scholarly journals Invariants of the Space Point Element Structure and Their Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yanping Mui ◽  
Youzheng Zhang ◽  
Guitao Cao
Keyword(s):  
Object Recognition ◽  
3D Reconstruction ◽  
Traditional Method ◽  
3D Structure ◽  
Geometric Invariant ◽  
Basic Matrix ◽  
Projective Invariants ◽  
Single Frame ◽  
Space Point ◽  
Geometric Relationship

In this paper, a new geometric structure of projective invariants is proposed. Compared with the traditional invariant calculation method based on 3D reconstruction, this method is comparable in the reliability of invariant calculation. According to this method, the only thing needed to find out is the geometric relationship between 3D points and 2D points, and the invariant can be obtained by using a single frame image. In the method based on 3D reconstruction, the basic matrix of two images is estimated first, and then, the 3D projective invariants are calculated according to the basic matrix. Therefore, in terms of algorithm complexity, the method proposed in this paper is superior to the traditional method. In this paper, we also study the projection transformation from a 3D point to a 2D point in space. According to this relationship, the geometric invariant relationships of other point structures can be easily derived, which have important applications in model-based object recognition. At the same time, the experimental results show that the eight-point structure invariants proposed in this paper can effectively describe the essential characteristics of the 3D structure of the target, without the influence of view, scaling, lighting, and other link factors, and have good stability and reliability.

Detection, Averaging, and 3D Reconstruction of Biological Specimens on Hypercubes (Transputer-based) Computers

1990 ◽  
Vol 48 (1) ◽  
pp. 454-455
Author(s):  
Jose-Maria Carazo ◽  
I. Benavides ◽  
S. Marco ◽  
J.L. Carrascosa ◽  
E.L. Zapata
Keyword(s):  
3D Reconstruction ◽  
3D Structure ◽  
Three Dimensional ◽  
Software Tools ◽  
Mapping Method ◽  
Computer Architectures ◽  
Parallel Computer ◽  
Reconstruction Process ◽  
Computing Power ◽  
Order Of Magnitude

Obtaining the three-dimensional (3D) structure of negatively stained biological specimens at a resolution of, typically, 2 - 4 nm is becoming a relatively common practice in an increasing number of laboratories. A combination of new conceptual approaches, new software tools, and faster computers have made this situation possible. However, all these 3D reconstruction processes are quite computer intensive, and the middle term future is full of suggestions entailing an even greater need of computing power. Up to now all published 3D reconstructions in this field have been performed on conventional (sequential) computers, but it is a fact that new parallel computer architectures represent the potential of order-of-magnitude increases in computing power and should, therefore, be considered for their possible application in the most computing intensive tasks.We have studied both shared-memory-based computer architectures, like the BBN Butterfly, and local-memory-based architectures, mainly hypercubes implemented on transputers, where we have used the algorithmic mapping method proposed by Zapata el at. In this work we have developed the basic software tools needed to obtain a 3D reconstruction from non-crystalline specimens (“single particles”) using the so-called Random Conical Tilt Series Method. We start from a pair of images presenting the same field, first tilted (by ≃55°) and then untilted. It is then assumed that we can supply the system with the image of the particle we are looking for (ideally, a 2D average from a previous study) and with a matrix describing the geometrical relationships between the tilted and untilted fields (this step is now accomplished by interactively marking a few pairs of corresponding features in the two fields). From here on the 3D reconstruction process may be run automatically.

Robot Learning from Demonstration Using 3D Computer Vision

2014 ◽  
Vol 875-877 ◽  
pp. 1994-1999
Author(s):  
James Aaron Debono ◽  
Gu Fang
Keyword(s):  
Object Recognition ◽  
Recognition Accuracy ◽  
Robot Learning ◽  
Learning From Demonstration ◽  
State Machines ◽  
Linear Motion ◽  
3D Computer Vision ◽  
Single Frame ◽  
Pick And Place ◽  
Finite State

For robot application to proliferate in industry, and in unregulated environments, a simple means of programming is required. This paper describes methods for robot Learning from Demonstration (LfD). These methods used an RGB-D sensor for demonstration observation, and used finite state machines (FSMs) for policy derivation. Particularly, a method for object recognition was developed, which required only a single frame of data for training, and was able to perform real-time recognition. A planning method for object grasping was also developed. Experiments with a pick-and-place robot show that the developed methods resulted in object recognition accuracy greater than 99% in cluttered scenes, and manipulation accuracies of below 3mm in linear motion and 2° in rotation.

scholarly journals Detecting Signatures of Positive Selection against a Backdrop of Compensatory Processes

2020 ◽  
Vol 37 (11) ◽  
pp. 3353-3362
Author(s):  
Peter B Chi ◽  
Westin M Kosater ◽  
David A Liberles
Keyword(s):  
Amino Acids ◽  
Statistical Model ◽  
Positive Selection ◽  
Traditional Method ◽  
3D Structure ◽  
Evolutionary Time ◽  
Synonymous Substitutions ◽  
Synonymous Mutations ◽  
Parametric Bootstrapping

Abstract There are known limitations in methods of detecting positive selection. Common methods do not enable differentiation between positive selection and compensatory covariation, a major limitation. Further, the traditional method of calculating the ratio of nonsynonymous to synonymous substitutions (dN/dS) does not take into account the 3D structure of biomacromolecules nor differences between amino acids. It also does not account for saturation of synonymous mutations (dS) over long evolutionary time that renders codon-based methods ineffective for older divergences. This work aims to address these shortcomings for detecting positive selection through the development of a statistical model that examines clusters of substitutions in clusters of variable radii. Additionally, it uses a parametric bootstrapping approach to differentiate positive selection from compensatory processes. A previously reported case of positive selection in the leptin protein of primates was reexamined using this methodology.

