Domain Adaptation for Holistic Skin Detection

Dourado, Aloisio; Guth, Frederico; Campos, Teófilo de; Weigang, Li

doi:10.1109/sibgrapi54419.2021.00056

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…Domain adaptation techniques are becoming increasingly useful in real-world situations, making them essential tools for enhancing the generalization capacities of computer and robotic vision systems [19]. These strategies enable the smooth deployment of vision models in a variety of real-world applications, including autonomous cars, healthcare systems, and industrial automation, by efficiently resolving the domain shift problem [20,21].…”

Section: Overview Of Domain Adaptation Techniquesmentioning

confidence: 99%

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

Tanveer,

Fatima,

Zardari

et al. 2023

Applied Sciences

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This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of computer and robotic vision. Through a cross-domain analysis, we scrutinize the performance of these approaches in different contexts, shedding light on their strengths and limitations. The findings gleaned from our evaluation of specific domains and models offer valuable insights for practical applications while reinforcing the validity of the proposed methodologies.

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Section: Overview Of Domain Adaptation Techniquesmentioning

confidence: 99%

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

Tanveer,

Fatima,

Zardari

et al. 2023

Applied Sciences

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“…This makes the comparison with traditional existing approaches very difficult, due to the different testing protocols. For instance, recently, a research study compares different deep learning approaching on different datasets using different training sets [54]. In this work we adopt a standard protocol to train the models and validate the results.…”

Section: Skin Detection Approachesmentioning

confidence: 99%

A Standardized Approach for Skin Detection: Analysis of the Literature and Case-Studies

Nanni¹,

Loreggia²,

Lumini³

et al. 2022

Preprint

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Skin detection, the process of distinguishing between skin and non-skin regions in a digital image, is widely used in a variety of applications ranging from hand gesture analysis to body part tracking to facial recognition. Skin detection is a challenging problem that has received a lot of attention from experts and proposals from the research community in the context of intelligent systems, but the lack of common benchmarks and unified testing protocols has hampered fairness among approaches. Comparisons are very difficult. Recently, the success of deep neural networks has had a major impact on the field of image segmentation detection, resulting in various successful models to date. In this work, we survey the most recent research in this field and propose fair comparisons between approaches using several different datasets. The main contributions of this work are: (i) a comprehensive literature review of approaches to skin color detection and a comparison of approaches that may help researchers and practitioners choose the best method for their application; (ii) a comprehensive list of datasets that report ground truth for skin detection; (iii) a framework for evaluating and combining different skin detection approaches. Moreover, we proposed an ensemble of convolutional neural networks and transformers that obtains state of the art performance. All the code is made publicly available at https://github.com/LorisNanni

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“…All the proposed architectures report comparable or better performance than other the state-of-theart methods; anyway, a fair comparison with traditional existing approaches is always difficult due to the different testing protocols. For example in a very recent work (Dourado, Guth, de Campos, & Li, 2019), the authors suggest to compare some deep learning approaches on four different datasets giving both in-domain and cross-domain results: for the latter approach they report different performance depending on the training set. Since none of the authors above has released a model to perform comparisons, in this work we train and use three of the most recently proposed models for image segmentation: SegNet (Badrinarayanan, Kendall, & Cipolla, 2017), U-Net (Ronneberger, Fischer, & Brox, 2015) and Deeplabv3+ (L. C. Chen, Zhu, Papandreou, Schroff, & Adam, 2018).…”

Section: Skin Detection Approachesmentioning

confidence: 99%