scholarly journals Automated Landmarking via Multiple Templates

2022 ◽  
Author(s):  
Chi Zhang ◽  
Arthur Porto ◽  
Sara Rolfe ◽  
Altan Kocatulum ◽  
A. Murat Maga
Keyword(s):  
Point Cloud ◽  
Selection Process ◽  
Human Observer ◽  
Fast Method ◽  
Complex Phenotypes ◽  
Evolutionary Studies ◽  
Landmark Data ◽  
Template Selection ◽  
Variable Morphology ◽  
Better Than

Geometric morphometrics based on landmark data has been increasingly used in biomedical and biological researchers for quantifying complex phenotypes. However, manual landmarking can be laborious and subject to intra and interobserver errors. This has motivated the development of automated landmarking methods. We have recently introduced ALPACA (Automated Landmarking through Point cloud Alignment and Correspondence), a fast method to automatically annotate landmarks via use of a landmark template as part of the SlicerMorph toolkit. Yet, using a single template may not consistently perform well for large study samples, especially when the sample consists of specimen with highly variable morphology, as it is common evolutionary studies. In this study, we introduce a variation on our ALPACA pipeline that supports multiple specimen templates, which we call MALPACA. We show that MALPACA outperforms ALPACA consistently by testing on two different datasets. We also introduce a method of choosing the templates that can be used in conjunction with MALPACA, when no prior information is available. This K-means method uses an approximation of the total morphological variation in the dataset to suggest samples within the population to be used as landmark templates. While we advise investigators to pay careful attention to the template selection process in any of the template-based automated landmarking approaches, our analyses show that the introduced K-means based method of templates selection is better than randomly choosing the templates. In summary, MALPACA can accommodate larger morphological disparity commonly found in evolutionary studies with performance comparable to human observer.

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

Research on Material Selection with Multi-Attribute Decision Method and G1 Method

2014 ◽  
Vol 952 ◽  
pp. 20-24 ◽  
Author(s):  
Xue Jun Xie
Keyword(s):  
Material Selection ◽  
Selection Process ◽  
Selection Problem ◽  
Topsis Method ◽  
Attribute Weight ◽  
Multi Attribute Decision Making ◽  
Judgment Matrix ◽  
Optimal Material ◽  
Selection Of ◽  
Better Than

The selection of an optimal material is an important aspect of design for mechanical, electrical, thermal, chemical or other application. Many factors (attributes) need to be considered in material selection process, and thus material selection problem is a multi-attribute decision making (MADM) problem. This paper proposes a new MADM method for material selection problem. G1 method does not need to test consistency of the judgment matrix. Thus it is better than AHP. In this paper, firstly, we use the G1 method to determine the attribute weight. Then TOPSIS method is used to calculate the closeness of the candidate materials with respect positive solution. A practical material selection case is used to demonstrate the effectiveness and feasibility of the proposed method.

scholarly journals Multi-View Laser Point Cloud Global Registration for a Single Object

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3729 ◽  
Author(s):  
Shuai Wang ◽  
Hua-Yan Sun ◽  
Hui-Chao Guo ◽  
Lin Du ◽  
Tian-Jian Liu
Keyword(s):  
Spatial Distribution ◽  
Objective Function ◽  
Point Cloud ◽  
Point Clouds ◽  
Low Rank ◽  
Single Object ◽  
Loop Closure Detection ◽  
Global Registration ◽  
Distribution Features ◽  
Better Than

Global registration is an important step in the three-dimensional reconstruction of multi-view laser point clouds for moving objects, but the severe noise, density variation, and overlap ratio between multi-view laser point clouds present significant challenges to global registration. In this paper, a multi-view laser point cloud global registration method based on low-rank sparse decomposition is proposed. Firstly, the spatial distribution features of point clouds were extracted by spatial rasterization to realize loop-closure detection, and the corresponding weight matrix was established according to the similarities of spatial distribution features. The accuracy of adjacent registration transformation was evaluated, and the robustness of low-rank sparse matrix decomposition was enhanced. Then, the objective function that satisfies the global optimization condition was constructed, which prevented the solution space compression generated by the column-orthogonal hypothesis of the matrix. The objective function was solved by the Augmented Lagrange method, and the iterative termination condition was designed according to the prior conditions of single-object global registration. The simulation analysis shows that the proposed method was robust with a wide range of parameters, and the accuracy of loop-closure detection was over 90%. When the pairwise registration error was below 0.1 rad, the proposed method performed better than the three compared methods, and the global registration accuracy was better than 0.05 rad. Finally, the global registration results of real point cloud experiments further proved the validity and stability of the proposed method.

