Surface Reconstruction: Roles in the Field of Computer Vision and Computer Graphics

2021 ◽  
pp. 2250008
Author(s):  
Soumi Dhar ◽  
Shyamosree Pal
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Computer Graphics ◽  
Surface Reconstruction ◽  
Related Problem ◽  
Data Modeling ◽  
Mathematical Foundation ◽  
Volume Data ◽  
3D Computer Vision ◽  
Real Objects

Surface Reconstruction is the most potent aspect of 3D computer vision. It allows recapturing or imitating of the shape of real objects. It also provides sufficient knowledge regarding the mathematical foundation for rendering applications which are widely used for analyzing medical volume data, modeling, 3D interior designing, architectural designing. In our paper, we have mentioned various algorithms and approaches for surface reconstruction and their applications. Moreover, we have tried to emphasize the necessity of surface reconstruction for solving image analysis related problem.

Automatic Splicing Technology in Image Splicing Processing

2014 ◽  
Vol 1014 ◽  
pp. 367-370
Author(s):  
Xiao Bo Yu ◽  
Yun Feng Zhang ◽  
Yue Gang Fu
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Computer Graphics ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Research Field ◽  
Important Research ◽  
Image Splicing ◽  
Manual Intervention

Automatic splicing technology is all important research field of image processing, and has become a research focusing on the computer vision and computer graphics,and has important practical value in the fields of image splicing processing, medical image analysis and so on.On the basis of a linear transition method, this paper presents an algorithm which realizes to diminish the seams in overlap region according to the content of scenes.This algorithm avoids manual intervention during the mosaic process.With the help of automatic splicing technology based on the overlapping areas linear transition, the requirement of seamless image splicing can be met. 1.Introduction

scholarly journals A SIMPLE ALGORITHM FOR HOMEOMORPHIC SURFACE RECONSTRUCTION

2002 ◽  
Vol 12 (01n02) ◽  
pp. 125-141 ◽  
Author(s):  
NINA AMENTA ◽  
SUNGHEE CHOI ◽  
TAMAL K. DEY ◽  
NAVEEN LEEKHA
Keyword(s):  
Computer Vision ◽  
Computer Graphics ◽  
Surface Reconstruction ◽  
Linear Approximation ◽  
Piecewise Linear ◽  
Simple Algorithm ◽  
Piecewise Linear Approximation ◽  
Original Surface ◽  
Output Surface ◽  
Sample Points

The problem of computing a piecewise linear approximation to a surface from a set of sample points is important in solid modeling, computer graphics and computer vision. A recent algorithm1 using the Voronoi diagram of the sample points gave a guarantee on the distance of the output surface from the original sampled surface assuming that the sample was sufficiently dense. We give a similar algorithm, simplifying the computation and the proof of the geometric guarantee. In addition, we guarantee that our output surface is homeomorphic to the original surface; to our knowledge this is the first such topological guarantee for this problem.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

scholarly journals Measurement of Gem Colour Using a Computer Vision System: A Case Study with Jadeite-Jade

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 791
Author(s):  
Sufei Zhang ◽  
Ying Guo
Keyword(s):  
Computer Vision ◽  
Regression Model ◽  
Vision System ◽  
Expert Group ◽  
Group Test ◽  
System A ◽  
Real Objects ◽  
Practical Tool ◽  
Almost All

This paper introduces computer vision systems (CVSs), which provides a new method to measure gem colour, and compares CVS and colourimeter (CM) measurements of jadeite-jade colour in the CIELAB space. The feasibility of using CVS for jadeite-jade colour measurement was verified by an expert group test and a reasonable regression model in an experiment involving 111 samples covering almost all jadeite-jade colours. In the expert group test, more than 93.33% of CVS images are considered to have high similarities with real objects. Comparing L*, a*, b*, C*, h, and ∆E* (greater than 10) from CVS and CM tests indicate that significant visual differences exist between the measured colours. For a*, b*, and h, the R2 of the regression model for CVS and CM was 90.2% or more. CVS readings can be used to predict the colour value measured by CM, which means that CVS technology can become a practical tool to detect the colour of jadeite-jade.

scholarly journals Proposing 3D Thermal Technology for Heritage Building Energy Monitoring

2021 ◽  
Vol 13 (8) ◽  
pp. 1537
Author(s):  
Antonio Adán ◽  
Víctor Pérez ◽  
José-Luis Vivancos ◽  
Carolina Aparicio-Fernández ◽  
Samuel A. Prieto
Keyword(s):  
Computer Vision ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Building Energy ◽  
Energy Monitoring ◽  
3D Computer Vision ◽  
Building Monitoring ◽  
Heritage Buildings ◽  
Heritage Building ◽  
Definition Of

The energy monitoring of heritage buildings has, to date, been governed by methodologies and standards that have been defined in terms of sensors that record scalar magnitudes and that are placed in specific positions in the scene, thus recording only some of the values sampled in that space. In this paper, however, we present an alternative to the aforementioned technologies in the form of new sensors based on 3D computer vision that are able to record dense thermal information in a three-dimensional space. These thermal computer vision-based technologies (3D-TCV) entail a revision and updating of the current building energy monitoring methodologies. This paper provides a detailed definition of the most significant aspects of this new extended methodology and presents a case study showing the potential of 3D-TCV techniques and how they may complement current techniques. The results obtained lead us to believe that 3D computer vision can provide the field of building monitoring with a decisive boost, particularly in the case of heritage buildings.

scholarly journals GEOBIA 2016: Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning

2019 ◽  
Vol 11 (10) ◽  
pp. 1181 ◽  
Author(s):  
Norman Kerle ◽  
Markus Gerke ◽  
Sébastien Lefèvre
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Analysis ◽  
The Netherlands ◽  
Object Based Image Analysis ◽  
Biennial Conference ◽  
Object Based ◽  
The University

The 6th biennial conference on object-based image analysis—GEOBIA 2016—took place in September 2016 at the University of Twente in Enschede, The Netherlands (see www [...]

Sign in / Sign up

Export Citation Format

Share Document