Deep skin detection on low resolution grayscale images

Paracchini, Marco; Marcon, Marco; Villa, Federica; Tubaro, Stefano

doi:10.1016/j.patrec.2019.12.021

Cited by 21 publications

(12 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Skin detection has been a long-standing challenge in the field of computer vision, and it has been used in many studies that involve decision-making about humans [ 33 , 38 ]. Several survey papers have explored various methods for skin detection [ 19 , 32 ].…”

Section: Proposed Solutionmentioning

confidence: 99%

A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment

Nadian-Ghomsheh

Farahani

Kavian

2021

Multimed Tools Appl

View full text Add to dashboard Cite

The healthcare industry requires the integration of digital technologies, such as Artificial Intelligence (AI) and the Internet of Things (IoT), to their full potential, particularly during this challenging time and the recent outbreak of the COVID-19 pandemic, which resulted in the disruptions in healthcare delivery, service operations, and shortage of healthcare personnel. However, every opportunity has barriers and bumps, and when it comes to IoT healthcare, data privacy is one of the main growing issues. Despite the recent advances in the development of IoT healthcare architectures, most of them are invasive for the data subjects. In this context, the broad applications of AI in the IoT domain have also been hindered by emerging strict legal and ethical requirements to protect individual privacy. Camera-based solutions that monitor human subjects in everyday settings, e.g., for Online Range of Motion (ROM) detection, are making this problem even worse. One actively practiced branch of such solutions is telerehabilitation, which provides remote solutions for the physically impaired to regain their strength and get back to their normal daily routines. The process usually involves transmitting video/images from the patient performing rehabilitation exercises and applying Machine Learning (ML) techniques to extract meaningful information to help therapists devise further treatment plans. Thereby, real-time measurement and assessment of rehabilitation exercises in a reliable, accurate, and Privacy-Preserving manner is imperative. To address the privacy issue of existing solutions, this paper proposes a holistic Privacy-Preserving (PP) hierarchical IoT solution that simultaneously addresses the utilization of AI-driven IoT and the demands for data protection. Furthermore, the efficiency of the proposed architecture is demonstrated by a novel machine learning-based system that allows immediate assessment and extraction of ROM as the critical information for analyzing the progress of patients.

show abstract

Section: Proposed Solutionmentioning

confidence: 99%

A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment

Nadian-Ghomsheh

Farahani

Kavian

2021

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…The complete data set is available for download at the link: https://github.com/marcobrando/Deep-Skin-Detection-on-Low-Resolution-Grayscale-Images. Further detail on the chosen architecture and training procedure are described in [27]. Although the SPAD acquisition frame rate is set to 100 Hz, the deep learning skin detection method is executed at 10 Hz on key frames obtained by averaging 10 consecutive frames.…”

Section: Skin Detectionmentioning

confidence: 99%

Biometric Signals Estimation Using Single Photon Camera and Deep Learning

Paracchini

Marcon

Villa

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

The problem of performing remote biomedical measurements using just a video stream of a subject face is called remote photoplethysmography (rPPG). The aim of this work is to propose a novel method able to perform rPPG using single-photon avalanche diode (SPAD) cameras. These are extremely accurate cameras able to detect even a single photon and are already used in many other applications. Moreover, a novel method that mixes deep learning and traditional signal analysis is proposed in order to extract and study the pulse signal. Experimental results show that this system achieves accurate results in the estimation of biomedical information such as heart rate, respiration rate, and tachogram. Lastly, thanks to the adoption of the deep learning segmentation method and dependability checks, this method could be adopted in non-ideal working conditions—for example, in the presence of partial facial occlusions.

show abstract

“…Various previous studies have proposed and used image processing techniques for skin detection, 7 skin lesion segmentation, 8 skin cancer diagnosis, 9 evaluating re‐pigmentation after vitiligo treatment 35 and differential diagnosis of hypo pigmenting disorders 10 …”

Section: Related Workmentioning

confidence: 99%

Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

Khatibi

Rezaei

Fashtami

et al. 2020

Skin Research and Technology

View full text Add to dashboard Cite

Background Vitiligo is an acquired pigmentary skin disorder characterized by depigmented macules and patches which brings many challenges for the patients suffering from. For vitiligo severity assessment, several scoring methods have been proposed based on morphometry and colorimetry. But, all methods suffer from much inter‐ and intra‐observer variations for estimating the depigmented area. For all mentioned assessment methods of vitiligo disorder, accurate segmentation of the skin images for lesion detection and localization is required. The image segmentation for localizing vitiligo skin lesions has many challenges because of illumination variation, different shapes and sizes of vitiligo lesions, vague lesion boundaries and skin hairs and vignette effects. The manual image segmentation is a tedious and time‐consuming task. Therefore, using automatic image segmentation methods for lesion detection is necessarily required. Materials and methods In this study, a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) methods is proposed for localizing vitiligo lesions in skin images. Unsupervised segmentation methods do not require prior manual segmentation of vitiligo lesions which is a tedious and time‐consuming task with intra‐ and inter‐observer variations. Results Our collected dataset includes 877 images taken from 21 patients with the resolution of 5760*3840 pixels suffering from vitiligo disorder. Experimental results show that SEDCIS outperforms the compared methods with accuracy of 97%, sensitivity of 98%, specificity of 96%, area overlapping of 94%, and Dice index of 97%. Conclusion The proposed method can segment vitiligo lesions with highly reasonable performance and can be used for assessing the vitiligo lesion surface.

show abstract

Deep skin detection on low resolution grayscale images

Cited by 21 publications

References 11 publications

A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment

A hierarchical privacy-preserving IoT architecture for vision-based hand rehabilitation assessment

Biometric Signals Estimation Using Single Photon Camera and Deep Learning

Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

Contact Info

Product

Resources

About