Construction of biological surface models from cross-sections image processing

1993 ◽  
Vol 40 (4) ◽  
pp. 329-334 ◽  
Author(s):  
O.S. Odesanya ◽  
W.N. Waggenspack ◽  
D.E. Thompson
Keyword(s):  
Image Processing ◽  
Cross Sections ◽  
Biological Surface ◽  
Surface Models

Image detection and parameterization for different components in cross-sections of radial tires

2021 ◽  
pp. 095440702110236
Author(s):  
Chen Liu ◽  
Yude Dong ◽  
Yanli Wei ◽  
Jiangtao Wang ◽  
Hongling Li
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Cross Sections ◽  
Digital Image ◽  
Detection Method ◽  
Detection System ◽  
Practical Significance ◽  
Vehicle Safety ◽  
Wire Steel ◽  
Good Detection

The internal structure analysis of radial tires is of great significance to improve vehicle safety and during tire research. In order to perform the digital analysis and detection of the internal composition in radial tire cross-sections, a detection method based on digital image processing was proposed. The research was carried out as follows: (a) the distribution detection and parametric analysis of the bead wire, steel belt, and carcass in the tire section were performed by means of digital image processing, connected domain extraction, and Hough transform; (b) using the angle of location distribution and area relationship, the detection data were optimized through coordinate and quantity relationship constraints; (c) a detection system for tire cross-section components was designed using the MATLAB platform. Our experimental results showed that this method displayed a good detection performance, and important practical significance for the research and manufacture of tires.

Image processing algorithms in the assessment of grain damage degree

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Wojciech Maliga ◽  
Włodzimierz Dudziński ◽  
Magdalena Łabowska ◽  
Jerzy Detyna ◽  
Marcin Łopusiewicz ◽  
...  
Keyword(s):  
Image Processing ◽  
Cross Sections ◽  
Three Dimensional ◽  
Sample Volume ◽  
Three Dimensional Model ◽  
Time Intervals ◽  
Damage Degree ◽  
Grain Damage ◽  
Analyse Sample

Abstract Objectives The paper presents preliminary results on the assessment of algorithms used in image processing of the grain damage degree. The purpose of the work is developing a tool allowing to analyse sample cross-sections of rye germs. Methods The analysis of the grain cross-sections was carried out on the basis of a series their photos taken at equal time intervals at a set depth. The cross-sections will be used to create additional virtual cross-sections allowing to analyse the whole sample volume. The ultimate plan is to generate two cross-sections perpendicular to each other. Based on volumetric data read from the sample section, a three-dimensional model of an object will be generated. Results The analysis of model surface will allowed us to detect possible grain damage. The developed method of preparing the research material and the proprietary application allowed for the identification of internal defects in the biological material (cereal grains). Conclusions The presented methodology may be used in the agri-food industry in the future. However, much research remains to be done. These works should primarily aim at significantly reducing the time-consuming nature of individual stages, as well as improving the quality of the reconstructed image.

Analysis of Insole Geometry and Deformity by Using a Three-Dimensional Image Processing Technique: A Preliminary Study

2019 ◽  
Vol 109 (2) ◽  
pp. 98-107
Author(s):  
Kit-lun Yick ◽  
Wai-ting Lo ◽  
Sun-pui Ng ◽  
Joanne Yip ◽  
Hung-hei Kwan ◽  
...  
Keyword(s):  
Image Processing ◽  
Cross Sections ◽  
Plantar Pressure ◽  
Three Dimensional ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Diabetic Patients ◽  
Clinical Practices ◽  
Cross Sectional ◽  
Orthotic Treatment

Background: Accurate representation of the insole geometry is crucial for the development and performance evaluation of foot orthoses designed to redistribute plantar pressure, especially for diabetic patients. Methods: Considering the limitations in the type of equipment and space available in clinical practices, this study adopted a simple portable three-dimensional (3-D) desktop scanner to evaluate the 3-D geometry of an orthotic insole and the corresponding deformities after the insole has been worn. The shape of the insole structure along horizontal cross sections is defined with 3-D scanning and image processing. Accompanied by an in-shoe pressure measurement system, plantar pressure distribution in four foot regions (hallux, metatarsal heads, midfoot, and heel) is analyzed and evaluated for insole deformity. Results: Insole deformities are quantified across the four foot regions. The hallux region tends to show the greatest changes in shape geometry (17%–50%) compared with the other foot regions after 2 months of insole wear. As a result of insole deformities, plantar peak pressures change considerably (–4.3% to +69.5%) during the course of treatment. Conclusions: Changes in shape geometry of the insoles could be objectively quantified with 3-D scanning techniques and image processing. This investigation finds that, in general, the design of orthotic insoles may not be adequate for diabetic individuals with similar foot problems. The drastic changes in the insole shape geometry and cross-sectional areas during orthotic treatment may reduce insole fit and conformity. An inadequate insole design may also affect plantar pressure reduction. The approach proposed herein, therefore, allows for objective quantification of insole shape geometry, which results in effective and optimal orthotic treatment.

