Images Of Human Body Research Articles

Visible Korean based on true color sectioned images for making realistic digital human, twenty years' record: a review.

Visible Korean (VK) consists of two-dimensional (2D) images and three-dimensional (3D) models. The VK is used in various educational tools and research sources for anatomy. In this paper, we report on the records of the VK over 20years. Research papers related to Visible Korean were reviewed. Through this report of VK records, we highlighted the essential points for making true color and ultra-high-resolution sectioned images of human and animal bodies, for making various 2D and 3D applications from the sectioned images, and for good use of the sectioned images and their applications. In this metaverse age that various virtual environments are required in medical education and research, the VK dataset meets the reality of virtual human models as fundamental data owing to the actual color and high resolution of the VK dataset.

Theta activity discriminates high-level, species-specific body processes

Abstract Among social stimuli that trigger rapid reactions, body images occupy a prominent place. Given that bodies carry information about other agents’ intentions, actions and emotional expressions, a foundational question concerns the neural basis of body processing. Previous functional magnetic resonance imaging (fMRI) studies have investigated this but were not yet able to clarify the time course and its functional significance. The present EEG study investigated the role of slow oscillatory cortical activity in body processing and species-specificity. Human participants viewed naturalistic images of human and monkey bodies, faces, and objects, along with mosaic-scrambled versions to control for low-level visual features. Analysis of event-related theta power (4–7 Hz) combined with data-driven methods revealed a strong, body-evoked neural response that is specific to human bodies and spans a widespread scalp region during a time window of 200–550 ms after the onset of the body image. Our results corroborate recent research proposing a species-specific cortical network of human body processing. We submit that this network may play an essential role in linking body processes to movement intentions.

Calculating Body Mass Index From 2D Image Using Convolutional Neural Networks and Anthropometric Measurements

Human body images encapsulate valuable biometric information. encompassing factors such as pupil color, gender, and weight. Of particular significance, body weight serves as a robust indicator of overall health. In alignment with recent health. science studies, this paper explores the viability of analyzing body weight through two- dimensional (2D) frontal-view human body images, utilizing the widely accepted Body Mass Index (BMI) as measure. To address varying levels of difficulty, three feasibility prob- lems are investigated, ranging from easy to hard. A comprehen sive framework is developed for body weight analysis, proposing the computation of five anthropometric features for precise body weight characterization. The correlation between the extracted anthropometric features and BMI values is thoroughly analyzed, affirming the usability of the selected features. For this study, a visual-body-to-BMI dataset is meticulously collected and cleaned, comprising 5900 images of 2950 subjects with corresponding labels for gender, height, and weight. findings demonstrate the feasibility of analyzing body The weight from 20 body images, with the proposed method outperforming two state-of-the-art facial image-based weight analysis approaches in most cases. Index Terms : Body weight analysis, visual analysis of body mass index (BMI), anthropometric features, visual-body-to- BMI dataset

Large Occluded Human Image Completion via Image-Prior Cooperating

The completion of large occluded human body images poses a unique challenge for general image completion methods. The complex shape variations of human bodies make it difficult to establish a consistent understanding of their structures. Furthermore, as human vision is highly sensitive to human bodies, even slight artifacts can significantly compromise image fidelity. To address these challenges, we propose a large occluded human image completion (LOHC) model based on a novel image-prior cooperative completion strategy. Our model leverages human segmentation maps as a prior, and completes the image and prior simultaneously. Compared to the widely adopted prior-then-image completion strategy for object completion, this cooperative completion process fosters more effective interaction between the prior and image information. Our model consists of two stages. The first stage is a transformer-based auto-regressive network that predicts the overall structure of the missing area by generating a coarse completed image at a lower resolution. The second stage is a convolutional network that refines the coarse images. As the coarse result may not always be accurate, we propose a Dynamic Fusion Module (DFM) to selectively fuses the useful features from the coarse image with the original input at spatial and channel levels. Through extensive experiments, we demonstrate our method’s superior performance compared to state-of-the-art methods.

