scholarly journals Reconstruction of a 3D Human Foot Shape Model Based on a Video Stream Using Photogrammetry and Deep Neural Networks

2021 ◽  
Vol 13 (12) ◽  
pp. 315
Author(s):  
Lev Shilov ◽  
Semen Shanshin ◽  
Aleksandr Romanov ◽  
Anastasia Fedotova ◽  
Anna Kurtukova ◽  
...  
Keyword(s):  
Medical Research ◽  
3D Model ◽  
Business Models ◽  
Point Clouds ◽  
Video Stream ◽  
Shape Model ◽  
Human Foot ◽  
Modern Computer ◽  
Dense Point ◽  
Orthopedic Shoes

Reconstructed 3D foot models can be used for 3D printing and further manufacturing of individual orthopedic shoes, as well as in medical research and for online shoe shopping. This study presents a technique based on the approach and algorithms of photogrammetry. The presented technique was used to reconstruct a 3D model of the foot shape, including the lower arch, using smartphone images. The technique is based on modern computer vision and artificial intelligence algorithms designed for image processing, obtaining sparse and dense point clouds, depth maps, and a final 3D model. For the segmentation of foot images, the Mask R-CNN neural network was used, which was trained on foot data from a set of 40 people. The obtained accuracy was 97.88%. The result of the study was a high-quality reconstructed 3D model. The standard deviation of linear indicators in length and width was 0.95 mm, with an average creation time of 1 min 35 s recorded. Integration of this technique into the business models of orthopedic enterprises, Internet stores, and medical organizations will allow basic manufacturing and shoe-fitting services to be carried out and will help medical research to be performed via the Internet.

scholarly journals Visualizations of the Doblar Accumulation Basin Based on the UAV Survey

2019 ◽  
Vol 1 ◽  
pp. 1-7
Author(s):  
Klemen Kozmus Trajkovski ◽  
Gašper Štebe ◽  
Dušan Petrovič
Keyword(s):  
3D Model ◽  
Weather Conditions ◽  
Point Clouds ◽  
Water Levels ◽  
Contour Lines ◽  
Digital Elevation ◽  
Short Period ◽  
Dense Point ◽  
Aerial Vehicle ◽  
Elevation Model

<p><strong>Abstract.</strong> Our research is based on a large case study of Unmanned Aerial Vehicle (UAV) surveys, modelling and visualizations of the Doblar accumulation basin. The various approaches for UAV surveying of large, demanding terrain configurations, and the benefits of surveying products used as a basis for other interdisciplinary hydrological and environmental services were researched. The demanding mountainous terrain, the steep slopes and deep and narrow streams required detailed pre-planning of the survey, including the pre-survey terrain overview. The accumulation basin was emptied merely for a short period; thus, the survey was performed in unfavourable weather conditions, which included coldness, snowfall and wind. Point clouds were generated and georeferenced from the 4377 recorded photos. The dense point cloud contained approximately 222 million points in the medium setting and more than a billion in the high setting. A 3D model was built from the data. This became the basis for numerous further analyses and for the presentation using cartographic principles: a digital elevation model with a resolution of 10&amp;thinsp;cm, an orthophoto with a resolution of 10&amp;thinsp;cm, a 3D model draped with orthophoto, contour lines with a 1&amp;thinsp;m interval, topographic profiles, calculations of volumes at different water levels, a flythrough, augmented reality and a video simulation of the water level changes. The model can also serve as a basis for hydraulic and environmental analysis and simulations or used for analyses of the accumulation and deposition of river material compared with previous and future surveys.</p>

scholarly journals Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5220
Author(s):  
Shima Sahebdivani ◽  
Hossein Arefi ◽  
Mehdi Maboudi
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
3D Model ◽  
Three Dimensional ◽  
Point Clouds ◽  
Rail Track ◽  
Three Dimensional Modeling ◽  
Railway Industry ◽  
Dense Point ◽  
Uav Images

