scholarly journals Performance Evaluation of Bundle Adjustment with Population Based Optimization Algorithms Applied to Panoramic Image Stitching

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5054
Author(s):  
Maria Júlia R. Aguiar ◽  
Tiago da Rocha Alves ◽  
Leonardo M. Honório ◽  
Ivo C. S. Junior ◽  
Vinícius F. Vidal
Keyword(s):  
Traditional Approach ◽  
Bat Algorithm ◽  
Population Based ◽  
Bundle Adjustment ◽  
Grey Wolf Optimizer ◽  
Image Stitching ◽  
Industrial Grade ◽  
Levenberg Marquardt ◽  
Automatic Image Stitching ◽  
3D Scene

The image stitching process is based on the alignment and composition of multiple images that represent parts of a 3D scene. The automatic construction of panoramas from multiple digital images is a technique of great importance, finding applications in different areas such as remote sensing and inspection and maintenance in many work environments. In traditional automatic image stitching, image alignment is generally performed by the Levenberg–Marquardt numerical-based method. Although these traditional approaches only present minor flaws in the final reconstruction, the final result is not appropriate for industrial grade applications. To improve the final stitching quality, this work uses a RGBD robot capable of precise image positing. To optimize the final adjustment, this paper proposes the use of bio-inspired algorithms such as Bat Algorithm, Grey Wolf Optimizer, Arithmetic Optimization Algorithm, Salp Swarm Algorithm and Particle Swarm Optimization in order verify the efficiency and competitiveness of metaheuristics against the classical Levenberg–Marquardt method. The obtained results showed that metaheuristcs have found better solutions than the traditional approach.

scholarly journals Montagem de panorâmicas 360 através de Multi-band Blending com ajuste ótimo via meta-heurísticas

2021 ◽  
Author(s):  
◽  
Maria Júlia Rosa Aguiar
Keyword(s):  
Bat Algorithm ◽  
Bundle Adjustment ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
De Se ◽  
Multi Band

O Stitching de imagens é o alinhamento de múltiplas imagens em composições maiores que representam partes de uma cena 3D. A construção automática de panoramas a partir de múltiplas imagens digitais é uma área de grande importância, encontrando aplicações em diferentes setores como sensoriamento remoto, inspeção e manutenção em ambientes de trabalho e medicina. Diversos algoritmos de mosaico de imagens foram propostos nos últimos anos. Ao mesmo tempo, o advento contínuo de novos métodos de mosaico torna muito difícil escolher um algoritmo apropriado para uma finalidade específica. Este trabalho apresenta técnicas para a montagem de panorâmicas 360° a partir de imagens tiradas por um sistema robótico desenvolvido. Foram utilizados os algoritmos de otimização bioinspirados Grey Wolf Optimizer e Bat Algorithm com intuito de se obter um ajuste ótimo no posicionamento das imagens sendo responsáveis por um Bundle adjustment. Após, o ajustamento das imagens para se corrigir possíveis diferenças de coloração e discrepâncias nas imagens utiliza-se a metodologia Multi-band Blending para se obter, ao final, uma imagem uniforme. A comparação entre os algoritmos envolverá análise da variabilidade das soluções e características de convergência.

Distributed Bundle Adjustment in 3D Scene Reconstruction with

2012 ◽  
Vol 38 (9) ◽  
pp. 1428 ◽  
Author(s):  
Xin LIU ◽  
Feng-Mei SUN ◽  
Zhan-Yi HU
Keyword(s):  
Bundle Adjustment ◽  
Scene Reconstruction ◽  
3D Scene ◽  
3D Scene Reconstruction

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

scholarly journals INFLUENCE ON HOMEOSTASIS AS THE CRITERION FOR SELECTING SINGLE NUCLEOTIDE POLYMORPHISMS FOR THE STUDY OF METABOLIC SYNDROM IN THE KAZAKH POPULATION

REPORTS ◽  
2020 ◽  
Vol 5 (333) ◽  
pp. 86-93
Author(s):  
V.V. Benberin ◽  
◽  
T.A. Voshchenkova ◽  
A.A. Nagymtayeva ◽  
A.S. Sibagatova ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Traditional Approach ◽  
Population Based ◽  
Malignant Neoplasms ◽  
Nucleotide Polymorphisms ◽  
Genetic Determinants ◽  
Single Nucleotide ◽  
Sequence Of Events ◽  
Kazakh Population ◽  
Symptom Complex