Prior based Pyramid Residual Clique Network for human body image super-resolution

Recent research in the analysis of human images, such as human parsing and pose estimation, usually requires input images to have a sufficiently high resolution. However, small images of people are commonly encountered in our daily lives, particularly in surveillance applications. This paper aims to ultra-resolve a tiny person image to its high-resolution counterpart by learning effective feature representations and exploiting useful human body prior knowledge. First, we propose the Residual Clique Block (RCB) to fully exploit compact feature representations for image Super-Resolution (SR). Second, a series of RCBs are cascaded in a coarse-to-fine manner to construct the Pyramid Residual Clique Network (PRCN), which simultaneously reconstructs multiple SR results (e.g. 2×, 4×, and 8×) in one feed-forward pass. Third, we utilize the human parsing map as the shape prior, and the high-frequency sub-bands of Uniform Discrete Curvelet Transform (UDCT) as the texture prior to enhance the details of reconstructed human body image. Experimental results demonstrate that our proposed method achieves state-of-the-art performance with superior visual quality and PSNR/SSIM scores. Moreover, we show that our results can considerably enhance the performance of human parsing and pose estimation tasks.

A Body Map Beyond Perceptual Experience.

The human body is perhaps the most ubiquitous and salient visual stimulus that we encounter in our daily lives. Given the prevalence of images of human bodies in natural scene statistics, it is no surprise that our mental representations of the body are thought to strongly originate from visual experience. Yet, little is still known about high-level cognitive representations of the body. Here, we retrieved a body map from natural language, taking this as a window into high-level cognitive processes. We first extracted a matrix of distances between body parts from natural language data and employed this matrix to extrapolate a body map. To test the effectiveness of this high-level body map, we then conducted a series of experiments in which participants were asked to classify the distance between pairs of body parts, presented either as words or images. We found that the high-level body map was systematically activated when participants were making these distance judgments. Crucially, the linguistic map explained participants' performance over and above the visual body map, indicating that the former cannot be simply conceived as a by-product of perceptual experience. These findings, therefore, establish the existence of a behaviorally relevant, high-level representation of the human body.

Camera-based physiological measurement: Recent advances and future prospects

Camera-based measurement of vital signs utilizes imaging equipment to measure physiological changes by analyzing human body images. The contactless physiological measurement provides benefits across various fields, from healthcare and clinical trial to telemedicine and exercise scenarios. On the basis of advances in medicine, optics, and computer vision, these technologies have made significant progress. Research in this area has grown exponentially in recent years. In this review, we provide a comprehensive and meticulous overview of camera-based measurements of physiological vital signs, including four aspects: data acquisition, methodology, vital signs and applications. It covers the measurement of heart rate, heart rate variability, respiratory rate, blood pressure, and oxygen saturation, using various technologies including motion detection, signal processing, and deep learning. We also present newly released datasets and applications in new areas. At last, future promising prospects and potential research directions are also discussed.

From prediction to measurement, an efficient method for digital human model obtainment

Digital human has been increasingly used in industry, for example in Metaverse which has been a popular topic in recent years. The existing method of obtaining digital human models are either expensive or lack of accuracy. In this paper, we discuss a novel method to reconstruct a 3D human model from 2D images captured by a monocular camera. The input of our method only requires a set of rotated human body images that can accept slight movement. First, we apply a deep learning method to predict an initial 3D human body model from multi-view human body images. Then the total detailed digital human model will be computed and optimized. The typical method requires the human body and cameras fixed to obtain a visual hull from a significant number of camera images. This could be extremely expensive and inconvenient when such an application is developed for online users. Compared to the structural lighting measurement system, our predict-optimized framework only requires several input images captured by personal equipment to provide enough accuracy and online use resolution results.

