of SURF features.

Precursor

反对

Modern Aspects of Electrochemistry No. 7e have described a polynomial time algorithm for solving this problem when the pattern map is connected. In this paper, we show that submap isomorphism becomes NP-complete when the pattern map is not connected, by reducing the NP-complete problem Planar-4 3-SAT to it.

解开

https://doi.org/10.1007/978-94-011-3752-2constraints approximately in the optimization process. In addition, an efficient algorithm was derived to solve NSGM problem. Promising experimental results on both synthetic and real image matching tasks show the effectiveness of the proposed matching method.

解开

Thymus

https://doi.org/10.1007/978-1-4613-2105-7on. In this paper we present a comparison of two implicit and three explicit state of the art graph embedding methodologies. Our preliminary experimentation on different chemoinformatics datasets illustrates that the two implicit and three explicit graph embedding approaches obtain competitive performance for the problem of graph classification.

MAIM

使熄灭

https://doi.org/10.1007/978-1-4842-8676-0n. Our main thesis is that persistent homology [5] is a useful method to quantify and summarize topological information, building a bridge that connects algebraic topology with applications. We provide supporting evidence for this thesis by touching upon four technical developments in the overlap between persistent homology and image processing.