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1

Subspace Memory Clustering

100%

Struski Ł., Spurek P., Tabor J.

Schedae Informaticae

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2015

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vol. 24

PL

We present a new subspace clustering method called SuMC (Subspace Memory Clustering), which allows to efficiently divide a dataset D RN into k 2 N pairwise disjoint clusters of possibly different dimensions. Since our approach is based on the memory compression, we do not need to explicitly specify dimensions of groups: in fact we only need to specify the mean number of scalars which is used to describe a data-point. In the case of one cluster our method reduces to a classical Karhunen-Loeve (PCA) transform. We test our method on some typical data from UCI repository and on data coming from real-life experiments.

2

Cross-Entropy Based Image Thresholding

100%

Malik M., Spurek P., Tabor J.

Schedae Informaticae

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2015

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vol. 24

PL

This paper presents a novel global thresholding algorithm for the binarization of documents and gray-scale images using Cross-Entropy Clustering. In the first step, a gray-level histogram is constructed, and the Gaussian densities are fitted. The thresholds are then determined as the cross-points of the Gaussian densities. This approach automatically detects the number of components (the upper limit of Gaussian densities is required).

3

Cross Entropy Clustering Approach to Iris Segmentation for Biometrics Purpose

88%

Misztal K., Saeed E., Saeed K., Spurek P., Tabor J.

Schedae Informaticae

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2015

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vol. 24

PL

This work presents the step by step tutorial for how to use cross entropy clustering for the iris segmentation. We present the detailed construction of a suitable Gaussian model which best fits for in the case of iris images, and this is the novelty of the proposal approach. The obtained results are promising, both pupil and iris are extracted properly and all the information necessary for human identification and verification can be extracted from the found parts of the iris.

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3 Schedae Informaticae

3 2015

3 Spurek P.

3 Tabor J.

1 Malik M.

1 Misztal K.

1 Saeed E.

1 Saeed K.

1 Struski Ł.

Subspace Memory Clustering

Cross-Entropy Based Image Thresholding

Cross Entropy Clustering Approach to Iris Segmentation for Biometrics Purpose