Motion Segmentation via Generalized Curvatures

Arn, Robert T.; Narayana, Pradyumna; Emerson, Tegan; Draper, Bruce A.; Kirby, Michael; Peterson, Chris

doi:10.1109/tpami.2018.2869741

Cited by 11 publications

(8 citation statements)

References 30 publications

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“…Here we are focused only on the clustering problem for motion data; however, there are many works on classifying motions in video sequences which are of a different flavor, e.g. [69], [70], [71]. As mentioned, clustering performance using CUR decompositions is tested using the Hopkins155 dataset.…”

Section: B Simulations Using Synthetic Datamentioning

confidence: 99%

CUR Decompositions, Similarity Matrices, and Subspace Clustering

Aldroubi

Hamm

Koku

et al. 2019

Front. Appl. Math. Stat.

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A general framework for solving the subspace clustering problem using the CUR decomposition is presented. The CUR decomposition provides a natural way to construct similarity matrices for data that come from a union of unknown subspaces U = M i=1 S i . The similarity matrices thus constructed give the exact clustering in the noise-free case. Additionally, this decomposition gives rise to many distinct similarity matrices from a given set of data, which allow enough flexibility to perform accurate clustering of noisy data. We also show that two known methods for subspace clustering can be derived from the CUR decomposition. An algorithm based on the theoretical construction of similarity matrices is presented, and experiments on synthetic and real data are presented to test the method.Additionally, an adaptation of our CUR based similarity matrices is utilized to provide a heuristic algorithm for subspace clustering; this algorithm yields the best overall performance to date for clustering the Hopkins155 motion segmentation dataset.

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Section: B Simulations Using Synthetic Datamentioning

confidence: 99%

CUR Decompositions, Similarity Matrices, and Subspace Clustering

Aldroubi

Hamm

Koku

et al. 2019

Front. Appl. Math. Stat.

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show abstract

“…In order to improve the accuracy of the data and the smoothness of the ECG signal and reduce the interference of noise without changing the signal trend, we employ the Savitzky-Golay algorithm [30] to smooth signal and restrain noise. In the Savitzky-Golay algorithm, the continuous subset of adjacent data points is fitted with a low-order polynomial by the linear least square method.…”

Section: Algorithmmentioning

confidence: 99%

“…(2) DenseNet [30]: From the perspective of features, DenseNet dramatically reduces the number of parameters of the network through feature reuse and bypass settings, which are easy to train and have a specific regularization effect.…”

Section: Comparisonmentioning

confidence: 99%

Electrocardiogram Signal Classification Based on Mix Time-Series Imaging

Cai

Xiong

et al. 2022

Electronics

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Arrhythmia is a significant cause of death, and it is essential to analyze the electrocardiogram (ECG) signals as this is usually used to diagnose arrhythmia. However, the traditional time series classification methods based on ECG ignore the nonlinearity, temporality, or other characteristics inside these signals. This paper proposes an electrocardiogram classification method that encodes one-dimensional ECG signals into the three-channel images, named ECG classification based on Mix Time-series Imaging (EC-MTSI). Specifically, this hybrid transformation method combines Gramian angular field (GAF), recurrent plot (RP), and tiling, preserving the original ECG time series’ time dependence and correlation. We use a variety of neural networks to extract features and perform feature fusion and classification. This retains sufficient details while emphasizing local information. To demonstrate the effectiveness of the EC-MTSI, we conduct abundant experiments in a commonly-used dataset. In our experiments, the general accuracy reached 93.23%, and the accuracy of identifying high-risk arrhythmias of ventricular beats and supraventricular beats alone are as high as 97.4% and 96.3%, respectively. The results reveal that the proposed method significantly outperforms the existing approaches.

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“…As a shape descriptor, the skeleton has the characteristics of translation, rotation, scaling, and perspective invariance, which can effectively reflect the connectivity and topology of the original object shape 1 . At present, image skeleton extraction algorithms are widely used in target recognition, shape matching and various detection applications [2][3][4][5][6][7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

Research on skeleton extraction algorithm based on distance transform

Yang¹,

Yang²,

Lan³

et al. 2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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Aiming at the problem that the position of skeleton obtained by the method based on topology refinement is not accurate enough, and the connectivity of the skeleton obtained by distance transform cannot be guaranteed, this paper combines the advantages of the topology refinement algorithm and distance transformation algorithm to propose an improved skeleton extraction algorithm. This method first obtains the skeleton points through distance transform, and then obtains the initial skeleton through the refinement method; last uses the skeleton points obtained by distance transformation to screen each branch obtained through the refinement method to ensure the accuracy of the skeleton in the later applications. Experiments on the MPEG7 dataset demonstrate the effectiveness of the algorithm.

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Motion Segmentation via Generalized Curvatures

Cited by 11 publications

References 30 publications

CUR Decompositions, Similarity Matrices, and Subspace Clustering

CUR Decompositions, Similarity Matrices, and Subspace Clustering

Electrocardiogram Signal Classification Based on Mix Time-Series Imaging

Research on skeleton extraction algorithm based on distance transform

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