Homography-Based Vehicle Pose Estimation from a Single Image by Using Machine-Learning for Wheel-Region and Tire-Road Contact Point Detection

2020 ◽  
pp. 169-179
Author(s):  
Nastaran Radmehr ◽  
Mehran Mehrandezh ◽  
Christine Chan
Keyword(s):  
Machine Learning ◽  
Pose Estimation ◽  
Contact Point ◽  
Single Image ◽  
Point Detection

Performance Analysis of Virtual Human Bodies with Clothing and Hair from Images to Animation

2018 ◽  
Vol 8 (6) ◽  
pp. 69
Author(s):  
Kulalvaimozhi. V. P. ◽  
Germanus Alex. M ◽  
John Peter. S
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Camera Calibration ◽  
Pose Estimation ◽  
Body Shape ◽  
Data Driven ◽  
Virtual Human ◽  
Single Image ◽  
Shape Estimation ◽  
Challenging Situation

Virtual human bodies, clothing, and hair are widely used in a number of scenarios such as 3D animated movies, gaming, and online fashion. Machine learning can be used to construct data-driven 3D human bodies, clothing, and hair. In this thesis, we provide a solution to 3D shape and pose estimation under the most challenging situation where only a single image is available and the image is captured in a natural environment with unknown camera calibration. We also demonstrate that a simplified 2D clothing model helps to increase the accuracy of 2D body shape estimation significantly.

scholarly journals Machine Learning Algorithms, Applied to Intact Islets of Langerhans, Demonstrate Significantly Enhanced Insulin Staining at the Capillary Interface of Human Pancreatic β Cells

Metabolites ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 363
Author(s):  
Louise Cottle ◽  
Ian Gilroy ◽  
Kylie Deng ◽  
Thomas Loudovaris ◽  
Helen E. Thomas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Islets Of Langerhans ◽  
Learning Algorithm ◽  
Contact Point ◽  
Machine Learning Algorithms ◽  
Automated Image Analysis ◽  
Β Cells ◽  
Pancreatic Β Cells ◽  
Blood Glucose Concentrations ◽  
Insulin Staining

Pancreatic β cells secrete the hormone insulin into the bloodstream and are critical in the control of blood glucose concentrations. β cells are clustered in the micro-organs of the islets of Langerhans, which have a rich capillary network. Recent work has highlighted the intimate spatial connections between β cells and these capillaries, which lead to the targeting of insulin secretion to the region where the β cells contact the capillary basement membrane. In addition, β cells orientate with respect to the capillary contact point and many proteins are differentially distributed at the capillary interface compared with the rest of the cell. Here, we set out to develop an automated image analysis approach to identify individual β cells within intact islets and to determine if the distribution of insulin across the cells was polarised. Our results show that a U-Net machine learning algorithm correctly identified β cells and their orientation with respect to the capillaries. Using this information, we then quantified insulin distribution across the β cells to show enrichment at the capillary interface. We conclude that machine learning is a useful analytical tool to interrogate large image datasets and analyse sub-cellular organisation.

scholarly journals Lightweight Stacked Hourglass Network for Human Pose Estimation

2020 ◽  
Vol 10 (18) ◽  
pp. 6497
Author(s):  
Seung-Taek Kim ◽  
Hyo Jong Lee
Keyword(s):  
Receptive Field ◽  
Pose Estimation ◽  
Network Architecture ◽  
Human Pose Estimation ◽  
Current State ◽  
Reduction Methods ◽  
Substantial Progress ◽  
Human Pose ◽  
Residual Block ◽  
Point Detection

Human pose estimation is a problem that continues to be one of the greatest challenges in the field of computer vision. While the stacked structure of an hourglass network has enabled substantial progress in human pose estimation and key-point detection areas, it is largely used as a backbone network. However, it also requires a relatively large number of parameters and high computational capacity due to the characteristics of its stacked structure. Accordingly, the present work proposes a more lightweight version of the hourglass network, which also improves the human pose estimation performance. The new hourglass network architecture utilizes several additional skip connections, which improve performance with minimal modifications while still maintaining the number of parameters in the network. Additionally, the size of the convolutional receptive field has a decisive effect in learning to detect features of the full human body. Therefore, we propose a multidilated light residual block, which expands the convolutional receptive field while also reducing the computational load. The proposed residual block is also invariant in scale when using multiple dilations. The well-known MPII and LSP human pose datasets were used to evaluate the performance using the proposed method. A variety of experiments were conducted that confirm that our method is more efficient compared to current state-of-the-art hourglass weight-reduction methods.

scholarly journals Dimension Reduction for Objects Composed of Vector Sets

2017 ◽  
Vol 27 (1) ◽  
pp. 169-180 ◽  
Author(s):  
Marton Szemenyei ◽  
Ferenc Vajda
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Discriminant Analysis ◽  
Probability Distribution ◽  
Dimension Reduction ◽  
Pose Estimation ◽  
Real World ◽  
Single Object ◽  
Real World Datasets

Abstract Dimension reduction and feature selection are fundamental tools for machine learning and data mining. Most existing methods, however, assume that objects are represented by a single vectorial descriptor. In reality, some description methods assign unordered sets or graphs of vectors to a single object, where each vector is assumed to have the same number of dimensions, but is drawn from a different probability distribution. Moreover, some applications (such as pose estimation) may require the recognition of individual vectors (nodes) of an object. In such cases it is essential that the nodes within a single object remain distinguishable after dimension reduction. In this paper we propose new discriminant analysis methods that are able to satisfy two criteria at the same time: separating between classes and between the nodes of an object instance. We analyze and evaluate our methods on several different synthetic and real-world datasets.

Single image-based head pose estimation with spherical parametrization and 3D morphing

2020 ◽  
Vol 103 ◽  
pp. 107316 ◽  
Author(s):  
Hui Yuan ◽  
Mengyu Li ◽  
Junhui Hou ◽  
Jimin Xiao
Keyword(s):  
Pose Estimation ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Single Image ◽  
Spherical Parametrization
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