Fast grid-based clustering method for automatic calculation of optimal parameters of skin color classifier for head tracking

Szkudlarek, Michal; Pietruszka, Maria

doi:10.1109/cybconf.2015.7175917

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…Manual finding of the optimal values may be hard task for the user, and certainly it is uncomfortable and timeconsuming. Therefore, in [8] we proposed the automatic method of finding the optimal values of skin-color filter. The method of automatic parameters calculation is based on the analysis of the image with arbitrarily marked area of the face.…”

Section: The Fuzzy Skin-color Classifiermentioning

confidence: 99%

“…Pixels of the obtained images are then use as factors in the objective functions G(f) which maximum is searched. To accelerate the computation of the optimum parameters the clustering of the input data transformed to the RGB space is performed with fast grid-based clustering method proposed in [8] where the entire process is described in detail.…”

Section: The Fuzzy Skin-color Classifiermentioning

confidence: 99%

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Fast GPU and CPU computing for Head Position Estimation

Szkudlarek¹,

Pietruszka²

2015

Annals of Computer Science and Information Systems

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Abstract-The head movement based control methods in the 3D graphic applications requires the real-time face position estimation. Therefore, the tracking method at the high speed and with the minimal latency is needed. This is especially hard to achieve when the face is tracked with the use of the high resolution video image on mobile devices. In the article, we present several methods for an acceleration of the face position estimation method based on the fuzzy skin color classifier and other color-based face tracking methods. The acceleration is achieved through a highly parallel GPU computation, the precalculation of the classifier weights and through the combined computations on the GPU and the CPU. The achieved computation time is independent of the used skin color classification method, allowing for use of very complex classifiers. The presented methods provides the robust head position tracking on the high resolution video image of 1920x1080 pixels, at 300 frames per second, on the mobile device with a low computing power.

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Section: The Fuzzy Skin-color Classifiermentioning

confidence: 99%

Section: The Fuzzy Skin-color Classifiermentioning

confidence: 99%

Fast GPU and CPU computing for Head Position Estimation

Szkudlarek¹,

Pietruszka²

2015

Annals of Computer Science and Information Systems

Self Cite

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show abstract

“…The basic idea of the grid‐based algorithms is dividing the clustering space into several grids, and then clustering on these grids. Recently, some grid‐based color clustering methods have been proposed . Szkudlarek et al proposed a grid‐based method to cluster in the Red, Green, Blue (RGB) space, which does not require any input parameters and works well with data of unknown and complex structure.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, some grid‐based color clustering methods have been proposed . Szkudlarek et al proposed a grid‐based method to cluster in the Red, Green, Blue (RGB) space, which does not require any input parameters and works well with data of unknown and complex structure. Despite the fact that the grid‐based algorithms are highly efficient, the clustering results of this kind of algorithm are sensitive to the size of the grid.…”

Section: Introductionmentioning

confidence: 99%

A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Zheng

Cao

et al. 2020

Color Research & Application

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Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non‐iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k‐means, fuzzy c‐means, mean‐shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.

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Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions

Yas

Zaidan

et al. 2018

Measurement

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Fast grid-based clustering method for automatic calculation of optimal parameters of skin color classifier for head tracking

Cited by 3 publications

References 15 publications

Fast GPU and CPU computing for Head Position Estimation

Fast GPU and CPU computing for Head Position Estimation

A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions

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