Fuzzy Heuristics for Sequential Linear Programming

1998 ◽  
Vol 120 (1) ◽  
pp. 17-23 ◽  
Author(s):  
E. L. Mulkay ◽  
S. S. Rao
Keyword(s):  
Fuzzy Logic ◽  
Linear Programming ◽  
Path Following ◽  
Human Observer ◽  
Sequential Linear Programming ◽  
Algorithm Performance ◽  
Primal Dual ◽  
Improve Algorithm ◽  
Range Of Values ◽  
Better Than

Numerical implementations of optimization algorithms often use parameters whose values are not strictly determined by the derivation of the algorithm, but must fall in some appropriate range of values. This work describes how fuzzy logic can be used to “control” such parameters to improve algorithm performance. This concept is shown with the use of sequential linear programming (SLP) due to its simplicity in implementation. The algorithm presented in this paper implements heuristics to improve the behavior of SLP based on current iterate values of design constraints and changes in search direction. Fuzzy logic is used to implement the heuristics in a form similar to what a human observer would do. An efficient algorithm, known as the infeasible primal-dual path-following interior-point method, is used for solving the sequence of LP problems. Four numerical examples are presented to show that the proposed SLP algorithm consistently performs better than the standard SLP algorithm.

scholarly journals A networked voting rule for democratic representation

2018 ◽  
Vol 5 (3) ◽  
pp. 172265 ◽  
Author(s):  
Alexis R. Hernández ◽  
Carlos Gracia-Lázaro ◽  
Edgardo Brigatti ◽  
Yamir Moreno
Keyword(s):  
Large Scale ◽  
Selection Process ◽  
General Relation ◽  
Community Size ◽  
Rule Based ◽  
Voting Rule ◽  
Large Populations ◽  
Negative Effect ◽  
High Connectivity ◽  
Better Than

We introduce a general framework for exploring the problem of selecting a committee of representatives with the aim of studying a networked voting rule based on a decentralized large-scale platform, which can assure a strong accountability of the elected. The results of our simulations suggest that this algorithm-based approach is able to obtain a high representativeness for relatively small committees, performing even better than a classical voting rule based on a closed list of candidates. We show that a general relation between committee size and representatives exists in the form of an inverse square root law and that the normalized committee size approximately scales with the inverse of the community size, allowing the scalability to very large populations. These findings are not strongly influenced by the different networks used to describe the individuals’ interactions, except for the presence of few individuals with very high connectivity which can have a marginal negative effect in the committee selection process.

scholarly journals Some External Characteristics of the Gala and Bozakh Sheep Native Breeds and Their Hybrids

2021 ◽  
Vol 7 (12) ◽  
pp. 87-91
Author(s):  
Q. Abdullayev ◽  
I. Farajullayeva
Keyword(s):  
Selection Process ◽  
Native Breed ◽  
The Village ◽  
External Characteristics ◽  
Better Than

In Azerbaijan, sheep mating is natural in both small and large sheep farms and very little attention is paid to the selection process. For industrial crossing of local sheep (western part of the country), five breeding rams of 1.5 years old were purchased in the village of Gala (Absheron) and delivered to the Gaji Tagi farm in the Dashkesan district. Two groups were formed from a native herd of 100 sheep each. Fertilization of females was carried out from October 15 to December 1. According to the indexing data, the external signs of the native breed of Bozakh sheep are better than those of other similar breeds. From the results obtained, it should be concluded that the livestock of Gala and Bozakh breeds can be adapted to the foothills of Azerbaijan.

scholarly journals MODIFIKASI BARU ALGORITMA KOLONI LEBAH BUATAN UNTUK MASALAH OPTIMASI GLOBAL

2018 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Nursyiva Irsalinda ◽  
Sugiyarto Surono
Keyword(s):  
Food Source ◽  
Artificial Bee Colony ◽  
Selection Process ◽  
Optimization Technique ◽  
Food Sources ◽  
Abc Algorithm ◽  
Average Value ◽  
Bee Colony ◽  
Objective Function Values ◽  
Better Than

Artificial Bee Colony (ABC) algorithm is one of metaheuristic optimization technique based on population. This algorithm mimicking honey bee swarm to find the best food source. ABC algorithm consist of four phases: initialization phase, employed bee phase, onlooker bee phase and scout bee phase. This study modify the onlooker bee phase in selection process to find the neighborhood food source. Not all food sources obtained are randomly sought the neighborhood as in ABC algorithm. Food sources are selected by comparing their objective function values. The food sources that have value lower than average value in that iteration will be chosen by onlooker bee to get the better food source. In this study the modification of this algorithm is called New Modification of Artificial Bee Colony Algorithm (MB-ABC). MB-ABC was applied to 4 Benchmark functions. The results show that MB-ABC algorithm better than ABC algorithm

scholarly journals The Mortality in Emergency Department Sepsis Score as a Predictor of 1-Month Mortality among Adult Patients with Sepsis: Weighing the Evidence

