Human Shape Research Articles

‘Retaining their bestial character’: Fashion, fear of degeneration and animal protection in late imperial Russia

Abstract This article examines the possible roots of the change in cultural attitudes, due to which fashionable objects of animal origin started to be viewed negatively in Russian fashion periodicals and advice manuals of the early twentieth century. Among these influences, we may count the development of the animal protection movement, with the Russian Society for the Protection of Animals established in 1865. Although initially this association was not concerned with fashion, it shaped the notion of the cruel treatment of animals, drawing public attention to this issue. An analysis of non-fiction writing by Fyodor Dostoevsky and Vikentii Veresaev will show that, while the Society’s agenda was ambiguous and met with a mixed response, the new sensibilities this group promoted were increasingly internalized by the educated public. On the other hand, fashion criticism during the era argued that fashion distorts and deforms human shape by giving it animal features – that is, it assists degeneration. A reversal of evolution, in this case triggered by brutality, was similarly evoked by the proponents of animal protection. The newly perceived inappropriateness of certain fashionable objects was thus due to the double ‘animality’ they were believed to impart to the wearer: that of following the ‘fashion instinct’ and that of being complicit in violence.

Bencao literature investigation of Polygonum multiflorum (Heshouwu)

Heshouwu, derived from the root of Polygonum multiflorum (=Fallopia multiflora), is widely used in the Chinese medicine market as a traditional tonic. The emergence of heshouwu material with a human shape reflects a pursuit of its supplementing effects. However, reports of Heshouwu toxicity have repeatedly surfaced in recent years, attracting widespread concern. To clarify the situation surrounding the safety and efficacy of Heshouwu, this research utilizes a systematic review of the text and illustrations in historical bencao (materia medica) literature to investigate the origin, botanical characteristics, actions and processing of Heshouwu, as well as the origin and historical evolution of Baishouwu ("white Heshouwu").

Evolving human landscapes: a virtual laboratory approach

ABSTRACTDifferent human societies shape landscapes differently. Anthroecology theory explains this long-term differential shaping of landscapes as the product of sociocultural niche construction (SNC): an evolutionary theory coupling social change with ecosystem engineering. The evolutionary mechanisms underpinning this theory cannot be tested without experimental approaches capable of reproducing emergent selection processes acting on the combined suite of cultural, material, and ecological inheritances that determine the adaptive fitness of human individuals, groups, and societies. Agent-based modeling, as a ‘generative social science’ tool, appears ideal for this. Here we propose an agent-based virtual laboratory (ABVL) approach to generating and testing basic hypotheses on SNC as a general mechanism capable of producing the broadly varied anthroecological forms and dynamics of human landscapes from prehistory to present. While major challenges must still be overcome, a prospective modeling framework specification, guiding questions, and illustrative examples demonstrate clear potential for an ABVL to test predictions of anthroecology theory through generative social simulation.

Agents within a developmental complex adaptive system: intrauterine male hormones influence human tooth size and shape

Agents within a developmental complex adaptive system: intrauterine male hormones influence human tooth size and shape

Comfort and pressure distribution in a human contour shaped aircraft seat (developed with 3D scans of the human body).

The concept of comfort is one way for the growing airline market to differentiate and build customer loyalty. This work follows the idea that increasing the contact area between human and seat can have a positive effect on comfort [5, 6, 7]. To improve comfort, reduce weight and optimise space used, a human contour shaped seat shell and cushioning was developed. First the most common activities, the corresponding postures and seat inclination angles were defined. The imprints of these postures on a rescue mat were 3D scanned and an average human contour curve was defined. The outcome was transferred to a prototype seat that was used to test the effect on perceived comfort/discomfort and pressure distribution. The resulting human contour based prototype seat has comfort and discomfort scores comparable to a traditional seat. The prototype seat had a significantly lower average pressure between subjects' buttocks and the seat pan over a traditional seat. This study shows that it is possible to design a seat pan and backrest based on the different contours of study subjects using 3D scan technology. However, translating the 3D scans into a prototype seat also showed that this can only be seen as a first step; additionally biomechanical information and calculations are needed to create ergonomic seats. Furthermore, it is not possible to capture all different human shapes and postures and translate these into one human contour shape that fits all activities and all human sizes.