An Improved Three-Dimensional Target Octonion Representation

2012 ◽  
Vol 263-266 ◽  
pp. 1614-1618
Author(s):  
Xiang Hua Chen ◽  
Juan Zhou
Keyword(s):  
3D Reconstruction ◽  
Traditional Method ◽  
Medical Images ◽  
Three Dimensional ◽  
Experimental Results ◽  
Three Dimensional Objects ◽  
Traditional Structure ◽  
Large Memory ◽  
Octree Representation

It is an efficient way to represent three-dimensional objects by octree.The traditional structure of pointer -based octree representation has several shortcomings,such as requiring large memory,missing relationship between two nodes,etc.Based on analyzing the space Iayout and the configuration of octree,this paper presents an improved octree for 3D representation.From the experimental results for 3D reconstruction of medical images,we can see the proposed method is superior to the traditional method in terms of the storing structure and visiting way,etc.

A method of 3D reconstruction based on panoramic cameras

2015 ◽  
Vol 06 (02) ◽  
pp. 1550021
Author(s):  
Hua-Gang Liang ◽  
Wen-Xiu Qian ◽  
Yong-Kui Liu ◽  
Feng Ru
Keyword(s):  
3D Reconstruction ◽  
Calibration Method ◽  
Matching Method ◽  
Panoramic Cameras ◽  
Optical Correction ◽  
Internal Parameters ◽  
Panoramic Images ◽  
Geometric Relationship

In this paper, a method of 3D reconstruction from two images acquired by two panoramic cameras is presented. Firstly, the features of the reconstruction object detected in each image are matched through the DP matching method. Secondly, optical correction is carried out on two cameras, and the internal parameters of panoramic cameras can be calculated. Finally, according to the calibration method, the geometric relationship between corresponding points in space and in two panoramic images is deduced. The results indicate that the method of 3D reconstruction based on two panoramic cameras is simple, and the accuracy can reach 98.82%.

Development of 3D Reconstruction and Object Recognition Model using Video

2020 ◽  
Vol 21 (11) ◽  
pp. 2011-2019
Author(s):  
Nahyuk Lee ◽  
Kyungtaek Lee ◽  
Youngsup Park ◽  
Sanghyun Seo ◽  
Taemin Lee
Keyword(s):  
Object Recognition ◽  
3D Reconstruction ◽  
Recognition Model

scholarly journals Calculation Method for Phenotypic Traits Based on the 3D Reconstruction of Maize Canopies

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1201 ◽  
Author(s):  
Xiaodan Ma ◽  
Kexin Zhu ◽  
Haiou Guan ◽  
Jiarui Feng ◽  
Song Yu ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Plant Height ◽  
Calculation Method ◽  
3D Structure ◽  
Calculated Data ◽  
Point Clouds ◽  
Measured Data ◽  
Stem Diameter ◽  
Phenotypic Traits ◽  
Area Index

A reasonable plant type is an essential factor for improving canopy structure, ensuring a reasonable expansion of the leaf area index and obtaining a high-quality spatial distribution of light. It is of great significance in promoting effective selection of the ecological breeding index and production practices for maize. In this study, a method for calculating the phenotypic traits of the maize canopy in three-dimensional (3D) space was proposed, focusing on the problems existing in traditional measurement methods in maize morphological structure research, such as their complex procedures and relatively large error margins. Specifically, the whole maize plant was first scanned with a FastSCAN hand-held scanner to obtain 3D point cloud data for maize. Subsequently, the raw point clouds were simplified by the grid method, and the effect of noise on the quality of the point clouds in maize canopies was further denoised by bilateral filtering. In the last step, the 3D structure of the maize canopy was reconstructed. In accordance with the 3D reconstruction of the maize canopy, the phenotypic traits of the maize canopy, such as plant height, stem diameter and canopy breadth, were calculated by means of a fitting sphere and a fitting cylinder. Thereafter, multiple regression analysis was carried out, focusing on the calculated data and the actual measured data to verify the accuracy of the calculation method proposed in this study. The corresponding results showed that the calculated values of plant height, stem diameter and plant width based on 3D scanning were highly correlated with the actual measured data, and the determinant coefficients R2 were 0.9807, 0.8907 and 0.9562, respectively. In summary, the method proposed in this study can accurately measure the phenotypic traits of maize. Significantly, these research findings provide technical support for further research on the phenotypic traits of other crops and on variety breeding.

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