Application of 3D Printing Technology in Customized Garment Production

Abstract In this paper, human body images are acquired by DSLR camera shooting, and the photos are grayscaled based on the HIS model. Then, the different operations weaken the similar parts between the images, and the other parts of the photos are highlighted. The image is separated from the background by image binarization. After the contour detection of the image, the pixel coordinates of the contour points in the image are deposited into a dynamic array by the size judgment of the pixel values. The subsequent size calculation is called to make a pad. According to the human body, clothing size automation measurement has been derived from the human body characteristics of the size, the use of three-dimensional printing technology to produce clothing to meet consumer demand, and three-dimensional printing technology to support the production of customized clothing under the experimental analysis. It is verified that the data difference between the actual size of the subject’s ready-made clothes and the size of the clothes produced based on 3D printing technology is kept within ±0.48cm. The virtual fitting pressure is less than the comfort threshold of women’s upper body pressure (2.75kpa), which indicates that the clothes produced by this research will not cause physiological discomfort to the human body and, at the same time, meet the consumer’s personalized clothing customization needs.

Massage Acupoint Positioning Method of Human Body Images Based on Transfer Learning

Massage Acupoint Positioning Method of Human Body Images Based on Transfer Learning

Putting people in context: ERP responses to bodies in natural scenes.

The N190 is a body-sensitive ERP component that responds to images of human bodies in different poses. In natural settings, bodies vary in posture and appear within complex, cluttered environments, frequently with other people. In many studies, however, such variability is absent. How does the N190 response change when observers see images that incorporate these sources of variability? In two experiments (N = 16 each), we varied the natural appearance of upright and inverted bodies to examine how the N190 amplitude, latency, and the Body-Inversion Effect (BIE) were affected by natural variability. In Experiment 1, we varied the number of people present in upright and inverted naturalistic scenes such that only one body, a subitizable number of bodies, or a "crowd" was present. In Experiment 2, we varied the natural body appearance by presenting bodies either as silhouettes or with photographic detail. Further, we varied the natural background appearance by either removing it or presenting individual bodies within a rich environment. Using component-based analyses of the N190, we found that the number of bodies in a scene reduced the N190 amplitude, but didn't affect the BIE (Experiment 1). Naturalistic body and background appearance (Experiment 2) also affected the N190, such that component amplitude was dramatically reduced by naturalistic appearance. To complement this analysis, we examined the contribution of spatiotemporal features (i.e., electrode × time point amplitude) via SVM decoding. This technique allows us to examine which timepoints across the entire waveform contribute the most to successful decoding of body orientation in each condition. This analysis revealed that later timepoints (after 300ms) contribute most to successful orientation decoding. These results demonstrate that natural appearance variability affects body processing at the N190 and that later ERP components may make important contributions to body processing in natural scenes.

A Segmentation Method of Serialized Human Body Slices Based on Matting Strategy and Skeleton Extraction.

In this paper, a semiautomatic image segmentation method for the serialized body slices of the Visible Human Project (VHP) is proposed. In our method, we first verified the effectiveness of the shared matting method for the VHP slices and utilized it to segment a single image. Then, to meet the need for the automatic segmentation of serialized slice images, a method based on the parallel refinement method and flood-fill method was designed. The ROI (region of interest) image of the next slice can be extracted by using the skeleton image of the ROI in the current slice. Utilizing this strategy, the color slice images of the Visible Human body can be continuously and serially segmented. This method is not complex but is rapid and automatic with less manual participation. The experimental results show that the primary organs of the Visible Human body can be accurately extracted.

