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Through an investigation of the body and its oppression by the church, the medical profession and the state, this book reveals the actual horrors lying beneath fictional horror in settings as diverse as the monastic community, slave plantation, operating theatre, Jewish ghetto and battlefield trench. The book provides original readings of canonical Gothic literary and film texts including The Castle of Otranto, The Monk, Frankenstein, Dracula and Nosferatu. This collection of fictionalised dangerous bodies is traced back to the effects of the English Reformation, Spanish Inquisition, French Revolution, Caribbean slavery, Victorian medical malpractice, European anti-Semitism and finally warfare, ranging from the Crimean up to the Vietnam War. The endangered or dangerous body lies at the centre of the clash between victim and persecutor and has generated tales of terror and narratives of horror, which function to either salve, purge or dangerously perpetuate such oppositions. This ground-breaking book will be of interest to academics and students of Gothic studies, gender and film studies and especially to readers interested in the relationship between history and literature.

Automated human posture estimation (A-HPE) systems need delicate methods for detecting body parts and selecting cues based on marker-less sensors to effectively recognize complex activity motions. Recognition of human activities using vision sensors is a challenging issue due to variations in illumination conditions and complex movements during the monitoring of sports and fitness exercises. In this paper, we propose a novel A-HPE method that intelligently identifies human behaviours by utilizing saliency silhouette detection, robust body parts model and multidimensional cues from full-body silhouettes followed by an entropy Markov model. Initially, images are pre-processed and noise is removed to obtain a robust silhouette. Body parts models are then used to extract twelve key body parts. These key body parts are further optimized to assist the generation of multidimensional cues. These cues include energy, optical flow and distinctive values that are fed into quadratic discriminant analysis to discriminate cues which help in the recognition of actions. Finally, these optimized patterns are further processed by a maximum entropy Markov model as a recognizer engine based on transition and emission probability values for activity recognition. For evaluation, we used a leave-one-out cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving better body parts detection and higher recognition accuracy over four benchmark datasets. The proposed method will be useful for man-machine interactions such as 3D interactive games, virtual reality, service robots, e-health fitness, and security surveillance.

Even though most existing monocular 3D human pose estimation methods achieve very competitive performance, they are limited in estimating heterogeneous human body parts with the same decoder architecture. In this work, we present an approach to build a part-aware 3D human pose estimator to better deal with these heterogeneous human body parts. Our proposed method consists of two learning stages: (1) searching suitable decoder architectures for specific parts and (2) training the part-aware 3D human pose estimator built with these optimized neural architectures. Consequently, our searched model is very efficient and compact and can automatically select a suitable decoder architecture to estimate each human body part. In comparison with previous state-of-the-art models built with ResNet-50 network, our method can achieve better performance and reduce 64.4% parameters and 8.5% FLOPs (multiply-adds). We validate the robustness and stability of our searched models by conducting extensive and rigorous ablation experiments. Our method can advance state-of-the-art accuracy on both the single-person and multi-person 3D human pose estimation benchmarks with affordable computational cost.

Skeleton-based human action recognition has attracted widespread interest, as skeleton data are extremely robust to changes in lighting, camera views, and complex backgrounds. In recent studies, transformer-based methods are proposed for the encoding of the latent information underlying the 3D skeleton. These methods focus on modeling the relationships of joints in skeleton sequences without any predefined graphical information by self-attention mechanism and have been proven to be effective. But there are two challenging issues ignored in these methods: the utilization of human body-related and dynamic semantic information. In this work, we propose a novel spatial–temporal semantic decomposition transformer network (STSD-TR) that models dependencies between joints with body parts semantics and sub-action semantics. In our STSD-TR, a body parts semantic decomposition module (BPSD) is used to extract body parts semantic information from 3D coordinates of joints, and then a temporal-local spatial–temporal attention module (TL-STA) is used to capture the relationships of joints in several consecutive frames which can be understood as local sub-action semantic information. Finally, a global spatial–temporal module (GST) is used to aggregate the temporal-local features and generate a global spatial–temporal representation. Moreover, we design a BodyParts-Mix strategy which mixes body parts from two people in a unique manner and further boosts the performance. Compared with the state-of-the-art methods, our method achieves competitive performance on two large-scale datasets.