scholarly journals Determining the Optimal Number of Ground Control Points for Varying Study Sites through Accuracy Evaluation of Unmanned Aerial System-Based 3D Point Clouds and Digital Surface Models

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 49 ◽  
Author(s):  
Jae Jin Yu ◽  
Dong Woo Kim ◽  
Eun Jung Lee ◽  
Seung Woo Son
Keyword(s):  
Image Processing ◽  
Point Clouds ◽  
Optimal Number ◽  
Ground Control ◽  
Control Points ◽  
Widespread Application ◽  
Ground Control Points ◽  
3D Point Clouds ◽  
Surface Models ◽  
Study Sites

The rapid development of drone technologies, such as unmanned aerial systems (UASs) and unmanned aerial vehicles (UAVs), has led to the widespread application of three-dimensional (3D) point clouds and digital surface models (DSMs). Due to the number of UAS technology applications across many fields, studies on the verification of the accuracy of image processing results have increased. In previous studies, the optimal number of ground control points (GCPs) was determined for a specific area of a study site by increasing or decreasing the amount of GCPs. However, these studies were mainly conducted in a single study site, and the results were not compared with those from various study sites. In this study, to determine the optimal number of GCPs for modeling multiple areas, the accuracy of 3D point clouds and DSMs were analyzed in three study sites with different areas according to the number of GCPs. The results showed that the optimal number of GCPs was 12 for small and medium sites (7 and 39 ha) and 18 for the large sites (342 ha) based on the overall accuracy. If these results are used for UAV image processing in the future, accurate modeling will be possible with minimal effort in GCPs.

scholarly journals Automatic Construction of Digital Woven Fabric by Using Sequential Yarn Images

2019 ◽  
Vol 19 (2) ◽  
pp. 147-156
Author(s):  
Zhongjian Li ◽  
Fei Yu ◽  
Ning Zhang ◽  
Yichen Lu ◽  
Ruru Pan ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Image Processing ◽  
Cross Sections ◽  
Woven Fabric ◽  
Intensity Curve ◽  
Automatic Construction ◽  
Weave Structure ◽  
Fabric Structure ◽  
Curve Model ◽  
The Mathematical Model

Abstract In this article, a computerized method is proposed for simulating digital woven fabric (DWF) based on sequential yarn images captured from a moving yarn. A mathematical model of woven fabric structure is established by assuming that the crimped shape of yarns in weave structure is elastica, and the cross-sections of yarn in sequence image and fabric are circular and ellipse, respectively. The sequential yarn images, which are preprocessed and stitched first by image processing methods, are resized based on the mathematical model. Then a light intensity curve, which consists of radial curve model and axial curve model, is used to simulate the gray texture distribution of interlacing points in radial and axial directions. Finally, a Boole Matrix model is used to control the woven pattern. In the experiment, a slub yarn and a normal yarn samples with same count are applied to simulate gray texture fabrics. Then the gray fabrics are transformed to color fabrics based on three color maps. The fabric simulations are confined to single fabrics of plain, 2/2 matt, and 1/3 twill weaves.

scholarly journals COMPARISON BETWEEN TWO GENERIC 3D BUILDING RECONSTRUCTION APPROACHES – POINT CLOUD BASED VS. IMAGE PROCESSING BASED

2016 ◽  
Vol XLI-B3 ◽  
pp. 599-604 ◽  
Author(s):  
D. Dahlke ◽  
M. Linkiewicz
Keyword(s):  
Image Processing ◽  
Point Cloud ◽  
The Other ◽  
3D Point Cloud ◽  
Real World Data ◽  
Surface Models ◽  
Order Of Magnitude ◽  
The Difference ◽  
The One ◽  
Supply Information

This paper compares two generic approaches for the reconstruction of buildings. Synthesized and real oblique and vertical aerial imagery is transformed on the one hand into a dense photogrammetric 3D point cloud and on the other hand into photogrammetric 2.5D surface models depicting a scene from different cardinal directions. One approach evaluates the 3D point cloud statistically in order to extract the hull of structures, while the other approach makes use of salient line segments in 2.5D surface models, so that the hull of 3D structures can be recovered. With orders of magnitudes more analyzed 3D points, the point cloud based approach is an order of magnitude more accurate for the synthetic dataset compared to the lower dimensioned, but therefor orders of magnitude faster, image processing based approach. For real world data the difference in accuracy between both approaches is not significant anymore. In both cases the reconstructed polyhedra supply information about their inherent semantic and can be used for subsequent and more differentiated semantic annotations through exploitation of texture information.