Self-supervised feature matched virtual try-on

Abstract Virtual try-on is a technology that enables users to preview the effect of wearing a target garment without wearing the actual garment. However, existing image-based virtual try-on methods often require additional human parsing or segmentation operations to generate intermediate representations required for garment deformation and texture fusion. These operations not only increase the computational complexity and memory consumption, but also limit the real-time and portability of virtual try-on. Additionally, inaccurate parsing results can lead to misleading final generated images. To overcome these challenges, we propose a self-supervised feature matched virtual try-on network, which can directly generate high-quality try-on results from human body images and target clothing images without any additional input. Specifically, we design an optical flow warp module, which focuses on the optical flow changes between the person image and the clothing image to achieve accurate clothing alignment and deformation. Furthermore, a feature refine warp module is designed to enhance the features of the extracted optical flow information and the original character segmentation and analysis operations, reducing the influence of background clutter features on the content, and ensuring that the wrinkles and deformation of the replacement clothes are close to the original clothes. The feature match module is developed to calculate the feature matching loss of the converted clothing and the generated results of the teacher network and the student network, and the corresponding knowledge is distilled and passed to the student network to assist in self-supervised training. We conduct experiments on the VITON dataset and show that our model can generate high quality and high resolution, and our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively.

Infants’ representations of the infant body in the first year of life: a preferential looking time study

Representing others’ bodies is of fundamental importance for interacting with our environment, yet little is known about how body representations develop. Previous research suggests that infants have expectations about the typical structure of human bodies from relatively early in life, but that these expectations are dependent on how closely the stimuli resemble the bodies infants are exposed to in daily life. Yet, all previous studies used images of adult human bodies, and therefore it is unknown whether infants’ representations of infant bodies follow a similar developmental trajectory. In this study we investigated whether infants have expectations about the relative size of infant body parts in a preferential looking study using typical and disproportional infant bodies. We recorded the looking behaviour of three groups of infants between 5 and 14 months of age while they watched images of upright and inverted infant bodies, typical and proportionally distorted, and also collected data on participants’ locomotor abilities. Our results showed that infants of all ages looked equally at the typical and proportionally distorted infant body stimuli in both the upright and inverted conditions, and that their looking behaviour was unrelated to their locomotor skills. These findings suggest that infants may need additional visual experience with infant bodies to develop expectations about their typical proportions.

Measurement of human body parameters for human postural assessment via single camera.

We present a camera-based human body parameters measurement approach and develop a human postural assessment system. The approach combines the conventional contact measurement method and the non-contact measurement method to overcome some shortcomings in terms of time, expense, and professionalism in early methods. The entire measurement system consists of a computer, a high-definition camera, and the sticky points that are applied to the participant's body before the measurement. The camera captures the triple view image of human body. Then, the human body outline and the joint points of the human skeleton are extracted to locate the bone feature points. Finally, measurements and extractions of the human parameters are made. Experimental results demonstrate that the global postural assessment system provides quantitative guidance for human postural evaluation, and it completely changes how human postural is evaluated. The postural assessment system is significant for early diagnosis of diseases and medical rehabilitation treatment.

DeepPortraitDrawing: Generating human body images from freehand sketches

Various methods for generating realistic images of objects and human faces from freehand sketches have been explored. However, generating realistic human body images from sketches is still a challenging problem. It is, first because of the sensitivity to human shapes, second because of the complexity of human images caused by body shape and pose changes, and third because of the domain gap between realistic images and freehand sketches. In this work, we present DeepPortraitDrawing, a deep generative framework for converting roughly drawn sketches to realistic human body images. To encode complicated body shapes under various poses, we take a local-to-global approach. Locally, we employ semantic part auto-encoders to construct part-level shape spaces, which are useful for refining the geometry of an input pre-segmented hand-drawn sketch. Globally, we employ a cascaded spatial transformer network to refine the structure of body parts by adjusting their spatial locations and relative proportions. Finally, we use a style-based generator as the global synthesis network for the sketch-to-image translation task which is modulated by segmentation maps for semantic preservation. Extensive experiments have shown that given roughly sketched human portraits, our method produces more realistic images than the state-of-the-art sketch-to-image synthesis techniques.