The expansion of the railway industry has increased the demand for the three-dimensional modeling of railway tracks. Due to the increasing development of UAV technology and its application advantages, in this research, the detection and 3D modeling of rail tracks are investigated using dense point clouds obtained from UAV images. Accordingly, a projection-based approach based on the overall direction of the rail track is proposed in order to generate a 3D model of the railway. In order to extract the railway lines, the height jump of points is evaluated in the neighborhood to select the candidate points of rail tracks. Then, using the RANSAC algorithm, line fitting on these candidate points is performed, and the final points related to the rail are identified. In the next step, the pre-specified rail piece model is fitted to the rail points through a projection-based process, and the orientation parameters of the model are determined. These parameters are later improved by fitting the Fourier curve, and finally a continuous 3D model for all of the rail tracks is created. The geometric distance of the final model from rail points is calculated in order to evaluate the modeling accuracy. Moreover, the performance of the proposed method is compared with another approach. A median distance of about 3 cm between the produced model and corresponding point cloud proves the high quality of the proposed 3D modeling algorithm in this study.

scholarly journals Digital Construction of Traditional Baruk in Sarawak, Malaysia

2020 ◽  
Vol 5 (14) ◽  
pp. 203-209
Author(s):  
Nik Umar Solihin Nik Kamaruzaman ◽  
Afiqah Ahmad ◽  
Norlina Mohamed Noor
Keyword(s):  
3D Model ◽  
Safety Concern ◽  
Publishing House ◽  
Three Dimensional ◽  
Point Clouds ◽  
Traditional Architecture ◽  
Close Range Photogrammetry ◽  
Dense Point ◽  
Close Range ◽  
And Function

The traditional Baruk in Sarawak has gone through some architectural changes in terms of its material and function due to the urban modernization and safety concern. Therefore, the research aims to construct the Three-Dimensional (3D) model of the building using digital close-range photogrammetry. The exploratory study can be categorized into four phases consist of Site Selection; Data Acquisition; Data Processing; and 3D Modelling. The 3D model generated from the photogrammetry software presents the result of the dense point clouds. The study could give fundamental guidelines on using a mobile device in digital close-range photogrammetry techniques. Keywords: Digital construction; traditional architecture, digital close-range photogrammetry, heritage documentation. eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5i14.2243

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

scholarly journals Towards footwear manufacturing 4.0: shoe sole robotic grasping in assembling operations

2021 ◽  
Author(s):  
Guillermo Oliver ◽  
Pablo Gil ◽  
Jose F. Gomez ◽  
Fernando Torres
Keyword(s):  
Industry 4.0 ◽  
Laser Scanner ◽  
Point Clouds ◽  
Average Score ◽  
Low Density ◽  
Contour Extraction ◽  
Shoe Industry ◽  
Simulated Environment ◽  
Dense Point ◽  
Different Shapes

AbstractIn this paper, we present a robotic workcell for task automation in footwear manufacturing such as sole digitization, glue dispensing, and sole manipulation from different places within the factory plant. We aim to make progress towards shoe industry 4.0. To achieve it, we have implemented a novel sole grasping method, compatible with soles of different shapes, sizes, and materials, by exploiting the particular characteristics of these objects. Our proposal is able to work well with low density point clouds from a single RGBD camera and also with dense point clouds obtained from a laser scanner digitizer. The method computes antipodal grasping points from visual data in both cases and it does not require a previous recognition of sole. It relies on sole contour extraction using concave hulls and measuring the curvature on contour areas. Our method was tested both in a simulated environment and in real conditions of manufacturing at INESCOP facilities, processing 20 soles with different sizes and characteristics. Grasps were performed in two different configurations, obtaining an average score of 97.5% of successful real grasps for soles without heel made with materials of low or medium flexibility. In both cases, the grasping method was tested without carrying out tactile control throughout the task.

scholarly journals Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

2019 ◽  
Vol 93 (3) ◽  
pp. 411-429 ◽  
Author(s):  
Maria Immacolata Marzulli ◽  
Pasi Raumonen ◽  
Roberto Greco ◽  
Manuela Persia ◽  
Patrizia Tartarino
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Volume ◽  
Forest Plot ◽  
3D Point Clouds ◽  
Dense Point ◽  
Minimum Number ◽  
Scale Point