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Bayushi Eka Putra ◽  
Ling Tiah
Keyword(s):  
Emergency Department ◽  
Outcome Measure ◽  
Selection Process ◽  
Area Under The Curve ◽  
C Reactive Protein ◽  
Consensus Process ◽  
Suspected Sepsis ◽  
Reactive Protein ◽  
Sepsis Score ◽  
Better Than

Objective. To evaluate the performance of Mortality in Emergency Department Sepsis (MEDS) score in comparison to biomarkers as a predictor of mortality in adult emergency department (ED) patients with sepsis. Methods. A literature search was performed using PubMed, ScienceDirect, SpringerLink, and Ovid databases. Studies were appraised by using the C2010 Consensus Process for Levels of Evidence for prognostic studies. The respective values for area under the curve (AUC) were obtained from the selected articles. Results. Four relevant articles met the selection process. Three studies defined the 1-month mortality as death occurring within 28 days of ED presentation, while the remaining one subcategorised the outcome measure as (5-day) early and (6- to 30-day) late mortality. In all four studies, the MEDS score performed better than the respective comparators (C-reactive protein, lactate, procalcitonin, and interleukin-6) in predicting mortality with an AUC ranging from 0.78 to 0.89 across the studies. Conclusion. The MEDS score has a better prognostic value than the respective comparators in predicting 1-month mortality in adult ED patients with suspected sepsis.

scholarly journals Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7026
Author(s):  
Dor Mizrahi ◽  
Inon Zuckerman ◽  
Ilan Laufer
Keyword(s):  
Focal Point ◽  
Selection Process ◽  
Belief System ◽  
Behavioral Model ◽  
Mean Value ◽  
Coordination Games ◽  
Autonomous Agent ◽  
Behavioral Game Theory ◽  
Tacit Coordination ◽  
Better Than

In recent years collaborative robots have become major market drivers in industry 5.0, which aims to incorporate them alongside humans in a wide array of settings ranging from welding to rehabilitation. Improving human–machine collaboration entails using computational algorithms that will save processing as well as communication cost. In this study we have constructed an agent that can choose when to cooperate using an optimal strategy. The agent was designed to operate in the context of divergent interest tacit coordination games in which communication between the players is not possible and the payoff is not symmetric. The agent’s model was based on a behavioral model that can predict the probability of a player converging on prominent solutions with salient features (e.g., focal points) based on the player’s Social Value Orientation (SVO) and the specific game features. The SVO theory pertains to the preferences of decision makers when allocating joint resources between themselves and another player in the context of behavioral game theory. The agent selected stochastically between one of two possible policies, a greedy or a cooperative policy, based on the probability of a player to converge on a focal point. The distribution of the number of points obtained by the autonomous agent incorporating the SVO in the model was better than the results obtained by the human players who played against each other (i.e., the distribution associated with the agent had a higher mean value). Moreover, the distribution of points gained by the agent was better than any of the separate strategies the agent could choose from, namely, always choosing a greedy or a focal point solution. To the best of our knowledge, this is the first attempt to construct an intelligent agent that maximizes its utility by incorporating the belief system of the player in the context of tacit bargaining. This reward-maximizing strategy selection process based on the SVO can also be potentially applied in other human–machine contexts, including multiagent systems.

scholarly journals A FAST METHOD FOR MEASURING THE SIMILARITY BETWEEN 3D MODEL AND 3D POINT CLOUD

2016 ◽  
Vol XLI-B1 ◽  
pp. 725-728
Author(s):  
Zongliang Zhang ◽  
Jonathan Li ◽  
Xin Li ◽  
Yangbin Lin ◽  
Shanxin Zhang ◽  
...  
Keyword(s):  
Surface Area ◽  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Average Distance ◽  
Synthetic Data ◽  
Fast Method ◽  
3D Point Cloud ◽  
Partial Similarity ◽  
Distance Weighted

This paper proposes a fast method for measuring the partial Similarity between 3D Model and 3D point Cloud (SimMC). It is crucial to measure SimMC for many point cloud-related applications such as 3D object retrieval and inverse procedural modelling. In our proposed method, the surface area of model and the Distance from Model to point Cloud (DistMC) are exploited as measurements to calculate SimMC. Here, DistMC is defined as the weighted distance of the distances between points sampled from model and point cloud. Similarly, Distance from point Cloud to Model (DistCM) is defined as the average distance of the distances between points in point cloud and model. In order to reduce huge computational burdens brought by calculation of DistCM in some traditional methods, we define SimMC as the ratio of weighted surface area of model to DistMC. Compared to those traditional SimMC measuring methods that are only able to measure global similarity, our method is capable of measuring partial similarity by employing distance-weighted strategy. Moreover, our method is able to be faster than other partial similarity assessment methods. We demonstrate the superiority of our method both on synthetic data and laser scanning data.

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