Faces and body parts play a crucial role in human social communication. Numerous studies emphasize their significance as sociobiological stimuli in daily interactions. Two experiments were conducted to examine the following: (a) whether faces or body parts are processed more quickly than other visual objects when relevant to the task and serving as targets, and (b) the effects of presenting faces or body parts as distractors on task reaction times and error rates. The first experiment focused on either faces or body parts, with five different visual objects. The second experiment examined effector body parts (e.g., hands) and core body parts (e.g., the torso), paired with the same visual objects. Thirty-six participants took part in the study, equally divided between Experiment 1 (N = 18) and Experiment 2 (N = 18). Participants were instructed to find if a target item, indicated by a green placeholder, matched a previously presented word cue, while they were instructed to keep ignoring the singleton object that was surrounded by the red placeholder. The results indicated that participants responded more quickly when finding faces but not body parts in Experiment 1. No such advantage was seen in Experiment 2 for either effector or core body parts compared to other objects. Interestingly, when faces were presented as distractors as a singleton, reaction times increased (Experiment 1), indicating that faces capture attention. This effect was not observed for effector or core body parts (Experiment 2). These findings suggest that faces capture attention more effectively than body parts. Les visages et les parties du corps jouent un rôle crucial dans la communication sociale humaine. De nombreuses études soulignent leur importance en tant que stimuli sociobiologiques dans les interactions quotidiennes. Deux expériences ont été menées pour examiner les points suivants : (a) si les visages ou les parties du corps sont traités plus rapidement que d'autres objets visuels lorsqu'ils sont pertinents pour la tâche et servent de cibles, et (b) les effets de la présentation de visages ou de parties du corps comme distracteurs sur les temps de réaction à la tâche et les taux d'erreur. La première expérience portait sur les visages ou les parties du corps, avec cinq objets visuels différents. La deuxième expérience portait sur les parties du corps effectrices (par exemple, les mains) et les parties du corps centrales (par exemple, le torse), associées aux mêmes objets visuels. Trente-six participants ont pris part à l'étude, répartis également entre l'expérience 1 (n = 18) et l'expérience 2 (n = 18). Les participants devaient trouver si un objet cible, indiqué par un marqueur vert, correspondait à un indice de mot précédemment présenté, tout en ignorant l'objet unique entouré d'un marqueur rouge. Les résultats indiquent que les participants ont réagi plus rapidement lorsqu'ils ont trouvé des visages, mais pas des parties du corps dans l'expérience 1. Aucun avantage de ce type n'a été observé dans l'expérience 2 pour les parties du corps effectrices ou centrales par rapport aux autres objets. Fait intéressant, lorsque les visages étaient présentés comme distracteurs en tant qu'élément unique, les temps de réaction augmentaient (expérience 1), ce qui indique que les visages captent l'attention. Cet effet n'a pas été observé pour les parties du corps effectrices ou centrales (expérience 2). Ces résultats suggèrent que les visages captent l'attention plus efficacement que les parties du corps. Public Significance Statement The study explores the significant role of faces and body parts in social communication. Two experiments were conducted to figure out whether faces and body parts are processed more quickly than other visual objects and how they affect reaction times and error rates when presented as distractors. Results showed that faces, but not body parts, are processed more rapidly and capture attention more effectively than other objects. These findings provide insight into the neural mechanisms involved in face processing, with potential applications in psychology and artificial intelligence.

Background Monitoring the body temperature of premature infants is vital, as it allows optimal temperature control and may provide early warning signs for severe diseases such as sepsis. Thermography may be a non-contact and wireless alternative to state-of-the-art, cable-based methods. For monitoring use in clinical practice, automatic segmentation of the different body regions is necessary due to the movement of the infant. Methods This work presents and evaluates algorithms for automatic segmentation of infant body parts using deep learning methods. Based on a U-Net architecture, three neural networks were developed and compared. While the first two only used one imaging modality (visible light or thermography), the third applied a feature fusion of both. For training and evaluation, a dataset containing 600 visible light and 600 thermography images from 20 recordings of infants was created and manually labeled. In addition, we used transfer learning on publicly available datasets of adults in combination with data augmentation to improve the segmentation results. Results Individual optimization of the three deep learning models revealed that transfer learning and data augmentation improved segmentation regardless of the imaging modality. The fusion model achieved the best results during the final evaluation with a mean Intersection-over-Union (mIoU) of 0.85, closely followed by the RGB model. Only the thermography model achieved a lower accuracy (mIoU of 0.75). The results of the individual classes showed that all body parts were well-segmented, only the accuracy on the torso is inferior since the models struggle when only small areas of the skin are visible. Conclusion The presented multi-modal neural networks represent a new approach to the problem of infant body segmentation with limited available data. Robust results were obtained by applying feature fusion, cross-modality transfer learning and classical augmentation strategies.

