Deformable Model Based Shape Analysis Stone Tool Application

This paper introduces a method to measure the average shape of handaxes, and characterize deviations from this average shape by taking into account both internal and external information. In the field of Paleolithic archaeology, standardization and symmetry can be two important concepts. For axially symmetrical shapes such as handaxes, it is possible to introduce a simple appropriate shape representation. We adapt a parameterized deformable model based approach to allow flexibility of shape coverage and analyze the similarity with a few compact parameters. Moreover a hierarchical fitting method ensures stability while measuring global and local shape features step-by-step. Our model incorporates a physics-based framework so as to deform due to forces exerted from boundary data sets

Modeling, simulation and analysis of the heart from four-dimensional cardiac tagged-MR images

Alteration in heart shape and motion is a reasonable indicator of heart diseases. Insights with regard to normal physiology and dysfunction can help in understanding effects and their causes. In the last decade, there has been much progress in developing techniques for studying heart motion with cardiac imaging. Most existing model-based techniques are in the preliminary stages. Statistical model-based techniques deal with the huge variations of shape and motion of the whole heart but these methods have no temporal and multiple subjects\u27 correspondences, so it is difficult to build a model from training sets and analyze different hearts and their motion. Previous parameterized model-based methods could only handle the left ventricle (LV) or up to mid right ventricle (RV) although a model including both the left and right ventricles up to the basal area is needed for comprehensive understanding of cardiac physiology and anatomy. The thesis uses a whole heart model, including LV RV and up to the basal area, for the functional analysis of heart motion. The model, based on a blended parameterized deformable model, is generic enough to deal with different hearts. A generic heart model is coupled with the finite element method to reconstruct heart motion from tagged magnetic resonance (MR) images. Tagged MR is the most promising non-invasive technology to characterize myocardial deformation during the heart cycle because it provides temporal correspondence of material points inside heart walls and enable tracking of these material points over time. The resulting parameters are used for assessing and analyzing global and local cardiac functions. The quantitative analysis derived the complicated but typical patterns of motion and strain on ten test subjects. The significant distinct motion and strains are found from RV hypertrophy patients. Enough sets of experiments can yield parameter values associated with typical normal subjects, identify abnormally functioning area such as infarct regions, and provide diagnostic information by associating altered parameter values with different stages of disease

Geometry-Incorporated Posing of a Full-Body Avatar From Sparse Trackers

Accurately rendering a user’s full body in a virtual environment is crucial for embodied mixed reality (MR) experiences. Conventional MR systems provide sparse trackers such as a headset and two hand-held controllers. Recent studies have intensively investigated learning methods to regress untracked joints from sparse trackers and have produced plausible poses in real time for MR applications. However, most studies have assumed that they either know the position of the root joint or constrain it, yielding stiff pelvis motions. This paper presents the first geometry-incorporated learning method to generate the position and rotation of all joints, including the root joint, from the head and hands information for a wide range of motions. We split the problem into identifying a reference frame and a pose inference with respect to the new reference frame. Our method defines an avatar frame by setting a non-joint as the origin and transforms joint data in a world coordinate system into the avatar coordinate system. Our learning builds on a propagating long short-term memory (LSTM) network exploiting prior knowledge of the kinematic chains and the previous time domain. The learned joints are transformed back to obtain the positions with respect to the world frame. In our experiments, our method achieves competitive accuracy and robustness with the state-of-the-art speed of approximately 130 fps on motion capture datasets and the wild tracking data obtained from commercial MR devices. Our experiments confirm that the proposed method is practically applicable to MR systems

Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar

With the popularity of virtual reality (VR) games and devices, demand is increasing for estimating and displaying user motion in VR applications. Most pose estimation methods for VR avatars exploit inverse kinematics (IK) and online motion capture methods. In contrast to existing approaches, we aim for a stable process with less computation, usable in a small space. Therefore, our strategy has minimum latency for VR device users, from high-performance to low-performance, in multi-user applications over the network. In this study, we estimate the upper body pose of a VR user in real time using a deep learning method. We propose a novel method inspired by a classical regression model and trained with 3D motion capture data. Thus, our design uses a convolutional neural network (CNN)-based architecture from the joint information of motion capture data and modifies the network input and output to obtain input from a head and both hands. After feeding the model with properly normalized inputs, a head-mounted display (HMD), and two controllers, we render the user’s corresponding avatar in VR applications. We used our proposed pose estimation method to build single-user and multi-user applications, measure their performance, conduct a user study, and compare the results with previous methods for VR avatars

Facial Feature Model for a Portrait Video Stylization

With the advent of the deep learning method, portrait video stylization has become more popular. In this paper, we present a robust method for automatically stylizing portrait videos that contain small human faces. By extending the Mask Regions with Convolutional Neural Network features (R-CNN) with a CNN branch which detects the contour landmarks of the face, we divided the input frame into three regions: the region of facial features, the region of the inner face surrounded by 36 face contour landmarks, and the region of the outer face. Besides keeping the facial features region as it is, we used two different stroke models to render the other two regions. During the non-photorealistic rendering (NPR) of the animation video, we combined the deformable strokes and optical flow estimation between adjacent frames to follow the underlying motion coherently. The experimental results demonstrated that our method could not only effectively reserve the small and distinct facial features, but also follow the underlying motion coherently

Doxorubicin Release Controlled by Induced Phase Separation and Use of a Co-Solvent

Electrospun-based drug delivery is emerging as a versatile means of localized therapy; however, controlling the release rates of active agents still remains as a key question. We propose a facile strategy to control the drug release behavior from electrospun fibers by a simple modification of polymer matrices. Polylactic acid (PLA) was used as a major component of the drug-carrier, and doxorubicin hydrochloride (Dox) was used as a model drug. The influences of a polar co-solvent, dimethyl sulfoxide (DMSO), and a hydrophilic polymer additive, polyvinylpyrrolidone (PVP), on the drug miscibility, loading efficiency and release behavior were investigated. The use of DMSO enabled the homogeneous internalization of the drug as well as higher drug loading efficiency within the electrospun fibers. The PVP additive induced phase separation in the PLA matrix and acted as a porogen. Preferable partitioning of Dox into the PVP domain resulted in increased drug loading efficiency in the PLA/PVP fiber. Fast dissolution of PVP domains created pores in the fibers, facilitating the release of internalized Dox. The novelty of this study lies in the detailed experimental investigation of the effect of additives in pre-spinning formulations, such as co-solvents and polymeric porogens, on the drug release behavior of nanofibers