Zero-sum game theory model for segmenting skin regions

Dahmani, Djamila; Cheref, Mehdi; Larabi, Slimane

doi:10.1016/j.imavis.2020.103925

Cited by 30 publications

(22 citation statements)

References 16 publications

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“…where based on the observation model stated in (18), the negative log-likelihood for the patch is given by…”

Section: A the Self-guided Form And Its Properties 1) The Self-guided Smoothing-sharpening Filtermentioning

confidence: 99%

“…Examples are shown in Figs 8(b) and 9(g),(h) in which the skin part of the image is sharpened. To solve this problem, we first estimate a binary skin map using one of the many algorithms for skin segmentation, e.g., [18]- [20]. We then transform the binary skin map by using the nonlinear transformation in (55) to obtain pixel-adaptive κ, which is used in the proposed filter to sharpen non-skin regions only while gently smoothing the skin region to produce a notable face enhancement.…”

Section: ) Texture Guided Smoothing and Sharpening Of Face Imagesmentioning

confidence: 99%

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A Guided Edge-Aware Smoothing-Sharpening Filter Based on Patch Interpolation Model and Generalized Gamma Distribution

Deng

Galetto

Al-nasrawi

et al. 2021

IEEE Open J. Signal Process.

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Smoothing and sharpening are two fundamental image processing operations. The latter is usually related to the former through the unsharp masking algorithm. In this paper, we develop a new type of filter which performs smoothing or sharpening via a tuning parameter. The development of the new filter is based on (1) a new Laplacian-based filter formulation which unifies the smoothing and sharpening operations, (2) a patch interpolation model similar to that used in the guided filter which provides edge-awareness capability, and (3) the generalized Gamma distribution which is used as the prior for parameter estimation. We have conducted detailed studies on the properties of two versions of the proposed filter (self-guidance and external guidance). We have also conducted experiments to demonstrate applications of the proposed filter. In the self-guidance case, we have developed adaptive smoothing and sharpening algorithms based on texture, depth and blurriness information extracted from an image. Applications include enhancing human face images, producing shallow depth of field effects, focus-based image enhancement, and seam carving. In the external guidance case, we have developed new algorithms for combining flash and no-flash images and for enhancing multi-spectral images using a panchromatic image.INDEX TERMS Edge-aware filter, image sharpening, image smoothing, maximum a posteriori estimate.

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“…where based on the observation model stated in (18), the negative log-likelihood for the patch is given by…”

Section: A the Self-guided Form And Its Properties 1) The Self-guided Smoothing-sharpening Filtermentioning

confidence: 99%

Section: ) Texture Guided Smoothing and Sharpening Of Face Imagesmentioning

confidence: 99%

A Guided Edge-Aware Smoothing-Sharpening Filter Based on Patch Interpolation Model and Generalized Gamma Distribution

Deng

Galetto

Al-nasrawi

et al. 2021

IEEE Open J. Signal Process.

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show abstract

“…Moreover, the separation between luminance and chrominance makes this color space popular in skin color detection. For that, the HSV color space can be a good choice for the human skin detection technique [25]. The transformation of the RGB color space into an HSV color space is assured by using Eq.…”

Section: Face Detectionmentioning

confidence: 99%

“…The first step is to detect the face in the image. For this, the Viola & Jones detector Besnassi et al [24] is combined with the human skin color detection based on the HSV color space [25]. This combination reinforces face detection against face degradation, such as partial occlusion and head pose variations.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study Based on De-Occlusion and Reconstruction of Face Images in Degraded Conditions

Ouannes¹,

Khalifa²,

Amara³

2021

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In the recent years, the face recognition task has attracted the attention of researchers due to its efficiency in several domains such as surveillance and access control. Unfortunately, there are multiple challenges that decrease the performance of face recognition. Partial occlusion is the most challenging one since it often causes a great lack of information. The main purpose of this paper is to prove that facial reconstruction improves the results of facial recognition compared to de-occlusion and full-face recognition in the presence of occlusion. Our objective is to achieve occluded-face recognition, de-occluded-face recognition, and reconstructed-face recognition. Regarding face reconstruction, we introduce two different methods based on Laplacian pyramid blending and CycleGANs. In order to validate our work, we perform two different feature extraction techniques: hand-crafted features and learned features exploiting the final layers of a pre-trained deep architecture model. The experimental results on the EURECOM Kinect Face Dataset (EKFD) and the IST-EURECOM Light Field Face Database (IST-EURECOM LFFD) show that the proposed face reconstruction approach, compared with the face de-occlusion and occluded-face recognition ones, clearly improves the face recognition task. Our method boosts the classification performance in comparison with the state-of-the-art methods, achieving 94.66% on EKFD and 72.35% on IST-EURECOM LFFD.

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“…Examples are shown in Figures 8(b) and 9(g,h) in which the skin part of the image is sharpened. To solve this problem, we first estimate a binary skin map using one of the many algorithms for skin segmentation, e.g., [18][19][20]. We then transform the binary skin map by using the non-linear transformation in (55) to obtain pixel-adaptive κ, which is used in the proposed filter to sharpen non-skin regions only while gently smoothing the skin region to produce a notable face enhancement.…”

Section: Texture Guided Smoothing and Sharpening Of Face Imagesmentioning

confidence: 99%

A guided edge-aware smoothing-sharpening filter based on patch interpolation model and generalized Gamma distribution

Deng,

Galetto,

Al-nasrawi

et al. 2021

Preprint

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show abstract

Zero-sum game theory model for segmenting skin regions

Cited by 30 publications

References 16 publications

A Guided Edge-Aware Smoothing-Sharpening Filter Based on Patch Interpolation Model and Generalized Gamma Distribution

A Guided Edge-Aware Smoothing-Sharpening Filter Based on Patch Interpolation Model and Generalized Gamma Distribution

Comparative Study Based on De-Occlusion and Reconstruction of Face Images in Degraded Conditions

A guided edge-aware smoothing-sharpening filter based on patch interpolation model and generalized Gamma distribution

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