Deep Neural Networks as a Computational Model for Human Shape Sensitivity

Theories of object recognition agree that shape is of primordial importance, but there is no consensus about how shape might be represented and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed. Recent studies suggest that state-of-the-art convolutional 'deep' neural networks (DNNs) capture important aspects of human object perception. We hypothesized that these successes might be partially related to a human-like representation of object shape. Here we demonstrate that sensitivity for shape features, characteristic to human and primate vision, emerges in DNNs when trained for generic object recognition from natural photographs. We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed. In particular, although never explicitly trained for such stimuli, DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition. Even more strikingly, when tested with a challenging stimulus set in which shape and category membership are dissociated, the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments. As a whole, these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes. An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks, which is so characteristic in human development. Meeting abstract presented at VSS 2016

Anthropometric Classification of Human Hand Shapes in Korean Population

Hand tools designed without the anthropometric perspective lead to lower performance and safety. This study suggests a statistical hand shape classification with Korean anthropometric data. A total of 321 anthropometric data were used for this study. To investigate hand types, 27 anthropometric hand variables normalized by the stature of each participant and factor analysis and cluster analysis were conducted. As a result, three major factors were deduced: factors of hand breadth, palm length, and finger length. Additionally, four hand types were determined: (a) a spacious hand with short fingers, (b) a hand with short palm with above average fingers, (c) a long palm and fingers, and (d) a narrow hand and short fingers. In the male population, the spacious hand with short fingers type was dominant while the narrow hand and short fingers type was dominant in the female population. These results are expected to be preliminarily utilized in design for the Korean population.

Model-based generation of personalized full-body 3D avatars from uncalibrated multi-view photographs

According to a number of studies, the use of personalized avatars in virtual environments can enhance the immersion experience of users and the effectiveness of communication between different players. The benefits of using personalized avatars in conjunction with recent technological developments in Virtual Reality (VR) prompted an increasing demand for low-cost systems that are capable of fast and easy generation of personal avatars for use in VR applications. In this paper we present a novel model-based technique that is capable of generating personalized full-body 3D avatars from orthogonal photographs. The proposed method utilizes a statistical model of human 3D shape and a multi-view statistical 2D shape model of its corresponding silhouettes. Our technique is automatic, requiring minimal user intervention, and does not need a calibrated camera. Each component of our proposed technique is extensively evaluated and validated, in an attempt to test the geometric accuracy and identifiability of generated avatars. Furthermore, we demonstrate the use of the proposed method for generating and importing animation-ready avatars in collaborative VR environments.

Pedestrian Segmentation in Infrared Images Based on Circular Shortest Path

A novel infrared pedestrian segmentation algorithm based on the circular shortest path is proposed. The foreground containing pedestrians is estimated by saliency mapping and gray thresholding. In the foreground area, the human shape feature is introduced by a regional polar transformation. By adding the human shape coefficient to the object term of the cost function, the extracted contour can fit the human shape well while excluding most false alarms. The proposed algorithm performs better in areas with low contrast, and obtains good segmentation quantitatively and qualitatively.

Multiple Human Identification and Cosegmentation: A Human-Oriented CRF Approach With Poselets

Localizing, identifying, and extracting humans with consistent appearance jointly from a personal photo stream is an important problem and has wide applications. The strong variations in foreground and background and irregularly occurring foreground humans make this realistic problem challenging. Inspired by advancements in object detection, scene understanding, and image cosegmentation, we explore explicit constraints to label and segment human objects rather than other nonhuman objects and “stuff.” We refer to such a problem as multiple human identification and cosegmentation (MHIC). To identify specific human subjects, we propose an efficient human instance detector by combining an extended color line model with a poselet-based human detector. Moreover, to capture high-level human shape information, a novel soft shape cue is proposed. It is initialized by the human detector, then further enhanced through a generalized geodesic distance transform, and finally refined with a joint bilateral filter. We also propose to capture the rich feature context around each pixel by using an adaptive cross-region data structure, which gives a higher discriminative power than a single pixel-based estimation. The high-level object cues from the detector and the shape are then integrated with the low-level pixel cues and midlevel contour cues into a principled conditional random field (CRF) framework, which can be efficiently solved by using fast graph cut algorithms. We evaluate our method over a newly created NTU-MHIC human dataset, which contains 351 images with manually annotated groundtruth segmentation. Both visual and quantitative results demonstrate that our method achieves state-of-the-art performance for the MHIC task.