Accurate cattle body detection can significantly enhance the efficiency and quality of animal husbandry production. Traditional manual observation approaches are not only inefficient but also lack objectivity, while computer vision-based methods demand prolonged training periods and present challenges in implementation. To address these issues, this paper develops a novel precise cattle body detection solution, namely YOLOv5-VF-W3. By introducing the Varifocal loss, the YOLOv5-VF-W3 model can handle unbalanced samples and focus more attention on difficult-to-recognize instances. Additionally, the introduction of the WIoUv3 loss function provides the model with a wise gradient gain allocation strategy. This strategy reduces the competitiveness of high-quality anchor boxes while mitigating harmful gradients produced by low-quality anchor boxes, thereby emphasizing anchor boxes of ordinary quality. Through these enhancements, the YOLOv5-VF-W3 model can accurately detect cattle bodies, improving the efficiency and quality of animal husbandry production. Numerous experimental results have demonstrated that the proposed YOLOv5-VF-W3 model achieves superior cattle body detection results in both quantitative and qualitative evaluation criteria. Specifically, the YOLOv5-VF-W3 model achieves an mAP of 95.2% in cattle body detection, with individual cattle detection, leg detection, and head detection reaching 95.3%, 94.8%, and 95.4%, respectively. Furthermore, in complex scenarios, especially when dealing with small targets and occlusions, the model can accurately and efficiently detect individual cattle and key body parts. This brings new opportunities for the development of precision livestock farming.

Recent years introduced process and material innovations in the design and manufacturing of lightweight body parts for larger scale manufacturing. However, lightweight materials and new manufacturing technologies often carry a higher environmental burden in earlier life cycle stages. The prospective life cycle evaluation of lightweight body parts remains to this day a challenging task. Yet, a functioning evaluation approach in early design stages is the prerequisite for integrating assessment results in engineering processes and thus allowing for a life cycle oriented decision making. The current paper aims to contribute to the goal of a prospective life cycle evaluation of fiber-reinforced lightweight body parts by improving models that enable to predict energy and material flows in the manufacturing stage. To this end, a modeling and simulation approach has been developed that integrates bottom-up process models into a process chain model. The approach is exemplarily applied on a case study of a door concept. In particular, the energy intensity of compression molding of glass fiber and carbon fiber sheet molding compounds has been analyzed and compared over the life cycle with a steel reference part.

Previously, the lack of a standard body part ontology has been identified as a critical deficiency needed to enable enterprise imaging. This whitepaper aims to provide a comprehensive assessment of anatomical ontologies with the aim of facilitating enterprise imaging. It offers an overview of the process undertaken by the Health Information Management Systems Society (HIMSS) and Society for Imaging Informatics in medicine (SIIM) Enterprise Imaging Community Data Standards Evaluation workgroup to assess the viability of existing ontologies for supporting cross-disciplinary medical imaging workflows. The report analyzes the responses received from representatives of three significant ontologies: SNOMED CT, LOINC, and ICD, and delves into their suitability for the complex landscape of enterprise imaging. It highlights the strengths and limitations of each ontology, ultimately concluding that SNOMED CT is the most viable solution for standardizing anatomy terminology across the medical imaging community.

INTRODUCTION: This work represents a significant step forward by harnessing the power of deep learning to classify X-ray images into distinct body parts. Over the years X-ray pictures were evaluated manually. OBJECTIVE: Our aim is to automate X-ray interpretation using deep learning techniques. METHOD: Leveraging cutting-edge frameworks such as FastAI and TensorFlow, a Convolutional Neural Network (CNN) has been meticulously trained on a dataset comprising DICOM images and their corresponding labels. RESULT: The results achieved by the model are indeed promising, as it demonstrates a remarkable ability to accurately identify various body parts. CNN shows 97.38% performance by compared with other classifiers. CONCLUSION: This innovation holds the potential to revolutionize medical diagnosis and treatment planning through the automation of image analysis, marking a substantial leap forward in the field of healthcare technology.