scholarly journals COMPARISON BETWEEN TWO GENERIC 3D BUILDING RECONSTRUCTION APPROACHES – POINT CLOUD BASED VS. IMAGE PROCESSING BASED

2016 ◽  
Vol XLI-B3 ◽  
pp. 599-604 ◽  
Author(s):  
D. Dahlke ◽  
M. Linkiewicz
Keyword(s):  
Image Processing ◽  
Point Cloud ◽  
The Other ◽  
3D Point Cloud ◽  
Real World Data ◽  
Surface Models ◽  
Order Of Magnitude ◽  
The Difference ◽  
The One ◽  
Supply Information

This paper compares two generic approaches for the reconstruction of buildings. Synthesized and real oblique and vertical aerial imagery is transformed on the one hand into a dense photogrammetric 3D point cloud and on the other hand into photogrammetric 2.5D surface models depicting a scene from different cardinal directions. One approach evaluates the 3D point cloud statistically in order to extract the hull of structures, while the other approach makes use of salient line segments in 2.5D surface models, so that the hull of 3D structures can be recovered. With orders of magnitudes more analyzed 3D points, the point cloud based approach is an order of magnitude more accurate for the synthetic dataset compared to the lower dimensioned, but therefor orders of magnitude faster, image processing based approach. For real world data the difference in accuracy between both approaches is not significant anymore. In both cases the reconstructed polyhedra supply information about their inherent semantic and can be used for subsequent and more differentiated semantic annotations through exploitation of texture information.

scholarly journals Digital image processing for aggregate orientation in asphalt concrete mixtures

1996 ◽  
Vol 23 (2) ◽  
pp. 480-489 ◽  
Author(s):  
Zhong Qi Yue ◽  
Isabelle Morin
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Cross Sections ◽  
Digital Image ◽  
Asphalt Concrete ◽  
Fine Particles ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Concrete Mixtures ◽  
Aggregate Particles

Asphalt concrete is a composite material which consists of asphalt cement, voids, fine particles, sand, and coarse aggregates. Previous investigations of asphalt concrete mixtures have mainly concentrated on the macroscopic properties of the composite materials based on the assumption that the mixtures are homogeneous and isotropic. This paper applies an innovative digital image processing technique to quantify the orientations of coarse aggregate particles (≥ 2 mm) in asphalt concrete mixtures such as hot mix asphalt, stone matrix asphalt, and large stone asphalt compacted in the laboratory or field. The results indicate quantitatively that asphalt concrete specimens compacted in the field or in the laboratory with gyratory compactor have an oriented structure of aggregate particle distribution where the major cross sections of aggregate particles have the tendency to lie horizontally, and that aggregate particles are more randomly oriented in the asphalt concrete specimens compacted in the laboratory with the Marshall compactor. Key words: asphalt concrete mixtures, digital image processing, microstructure, aggregate orientation, anisotropy, compaction methods.

scholarly journals Automatic urban 3D building reconstruction from multi-ray photogrammetry

2014 ◽  
Vol XL-3 ◽  
pp. 219-226 ◽  
Author(s):  
A. P. McClune ◽  
P. E. Miller ◽  
J. P. Mills ◽  
D. Holland
Keyword(s):  
Cross Sections ◽  
Three Dimensional ◽  
Multiple Sources ◽  
Building Models ◽  
Surface Models ◽  
Canny Edge ◽  
Complementary Nature ◽  
Airborne Sensors ◽  
Building Reconstruction

Over the last 20 years the use of, and demand for, three dimensional (3D) building models has meant there has been a vast amount of research conducted in automating the extraction and reconstruction of these models from airborne sensors. Whilst many different approaches have been suggested, full automation is yet to be achieved and research has suggested that the combination of data from multiple sources is required in order to achieve this. Developments in digital photogrammetry have delivered improvements in spatial resolution whilst higher image overlap to increase the number of pixel correspondents between images, giving the name multi-ray photogrammetry, has improved the resolution and quality of its by-products. In this paper the extraction of roof geometry from multiray photogrammetry will be covered, which underpins 3D building reconstruction. Using orthophotos, roof vertices are extracted using the Canny edge detector. Roof planes are detected from digital surface models (DSM) by extracting information from 2D cross sections and measuring height differences. To eliminate overhanging vegetation, the segmentation of trees is investigated by calculating the characteristics of a point within a local neighbourhood of the photogrammetric point cloud. The results highlight the complementary nature of these information sources, and a methodology for integration and reconstruction of roof geometry is proposed.

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