Metabolic syndrome (MS) is increasingly cited as the world's leading health risk. The sequence of events toward multimorbidity in most cases passes through MS. According to the research, MS heritability ranges from 23 to 27% in Europeans, and 51 to 60% in Asians. The purpose of the review: to form a strategy for the selection of single nucleotide polymorphisms (SNPs) for the study of MS in the Kazakh population based on the effect of SNPs on homeostasis indicators The stable symptom complex of MS is a complicated dynamic system of successive accumulations of dysmetabolic disorders of homeostasis. This system starts the development of subsequent age-associated diseases), such as cardiometabolic, neurodegenerative, and malignant neoplasms. The system for selecting SNPs for the MS study, proposed on the basis of the concept of homeostasis dysfunction, assumes, in conditions of limited resources, to see the greatest level of their influence within the conditional framework of three genetic models of homeostasis dysregulation: insulin resistance , oxidative stress, and chronic inflammation. This approach is fundamentally different from the traditional approach involving candidate genes. It is expected that scientific research in this direction will contribute not only to the understanding of general biological processes, but also to the targeted search for genetic determinants and for new opportunities for personalized interventions.

scholarly journals MODERN METHODS OF BUNDLE ADJUSTMENT ON THE GPU

2016 ◽  
Vol III-3 ◽  
pp. 43-50 ◽  
Author(s):  
R. Hänsch ◽  
I. Drude ◽  
O. Hellwich
Keyword(s):  
Conjugate Gradient ◽  
Gradient Descent ◽  
Optimization Problem ◽  
Error Rates ◽  
Bundle Adjustment ◽  
Test Cases ◽  
Current State ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Nonlinear Conjugate Gradient

The task to compute 3D reconstructions from large amounts of data has become an active field of research within the last years. Based on an initial estimate provided by structure from motion, bundle adjustment seeks to find a solution that is optimal for all cameras and 3D points. The corresponding nonlinear optimization problem is usually solved by the Levenberg-Marquardt algorithm combined with conjugate gradient descent. While many adaptations and extensions to the classical bundle adjustment approach have been proposed, only few works consider the acceleration potentials of GPU systems. This paper elaborates the possibilities of time and space savings when fitting the implementation strategy to the terms and requirements of realizing a bundler on heterogeneous CPUGPU systems. Instead of focusing on the standard approach of Levenberg-Marquardt optimization alone, nonlinear conjugate gradient descent and alternating resection-intersection are studied as two alternatives. The experiments show that in particular alternating resection-intersection reaches low error rates very fast, but converges to larger error rates than Levenberg-Marquardt. PBA, as one of the current state-of-the-art bundlers, converges slower in 50 % of the test cases and needs 1.5-2 times more memory than the Levenberg- Marquardt implementation.

scholarly journals Optimal Synthesis of Four-Bar Linkage Path Generation through Evolutionary Computation with a Novel Constraint Handling Technique

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Suwin Sleesongsom ◽  
Sujin Bureerat
Keyword(s):  
Population Based ◽  
Constraint Handling ◽  
New Technique ◽  
Grey Wolf Optimizer ◽  
Test Problems ◽  
Path Generation ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Handling Technique

This paper presents a novel constraint handling technique for optimum path generation of four-bar linkages using evolutionary algorithms (EAs). Usually, the design problem is assigned to minimize the error between desired and obtained coupler curves with penalty constraints. It is found that the currently used constraint handling technique is rather inefficient. In this work, we propose a new technique, termed a path repairing technique, to deal with the constraints for both input crank rotation and Grashof criterion. Three traditional path generation test problems are used to test the proposed technique. Metaheuristic algorithms, namely, artificial bee colony optimization (ABC), adaptive differential evolution with optional external archive (JADE), population-based incremental learning (PBIL), teaching-learning-based optimization (TLBO), real-code ant colony optimization (ACOR), a grey wolf optimizer (GWO), and a sine cosine algorithm (SCA), are applied for finding the optimum solutions. The results show that new technique is a superior constraint handling technique while TLBO is the best method for synthesizing four-bar linkages.

