On-barn pig weight estimation based on body measurements by structure-from-motion (SfM)

Information on the body shape of pigs is a key indicator to monitor their performance and health and to control or predict their market weight. Manual measurements are among the most common ways to obtain an indication of animal growth. However, this approach is laborious and difficult, and it may be stressful for both the pigs and the stockman. The present paper proposes the implementation of a Structure from Motion (SfM) photogrammetry approach as a new tool for on-barn animal reconstruction applications. This is possible also to new software tools allowing automatic estimation of camera parameters during the reconstruction process even without a preliminary calibration phase. An analysis on pig body 3D SfM characterization is here proposed, carried out under different conditions in terms of number of camera poses and animal movements. The work takes advantage of the total reconstructed surface as reference index to quantify the quality of the achieved 3D reconstruction, showing how as much as 80% of the total animal area can be characterized

Quantitative body shape analysis for obesity evaluation

Obesity is a public health concern as it is associated with a number of diseases, such as diabetes mellitus type 2, cardiovascular disease, some forms of renal failure and certain types of cancers. Growing evidence suggests that it is not only the amount of fat, but also its distribution in the body that is important to predict metabolic risk factors and adverse changes in organs. In this respect, it is necessary to develop convenient and inexpensive measures to characterize human body fat distribution and to investigate the unknown linkage between intrinsic adiposity and external body shape. This dissertation research aims to improve the obesity assessment by developing new quantitative measurements that comprehensively characterize body shape, and are highly relevant to intrinsic abdominal adiposity conditions. The proposed body shape descriptors were defined based on three-dimensional body images reconstructed from a custom-made stereovision body imaging system, which is particularly suitable for clinical use as an obesity monitoring equipment for its high portability and affordability. In this study, we developed a fully-automated algorithm to process T1-weighted magnetic resonance imaging (MRI) slices for abdominal adiposity measurements. This algorithm dramatically reduces the processing time and workload compared with traditional manual or semi-automatic methods for MRI processing, and greatly improves the repeatability and objectivity of fat assessments. A new obesity categorization method was then defined based on MRI adiposity data to depict characteristics of abdominal fat distribution, and the associations between the body shape descriptors and the MRI abdominal adiposity were explored. It was shown that the proposed body shape descriptors are able to capture the body shape differences between the subjects with dissimilar internal fat distribution (i.e., different categories), and to provide excellent prediction for the category of fat distribution through an optimized support-vector-machine classifier. The predictive models established in this dissertation demonstrate that the novel body shape descriptors were also effective for prediction of the volumes of abdominal visceral fat and subcutaneous fat accumulated in male and female adults. This dissertation introduces an innovative approach to assess obesity and fat distribution based on newly defined shape descriptors, and provides new findings that reveal the associations of intrinsic fat distribution with external body shapes, which enable both qualitative and quantitative assessment of obesity from body shape measurements.Biomedical Engineerin

Concise and Effective Network for 3D Human Modeling from Orthogonal Silhouettes

In this paper, we revisit the problem of 3D human modeling from two orthogonal silhouettes of individuals (i.e., front and side views). Different from our prior work {\cite{wang2003virtual}}, a supervised learning approach based on \textit{convolutional neural network} (CNN) is investigated to solve the problem by establishing a mapping function that can effectively extract features from two silhouettes and fuse them into coefficients in the shape space of human bodies. A new CNN structure is proposed in our work to exact not only the discriminative features of front and side views and also their mixed features for the mapping function. 3D human models with high accuracy are synthesized from coefficients generated by the mapping function. Existing CNN approaches for 3D human modeling usually learn a large number of parameters (from {8.5M} to {355.4M}) from two binary images. Differently, we investigate a new network architecture and conduct the samples on silhouettes as input. As a consequence, more accurate models can be generated by our network with only {2.4M} coefficients. The training of our network is conducted on samples obtained by augmenting a publicly accessible dataset. Learning transfer by using datasets with a smaller number of scanned models is applied to our network to enable the function of generating results with gender-oriented (or geographical) patterns

EMAT/Synthetic Aperture Approach to Thick-Weld Inspection

Rapid advances in automated welding and increased demands for reliable weld-quality inspection tools have created a need for new ultrasonic inspection systems. In particular, new systems capable of operation at elevated temperatures and rapid scan rates are in demand in fully and semi-automated welding applications to complement radiographic and conventional ultrasonic inspection techniques. In such applications, radiography is fundamentally limited because of its inability to detect and dimension most sharp flaws, and possible health hazards. On the other hand, conventional ultrasonic techniques are limited because they tend to be difficult to automate, require fluid couplants, and are often operator-dependent