Abstract Methods for the three-dimensional (3D) reconstruction of forest trees have been suggested for data from active and passive sensors. Laser scanner technologies have become popular in the last few years, despite their high costs. Since the improvements in photogrammetric algorithms (e.g. structure from motion—SfM), photographs have become a new low-cost source of 3D point clouds. In this study, we use images captured by a smartphone camera to calculate dense point clouds of a forest plot using SfM. Eighteen point clouds were produced by changing the densification parameters (Image scale, Point density, Minimum number of matches) in order to investigate their influence on the quality of the point clouds produced. In order to estimate diameter at breast height (d.b.h.) and stem volumes, we developed an automatic method that extracts the stems from the point cloud and then models them with cylinders. The results show that Image scale is the most influential parameter in terms of identifying and extracting trees from the point clouds. The best performance with cylinder modelling from point clouds compared to field data had an RMSE of 1.9 cm and 0.094 m3, for d.b.h. and volume, respectively. Thus, for forest management and planning purposes, it is possible to use our photogrammetric and modelling methods to measure d.b.h., stem volume and possibly other forest inventory metrics, rapidly and without felling trees. The proposed methodology significantly reduces working time in the field, using ‘non-professional’ instruments and automating estimates of dendrometric parameters.

scholarly journals QUALITY PREDICTION OF DENSE POINTS GENERATED BY STRUCTURE FROM MOTION FOR HIGH-QUALITY AND EFFICIENT AS-IS MODEL RECONSTRUCTION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 95-101
Author(s):  
R. Moritani ◽  
S. Kanai ◽  
H. Date ◽  
Y. Niina ◽  
R. Honma
Keyword(s):  
Structure From Motion ◽  
Point Clouds ◽  
Quality Prediction ◽  
Construction Site ◽  
Model Reconstruction ◽  
High Quality ◽  
Bridge Column ◽  
Dense Point ◽  
Selection Of

<p><strong>Abstract.</strong> In this paper, we introduce a method for predicting the quality of dense points and selecting low-quality regions on the points generated by the structure from motion (SfM) and multi-view stereo (MVS) pipeline to realize high-quality and efficient as-is model reconstruction, using only results from the former: sparse point clouds and camera poses. The method was shown to estimate the quality of the final dense points as the quality predictor on an approximated model obtained from SfM only, without requiring the time-consuming MVS process. Moreover, the predictors can be used for selection of low-quality regions on the approximated model to estimate the next-best optimum camera poses which could improve quality. Furthermore, the method was applied to the prediction of dense point quality generated from the image sets of a concrete bridge column and construction site, and the prediction was validated in a time much shorter than using MVS. Finally, we discussed the correlation between the predictors and the final dense point quality.</p>

Bioentrepreneurship in Medical Biotechnology

2021 ◽  
pp. 201-228
Author(s):  
Neeti Sharma
Keyword(s):  
Medical Research ◽  
Business Models ◽  
Medical Science ◽  
Living Cells ◽  
Multiple Features ◽  
Research Product ◽  
Disruptive Technologies ◽  
Sustainable Environment ◽  
Treatment And Prevention ◽  
The World

Being a developing sector, biotechnology combines both medical science and various facets of a sustainable environment. Scientific innovations from academics and industry contribute towards the rise of bioentrepreneurship across the world. Medical biotechnology helps the treatment and prevention of human diseases by using living cells and cell materials to research and produce pharmaceutical and diagnostic products. It requires incubation of research, product, and its yield for the improvement of humans to transform medical research into an entrepreneurial industry. Biotechnology provides an opportunity for entrepreneurs to harness the power of genomics for the development and marketing of new therapeutics. These opportunities allow entrepreneurs to visualize the creation of both disruptive business and disruptive technologies for today's business models. This chapter will summarize multiple features of entrepreneurship in the biotechnology industry in combination with other industries and will explain the significance of bio-entrepreneurship in the field of medical biotechnology.

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