A Comprehensive Literature Review on Nature-Inspired Soft Computing and Algorithms

2020 ◽  
pp. 1851-1885
Author(s):  
Bilal Ervural ◽  
Beyzanur Cayir Ervural ◽  
Cengiz Kahraman
Keyword(s):  
Soft Computing ◽  
Bat Algorithm ◽  
Computing Methods ◽  
Analytical Models ◽  
Grey Wolf Optimizer ◽  
Particle Swarm Algorithm ◽  
Bee Colony ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Firefly Optimization

Soft Computing techniques are capable of identifying uncertainty in data, determining imprecision of knowledge, and analyzing ill-defined complex problems. The nature of real world problems is generally complex and their common characteristic is uncertainty owing to the multidimensional structure. Analytical models are insufficient in managing all complexity to satisfy the decision makers' expectations. Under this viewpoint, soft computing provides significant flexibility and solution advantages. In this chapter, firstly, the major soft computing methods are classified and summarized. Then a comprehensive review of eight nature inspired – soft computing algorithms which are genetic algorithm, particle swarm algorithm, ant colony algorithms, artificial bee colony, firefly optimization, bat algorithm, cuckoo algorithm, and grey wolf optimizer algorithm are presented and analyzed under some determined subject headings (classification topics) in a detailed way. The survey findings are supported with charts, bar graphs and tables to be more understandable.

Automatic Incremental Clustering Using Bat-Grey Wolf Optimizer-Based MapReduce Framework for Effective Management of High-Dimensional Data

2020 ◽  
Vol 11 (4) ◽  
pp. 72-92
Author(s):  
Ch. Vidyadhari ◽  
N. Sandhya ◽  
P. Premchand
Keyword(s):  
Feature Extraction ◽  
Bat Algorithm ◽  
Grey Wolf Optimizer ◽  
Jaccard Coefficient ◽  
Grey Wolf ◽  
Incremental Clustering ◽  
Mapreduce Framework ◽  
Text Documents ◽  
Two Phases ◽  
Point Distance

In this research paper, an incremental clustering approach-enabled MapReduce framework is implemented that include two phases, mapper and reducer phase. In the mapper phase, there are two processes, pre-processing and feature extraction. Once the input data is pre-processed, the feature extraction is done using wordnet features. Then, the features are fed to the reducer phase, where the features are selected using entropy function. Then, the automatic incremental clustering is done using bat-grey wolf optimizer (BAGWO). BAGWO is the integration of bat algorithm (BA) into grey wolf optimization (GWO) for generating various clusters of text documents. Upon the arrival of the incremental data, the mapping of the new data with respect to the centroids is done to obtain the effective cluster. For mapping, kernel-based deep point distance and for centroid update, fuzzy concept is used. The performance of the proposed framework outperformed the existing techniques using rand coefficient, Jaccard coefficient, and clustering accuracy with maximal values 0.921, 0.920, and 0.95, respectively.

scholarly journals TV White Space Network Power Allocation Using Hybrid Grey Wolf Optimizer with Firefly Algorithm and Particle Swarm Optimization

2021 ◽  
Vol 20 ◽  
pp. 66-75
Author(s):  
Kennedy Ronoh ◽  
George Kamucha
Keyword(s):  
Particle Swarm Optimization ◽  
Power Allocation ◽  
Firefly Algorithm ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Secondary Users ◽  
Swarm Optimization ◽  
Network Power

TV white spaces (TVWS) can be utilized by Secondary Users (SUs) equipped with cognitive radio functionality on the condition that they do not cause harmful interference to Primary Users (PUs). Optimization of power allocation is necessary when there is a high density of secondary users in a network in order to reduce the level of interference among SUs and to protect PUs against harmful interference. Grey Wolf Optimizer (GWO) is relatively recent population based metaheuristic algorithm that has shown superior performance compared to other population based metaheuristic algorithms. Recent trend has been to hybridize population based metaheuristic algorithms in order to avoid the problem of getting trapped in a local optimum. This paper presents the design and analysis of performance of a hybrid grey wolf optimizer and Firefly Algorithm (FA) with Particle Swarm Optimization operators for optimization of power allocation in TVWS network power allocation as a continuous optimization problem. Matlab was used for simulation. The hybrid of GWO, FA and PSO (HFAGWOPSO) reduces sum power by 81.42% compared to GWO and improves sum throughput by 16.41% when compared to GWO. Simulation results also show that the algorithm has better convergence rate.

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