Body talk

Realistic, metrically accurate, 3D human avatars are useful for games, shopping, virtual reality, and health applications. Such avatars are not in wide use because solutions for creating them from high-end scanners, low-cost range cameras, and tailoring measurements all have limitations. Here we propose a simple solution and show that it is surprisingly accurate. We use crowdsourcing to generate attribute ratings of 3D body shapes corresponding to standard linguistic descriptions of 3D shape. We then learn a linear function relating these ratings to 3D human shape parameters. Given an image of a new body, we again turn to the crowd for ratings of the body shape. The collection of linguistic ratings of a photograph provides remarkably strong constraints on the metric 3D shape. We call the process crowdshaping and show that our Body Talk system produces shapes that are perceptually indistinguishable from bodies created from high-resolution scans and that the metric accuracy is sufficient for many tasks. This makes body "scanning" practical without a scanner, opening up new applications including database search, visualization, and extracting avatars from books.

Anatomographic Volumetric Skin-Musculoskeletal Model and Its Kinematic Deformation With Surface-Based SSD

Conventional musculoskeletal models mainly consist of bones modeled by rigid linkages, and muscles, tendons, and ligaments modeled by ideal wires. The lack of volumetric modeling of muscle makes a representation of interaction between muscles and natural muscle pathway difficult. This difficulty results in a physiologically inappropriate estimation of a muscle momentum arm that is critical for a muscle activity estimation. In this letter, we develop a volumetric skin-musculoskeletal model based on an anatomographic human shape database to improve an estimation of a muscle momentum arm. A volumetric deformation of surface skin and muscle is realized by an extended skeleton subspace deformation (SSD, the linear blend skinning algorithm) that considers a surface profile of bone with low computational cost. This extended SSD considers a sub-bone that is projected to the bone surface polygon so that the skin and muscle deformation is significantly affected by the bone surface profile. The surface-based SSD realized the natural skin deformation avoiding a penetration between skin and bones during trunk rotation that results in a physiologically appropriate estimation of a muscle momentum arm. The volumetric skin-musculoskeletal model and the surface-based SSD estimates the momentum arm of vastus lateralis with 14.1% maximum error from a literature values, though there is 44.8% maximum error with the wire musculoskeletal model. This model would accurize a muscle activity estimation that leads to a more correct understanding of human motion control/generation mechanisms.

Il problema dell'identità personale e della riproducibilità in esseri viventi post-umani

Keeping Helmuth Plessner’s philosophical anthropology in the background, the paper analyses in a post-humanist perspective the formation of the self and personal identity, taking also into account the risk relating to the conservation of the living species that will come after the widespread hybridization between machines and human beings. The interventions on the body could be eventually limited by the necessity to preserve some fundamental characteristics of the human shape, to avert the risk that new-born children lack the conditions for the fixing of the consciousness of their own selves. With reference to all of this, the paper urges on a restructuring of the imaginary used by the thought on the post-human condition to find its categories and its main developments.

Place, Space, and Time in MC Solaar’s American Francophone

Place, Space, and Time in MC Solaar’s American Francophone

Pyrodiversity is the coupling of biodiversity and fire regimes in food webs.

Fire positively and negatively affects food webs across all trophic levels and guilds and influences a range of ecological processes that reinforce fire regimes, such as nutrient cycling and soil development, plant regeneration and growth, plant community assembly and dynamics, herbivory and predation. Thus we argue that rather than merely describing spatio-temporal patterns of fire regimes, pyrodiversity must be understood in terms of feedbacks between fire regimes, biodiversity and ecological processes. Humans shape pyrodiversity both directly, by manipulating the intensity, severity, frequency and extent of fires, and indirectly, by influencing the abundance and distribution of various trophic guilds through hunting and husbandry of animals, and introduction and cultivation of plant species. Conceptualizing landscape fire as deeply embedded in food webs suggests that the restoration of degraded ecosystems requires the simultaneous careful management of fire regimes and native and invasive plants and animals, and may include introducing new vertebrates to compensate for extinctions that occurred in the recent and more distant past.This article is part of the themed issue ‘The interaction of fire and mankind’.

Shape estimation of subcutaneous adipose tissue using an articulated statistical shape model

The quantification of adipose tissue can give insights into obesity and the associated diseases. The segmentation of this tissue is however difficult, particularly due to the complex geometry and the local appearance similarities between the subcutaneous and visceral types. Shape priors can be used to regularise the segmentation of geometries with small variation in shape. However, human bodies are articulated and substantially different across subjects. In this paper, a novel method is proposed for the segmentation of the subcutaneous fat layer in full-body magnetic resonance imaging scans. The proposed method is based on a statistical shape model of the whole body surface, which is learned from geometric scans. The body model is factorised into pose and shape deformations, which allows a compact parametrisation of large variations in human shape. The proposed method is applied in the segmentation of five magnetic resonance imaging data-sets. The experiments show that the proposed model can be used to effectively segment the subcutaneous fat geometry in subjects with different body mass indices. The incorporation of the statistical model in the algorithm regularises the segmentation, and establishes correspondences between the subcutaneous fat layer of the geometries across subjects. The registration of the fat layer with a common geometry could facilitate the statistical analysis of the shape distribution across the different geometries.

