Real-Time Hair Segmentation Using Mobile-Unet

Yoon, Han Young; Park, Seong-Woo; Yoo, Ji-Sang

doi:10.3390/electronics10020099

Cited by 14 publications

(3 citation statements)

References 17 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fully convolutional networks (FCN) have made substantial progress in the field of semantic segmentation of images ( 14 ). The FCN model introduces an encoder–decoder-style restoration structure as the core of the model ( 15 ). The encoder is employed to extract features, while the decoder restores as much image resolution as possible while combining high-level semantic and low-level spatial information ( 16 ).…”

Section: Methodsmentioning

confidence: 99%

Establishing a machine learning model for predicting nutritional risk through facial feature recognition

Wang,

He,

Long

2023

Front. Nutr.

View full text Add to dashboard Cite

BackgroundMalnutrition affects many worldwide, necessitating accurate and timely nutritional risk assessment. This study aims to develop and validate a machine learning model using facial feature recognition for predicting nutritional risk. This innovative approach seeks to offer a non-invasive, efficient method for early identification and intervention, ultimately improving health outcomes.MethodsWe gathered medical examination data and facial images from 949 patients across multiple hospitals to predict nutritional status. In this multicenter investigation, facial images underwent preprocessing via face alignment and cropping. Orbital fat pads were isolated using the U-net model, with the histogram of oriented gradient (HOG) method employed for feature extraction. Standardized HOG features were subjected to principal component analysis (PCA) for dimensionality reduction. A support vector machine (SVM) classification model was utilized for NRS-2002 detection. Our approach established a non-linear mapping between facial features and NRS-2002 nutritional risk scores, providing an innovative method for evaluating patient nutritional status.ResultsIn context of orbital fat pad area segmentation with U-net model, the averaged dice coefficient is 88.3%. Our experimental results show that the proposed method to predict NRS-2002 scores achieves an accuracy of 73.1%. We also grouped the samples by gender, age, and the location of the hospital where the data were collected to evaluate the classification accuracy in different subsets. The classification accuracy rate for the elderly group was 85%, while the non-elderly group exhibited a classification accuracy rate of 71.1%; Furthermore, the classification accuracy rate for males and females were 69.2 and 78.6%, respectively. Hospitals located in remote areas, such as Tibet and Yunnan, yielded a classification accuracy rate of 76.5% for collected patient samples, whereas hospitals in non-remote areas achieved a classification accuracy rate of 71.1%.ConclusionThe attained accuracy rate of 73.1% holds significant implications for the feasibility of the method. While not impeccable, this level of accuracy highlights the potential for further improvements. The development of this algorithm has the potential to revolutionize nutritional risk assessment by providing healthcare professionals and individuals with a non-invasive, cost-effective, and easily accessible tool.

show abstract

Section: Methodsmentioning

confidence: 99%

Establishing a machine learning model for predicting nutritional risk through facial feature recognition

Wang,

He,

Long

2023

Front. Nutr.

View full text Add to dashboard Cite

show abstract

“…In particular, remarkable success has been achieved using deep CNN; [50], [51], [52] utilized deep CNN to learn various characteristics of human hair for automatic hair segmentation. Recently, some approaches [3], [5], [53] have been based on encoder-decoder network architectures to utilize multi-scale features for predicting finely detailed hair masks. In a subsequent study [6], a border refinement module was added to enhance hair segmentation and refine the details of hair borders.…”

Section: Human Hair Segmentationmentioning

confidence: 99%

Fine-Grained Human Hair Segmentation Using a Text-to-Image Diffusion Model

Kim,

Lee,

Yoo

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Human hair segmentation is essential for face recognition and for achieving natural transformation of style transfer. However, it remains a challenging task due to the diverse appearances and complex patterns of hair in image. In this study, we propose a novel method utilizing diffusion-based generative models, which have been extensively researched in recent times, to effectively capture and to finely segment human hair. In diffusion-based models, an internal visual representation during the denoising process contains pixel-level rich information. Inspired by this aspect, we introduce diffusion-based models for segmenting fine-grained human hair. Specifically, we extract the representation from the diffusion-based models, which contains pixel-level semantic information, and then train a segmentation network using it. Particularly, to more finely segment human hair, our approach employs the representation from a text-toimage diffusion model, conditioned on text information, to extract more relevant information for human hair, thereby predicting detailed hair masks. To validate our method, we conducted experiments on three distinct hair-related datasets with unique characteristics: Figaro-1k, CelebAMask-HQ, and Face Synthetics. The experimental results show the improved performance of our proposed method across all three datasets, outperforming existing methods in terms of mIoU (mean intersection over union), accuracy, precision, and F1-score. This is particularly evident in its ability to accurately capture and finely segment human hair from background and non-hair elements. This demonstrates the effectiveness of our method in accurately and finely segmenting human hair with complex characteristics. Our research contributes not only to the fine-grained segmentation of human hair but also to the application of generative models in semantic segmentation tasks. We hope that the proposed method will be applied for detailed semantic segmentation in various fields in the future.

show abstract

“…The accuracy of the method proposed in this study is comparable to previous methods and has shown an accuracy of over 90% on multiple test sets. The ultra-lightweight model in this work only used the 1.10 million parameter, which is far smaller than some existing segmentation models that can be used for real-time detection [46,47], and demonstrated good robustness and generalization ability in various test sets. However, we only used images of a single growth stage and height, while many previous research methods have used deep learning with more dimensional data.…”

Section: Comparison Of Generalization Ability Of Different Models On ...mentioning

confidence: 99%

Segmentation of Wheat Lodging Areas from UAV Imagery Using an Ultra-Lightweight Network

Feng,

Wang,

Wang

et al. 2024

Agriculture

View full text Add to dashboard Cite

Crop lodging is an important cause of direct economic losses and secondary disease transmission in agricultural production. Most existing methods for segmenting wheat lodging areas use a large-volume network, which poses great difficulties for annotation and crop monitoring in real time. Therefore, an ultra-lightweight model, Lodging-U2NetP (L-U2NetP), based on a novel annotation strategy which crops the images before annotating them (Crop-annotation), was proposed and applied to RGB images of wheat captured with an unmanned aerial vehicle (UAV) at a height of 30 m during the maturity stage. In the L-U2NetP, the Dual Cross-Attention (DCA) module was firstly introduced into each small U-structure effectively to address semantic gaps. Then, Crisscross Attention (CCA) was used to replace several bulky modules for a stronger feature extraction ability. Finally, the model was compared with several classic networks. The results showed that the L-U2NetP yielded an accuracy, F1 score, and IoU (Intersection over Union) for segmenting of 95.45%, 93.11%, 89.15% and 89.72%, 79.95%, 70.24% on the simple and difficult sub-sets of the dataset (CA set) obtained using the Crop-annotation strategy, respectively. Additionally, the L-U2NetP also demonstrated strong robustness in the real-time detection simulations and the dataset (AC set) obtained using the mainstream annotation strategy, which annotates images before cropping (Annotation-crop). The results indicated that L-U2NetP could effectively extract wheat lodging and the Crop-annotation strategy provided a reliable performance which is comparable with that of the mainstream one.

show abstract

Real-Time Hair Segmentation Using Mobile-Unet

Cited by 14 publications

References 17 publications

Establishing a machine learning model for predicting nutritional risk through facial feature recognition

Establishing a machine learning model for predicting nutritional risk through facial feature recognition

Fine-Grained Human Hair Segmentation Using a Text-to-Image Diffusion Model

Segmentation of Wheat Lodging Areas from UAV Imagery Using an Ultra-Lightweight Network

Contact Info

Product

Resources

About