Design For Movement: Block Pattern Design For Stretch Performancewear

This thesis is in 2 volumesPattern drafting techniques for woven block patterns have been well established. Applying existing techniques with modifications to generate patterns for modern stretch fabrics can be successful but it is often at a cost. In the development of a stretch pattern, an acceptable fit cannot be guaranteed merely by using a rationalised simple pattern profile shape. Producing a pattern, without darts, to closely adhere to the contours of the body without restricting movement, is a contradiction in design terms. In woven fabric, darts and ease are used to manipulate the fabric around the form and allow movement. However, in stretch knit fabric the development of a block pattern involves the synthesis of information from a variety of disciplines and requires a more specialist approach. This study has endeavoured to show that a new interpretation of pattern design principles is needed to create an improved stretch block pattern for stretch knit performancewear. This work has been refined based on a new method of classifying stretch fabric parameters and personal observation of the effect of stretch distortion characteristics and the changes that occur in the twodimensional pattern profile, when stretched to conform to the threedimensional body. The results of this study will provide a more SCientific and practical approach to assessing stretch fabric parameters as an integral part of block pattern design for stretch performancewear. The fabric stretch potential has been maximised to contour the body for optimum fit, providing comfort and mobility without the need for redistribution of the fabric when activity ceases. A method of creating a stretch block pattern from direct measurements to replicate the body shape and proportions was devised which can be reduplicated. This study addresses primarily the designer/pattern cutter who has a passion for good fit, which enhances comfort and mobility, who does not necessarily have a scientific background. However this study is relevant to the textile technologist concerned with proposing a standard to compare stretch fabrics for garment production. It should also appeal to the computer programmer concerned with the link between 3D body scanning and interpreting the body profile accurately in the 2D pattern draft

Penile Girth Enhancement using Amniotic Membrane in a Rabbit Model: A stereological study

Objectives: This study aimed to evaluate the efficacy of Penile Girth Enhancement (PGE) using Amniotic Membrane (AM) as a graft in a rabbit model. Additionally, stereological studies were used to obtain quantitative histological data regarding the structure of the penis. Methods: In this study, 20 adult male rabbits of similar age and weight were allocated to two sham and surgery+AM groups. Both groups underwent surgery by longitudinal Ishape midline incision of the tunica albuginea on the dorsal surface of the penis. The surgery +AM group underwent PGE by AM graft. The penile length and mid circumference were measured using a Vernier caliper before and two months after the surgery. Stereological studies were used to obtain quantitative histological data regarding the structure of the penis. Results: The mean total volume and diameter of the penis increased in the surgery +AM group (p<0.03 and p<0.04, respectively). The stereological evaluation showed a significant increase in the mean volumes of the tunica albuginea and corpora cavernosa in the surgery +AM group compared to the sham group (p<0.01, p< 0.03). Additionally, the mean volume density of the collagen bundles, muscle fibers, and cavernous sinuses and the total number of fibroblasts and smooth muscle cells increased in the surgery +AM group compared to the sham group (p<0.01, p<0.01, p<0.03, p<0.01, and p<0.05, respectively). No infections, bleedings, or other complications were seen. Conclusions: AM is a method that has appeared promising for material use in penile enhancement. Thus, it may be used for PGE in the future. Keywords: Amniotic Membrane; Histopathology; Animal; Penile Girth Enhancement

Footwear bio-modelling: An industrial approach

There is a growing need within the footwear sector to customise the design of the last from which a specific footwear style is to be produced. This customisation is necessary for user comfort and health reasons, as the user needs to wear a suitable shoe. For this purpose, a relationship must be established between the user foot and the last with which the style will be made; up until now, no model has existed that integrates both elements. On the one hand, traditional customised footwear manufacturing techniques are based on purely artisanal procedures which make the process arduous and complex; on the other hand, geometric models proposed by different authors present the impossibility of implementing them in an industrial environment with limited resources for the acquisition of morphometric and structural data for the foot, apart from the fact that they do not prove to be sufficiently accurate given the non-similarity of the foot and last. In this paper, two interrelated geometric models are defined, the first, a bio-deformable foot model and the second, a deformable last model. The experiments completed show the goodness of the model, with it obtaining satisfactory results in terms of comfort, efficiency and precision, which make it viable for use in the sector