Non-parametric human segmentation using support vector machine

The human category is among the most commonly captured and therefore important subject in digital cameras. As a result, human segmentation also plays an important role in the camera system. This paper presents a framework of non-parametric learning that achieves human segmentation using support vector machine (SVM). In the training stage, the training human windows are warped to a normalized size and oversegmented into regular superpixels. A two-dimensional (2D) array of SVM models is then trained by extracting various edge and color features from each superpixel. In this process of SVM training, the 2D array of SVM models automatically and effectively learns the characteristics of the human shape. Given an affinely warped human window for testing, the proposed method calculates superpixels' initial scores of belonging to a human (foreground) using the trained SVM models. Finally, the initial prediction scores are effectively propagated by optimizing a well-defined energy function using an estimated confidence map. In experiments on a publicly available challenging dataset, the proposed framework rapidly yields excellent results in human segmentation both qualitatively and quantitatively1.

Deep Neural Networks as a Computational Model for Human Shape Sensitivity.

Theories of object recognition agree that shape is of primordial importance, but there is no consensus about how shape might be represented, and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed. Recent studies suggest that state-of-the-art convolutional ‘deep’ neural networks (DNNs) capture important aspects of human object perception. We hypothesized that these successes might be partially related to a human-like representation of object shape. Here we demonstrate that sensitivity for shape features, characteristic to human and primate vision, emerges in DNNs when trained for generic object recognition from natural photographs. We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed. In particular, although never explicitly trained for such stimuli, DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition. Even more strikingly, when tested with a challenging stimulus set in which shape and category membership are dissociated, the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments. As a whole, these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes. An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks, which is so characteristic in human development.

Realization and swimming performance of the breaststroke by a swimming humanoid robot

In order to clarify the mechanics of human swimming, a full-body swimming humanoid robot called “SWUMANOID” was developed as an experimental platform for research about human swimming. SWUMANOID had a detailed human body shape, created using three-dimensional scanning and printing equipment, and was developed as an experimental model substituting for human subjects. Not only the appearance but also the methodology to realize various swimming strokes was considered. In order to reproduce complicated swimming motions with high fidelity, 20 waterproof actuators were installed. The free swimming of the crawl stroke at a velocity of 0.24 m/s was realized in the previous study. However, it could not perform the breaststroke due to mechanical limitations. The objectives of this study were to realize the breaststroke for SWUMANOID by improving its lower limbs, and to investigate the swimming performance of the breaststroke experimentally. The lower body of SWUMANOID was fully redesigned, built, and connected to the upper body. The swimming motion of the breaststroke was created based on that of an actual swimmer. A free swimming experiment was conducted in a 25 m outdoor swimming pool. In addition, in order to discuss the experimental results in detail, the experiment was reproduced by the simulation. From the experiment, it was found that SWUMANOID could perform the breaststroke successfully. The swimming speed for the stroke cycle of 2.3 s was found to be 0.12 m/s. Since this swimming speed was considered low compared to that of the actual swimmer, the reason for the discrepancy was examined by simulation. From the simulation, it was found that one main reason for the low swimming speed was insufficient output power of the motors, especially for the knee and shoulder joints.

Human Detection Based on the Generation of a Background Image and Fuzzy System by Using a Thermal Camera.

Recently, human detection has been used in various applications. Although visible light cameras are usually employed for this purpose, human detection based on visible light cameras has limitations due to darkness, shadows, sunlight, etc. An approach using a thermal (far infrared light) camera has been studied as an alternative for human detection, however, the performance of human detection by thermal cameras is degraded in case of low temperature differences between humans and background. To overcome these drawbacks, we propose a new method for human detection by using thermal camera images. The main contribution of our research is that the thresholds for creating the binarized difference image between the input and background (reference) images can be adaptively determined based on fuzzy systems by using the information derived from the background image and difference values between background and input image. By using our method, human area can be correctly detected irrespective of the various conditions of input and background (reference) images. For the performance evaluation of the proposed method, experiments were performed with the 15 datasets captured under different weather and light conditions. In addition, the experiments with an open database were also performed. The experimental results confirm that the proposed method can robustly detect human shapes in various environments.