Prediction and prevention system for Severe Acute Respiratory Syndrome CoronaVirus 2 infection by preempting the onset of a cough.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is fast becoming one of the most significant infections worldwide. Of all the causes of SARS-CoV-2 infection, airborne-droplet infection via coughing is the most common. Therefore, if predicting the onset of a cough and preventing infection were possible, it would have a globally positive impact. Here, we describe a new prediction and prevention system for SARS-CoV-2 infection. Usually, air is inhaled prior to coughing, and the cough, which contains droplets of the virus, then occurs during acute exhalation. Therefore, if we can predict the onset of a cough, we can prevent the spread of SARS-CoV-2. At Tohoku University, a diagnosis system for evaluating swallowing motions and peripheral circulation has already been developed, and our prediction system can be integrated into this system. Using three-dimensional human body imaging, we developed a prediction system for preempting the onset of a cough. If we can predict the onset a cough, we can prevent the spread of SARS-CoV-2 infection, by decreasing the shower of virally active airborne droplets. Here, we describe the newly developed prediction and prevention system for SARS-CoV-2 infection that preempts the onset of a cough.Clinical Relevance- If predicting the onset of a cough and preventing infection were possible, it would have a globally positive impact. Here, we describe the newly developed prediction and prevention system for SARS-CoV-2 infection.

Classification of female body shape based on two-dimensional image and computer vision technology

Traditional body classification methods are usually based on three-dimensional human body data. With the development of computer vision technology, two-dimensional (2D) anthropometry technology has garnered a great deal of research attention in the field of anthropometry. This paper presents a body shape classification and discrimination method using 2D images based on computer vision technology. The research included three main parts. (1) Index extraction of body shape classification based on computer vision. The orthogonal 2D human body image information of 362 young female samples was extracted. After normalizing the body height, three body shape classification indexes were separated: the body height pixel value ( H), the feature of the projected unit area ( ρ), and the feature of the projected area ratio of the front and side of the human body ( F). (2) Two-dimensional human body shape classification based on the two-step cluster model. The optimal classification number was determined, and the characteristics of each type of body shape were analyzed. (3) Automatic discrimination of the 2D human body shape based on the Bayesian algorithm. The correct rate of recognition was 94.8%. The results indicate that the body shape classification method based on computer vision technology and the selection of the proposed classification indexes are effective, and the accuracy of body shape recognition is high. In this paper, the classification of human body shape based on 2D digital images was realized, and this method can be applied to 2D anthropometry and other related fields.

A Machine Learning-Assisted Inversion Method for Solving Biomedical Imaging Based on Semi-Experimental Data

Machine learning approaches have been extensively utilized in the field of inverse scattering problems. Typically, the training dataset is generated synthetically using ideal radiation sources such as plane waves or cylindrical waves. However, the testing data often consist of experimental data that take into account the antenna port couplings and waveform distortions within the system. While noise can be artificially added to synthetic data, it may not accurately represent the real experimental noise. Consequently, the application of machine learning-assisted inversion techniques may encounter challenges when the training dataset differs significantly from the experimental data. In this paper, we propose an experimental system specifically designed for human body imaging. A semi-experimental training dataset is constructed using full-wave simulation software, incorporating the relative permittivities of common human tissues. Furthermore, the system noise is meticulously considered through full-wave simulation, enhancing the authenticity of the dataset. A back-propagation scheme is firstly employed to obtain the rough reconstructed images. Then, the U-net convolutional neural network (CNN) is employed to map these rough images to high-resolution ones. Numerical results demonstrate that, in comparison to networks trained solely on synthetic data, the network trained using semi-experimental data achieves superior reconstruction results with lower errors and improved image quality.

통계적 신체 외형 데이터베이스를 활용한 실루엣으로부터의 3차원 인체 외형 예측

In this paper, we present a method for estimating full 3D shapes from given 2D silhouette images of human bodies. Because the silhouette only consists of the partial information on the true shape, it is an ill-posed problem. To address the problem, we use the statistical human shape space obtained from the existing large 3D human shape database. The method consists of three steps. First, we extract the boundary pixels and their appropriate normal vectors from the input silhouette images. Then, we initialize the correspondences of each pixel to the vertex of the statistically-deformable 3D human model. Finally, we numerically optimize the parameters of the statistical model to fit best to the given silhouettes. The viability and the robustness of the method is demonstrated with various experiments.