A GA-BASED APPROACH FOR EPIPOLAR GEOMETRY ESTIMATION

2013 ◽  
Vol 27 (08) ◽  
pp. 1355014 ◽  
Author(s):  
NEHAL KHALED ◽  
ELSAYED E. HEMAYED ◽  
MAGDA B. FAYEK
Keyword(s):  
Genetic Algorithm ◽  
Fundamental Matrix ◽  
Real Data ◽  
Epipolar Geometry ◽  
Optimum Estimation ◽  
Geometry Estimation

In this paper, a genetic algorithm (GA)-based approach to estimate the fundamental matrix is presented. The aim of the proposed GA-based algorithm is to reduce the effect of noise and outliers in the corresponding points which affect the accuracy of the estimated fundamental matrix. Although in the proposed approach the GA is allowed to select the significant among all detected points, on the average half of the matched points have been determined to give optimum estimation of the fundamental matrix. Experiments with synthetic and real data show that the proposed approach is accurate especially in the presence of a high percentage of outliers. The proposed GA can always obtain good results in both high and low detailed images. Even for low detailed images which have a small number of matched points available to estimate the fundamental matrix, the proposed GA outperformed other methods.

Patent Innovation Factors Evolution Based on P-SVM and GCCA

2010 ◽  
Vol 39 ◽  
pp. 247-252
Author(s):  
Sheng Xu ◽  
Zhi Juan Wang ◽  
Hui Fang Zhao
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Correlation Coefficient ◽  
Network Architecture ◽  
Innovation Management ◽  
Model Development ◽  
Real Data ◽  
Support Vector ◽  
Vector Machines ◽  
Gray Correlation

A two-stage neural network architecture constructed by combining potential support vector machines (P-SVM) with genetic algorithm (GA) and gray correlation coefficient analysis (GCCA) is proposed for patent innovation factors evolution. The enterprises patent innovation is complex to conduct due to its nonlinearity of influenced factors. It is necessary to make a trade off among these factors when some of them conflict firstly. A novel way about nonlinear regression model with the potential support vector machines (P-SVM) is presented in this paper. In the model development, the genetic algorithm is employed to optimize P-SVM parameters selection. After the selected key factors by the PSVM with GA model, the main factors that affect patent innovation generation have been quantitatively studied using the method of gray correlation coefficient analysis. Using a set of real data in China, the results show that the methods developed in this paper can provide valuable information for patent innovation management and related municipal planning projects.

scholarly journals Genetic Algorithm for Exam Timetabling Problem - A Specific Case for Japanese University Final Presentation Timetabling

2020 ◽  
Author(s):  
Jiawei LI ◽  
Tad Gonsalves
Keyword(s):  
Genetic Algorithm ◽  
Real Data ◽  
Initial Population ◽  
Exam Timetabling ◽  
Timetabling Problem ◽  
Training Approach ◽  
Chromosome Representation ◽  
Genetic Algorithm Approach ◽  
Hard And Soft Constraints ◽  
Microsoft Office

This paper presents a Genetic Algorithm approach to solve a specific examination timetabling problem which is common in Japanese Universities. The model is programmed in Excel VBA programming language, which can be run on the Microsoft Office Excel worksheets directly. The model uses direct chromosome representation. To satisfy hard and soft constraints, constraint-based initialization operation, constraint-based crossover operation and penalty points system are implemented. To further improve the result quality of the algorithm, this paper designed an improvement called initial population pre-training. The proposed model was tested by the real data from Sophia University, Tokyo, Japan. The model shows acceptable results, and the comparison of results proves that the initial population pre-training approach can improve the result quality.

scholarly journals The Combination of Morphological and Evolutionary Algorithms for Graph-based Pathfinding on Maps with Complex Topologies

2019 ◽  
Author(s):  
Игорь Савостин ◽  
Igor' Savostin ◽  
Андрей Трубаков ◽  
Andrey Trubakov
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Real Data ◽  
Combinatorial Complexity ◽  
Ant Algorithm ◽  
Stochastic Algorithms ◽  
Morphological Operations ◽  
Path Finding ◽  
Large Size ◽  
Dijkstra's Algorithm

One of the difficult problems to solve has always been and still remains the problem of finding a path either in a graphic chart or a graphic maze of large size. The main problem is that traditional algorithms require a lot of time due to combinatorial complexity. At the same time, both classical algorithms based on the search of variants (such as Dijkstra's algorithm, A*, ARA*, D* lite), and stochastic algorithms (ant algorithm, genetic), alongside with algorithms based on morphology (wave) are not always able to achieve the goal. The article proposes a new modification of the path-finding algorithm, which is a hybrid of the following: the morphological operations on graphic chart approach and genetic algorithm having a useful property of elasticity in time. The experiments (both synthetic and real data) have shown the feasibility of the proposed idea and its comparison with the most commonly used algorithms of contemporaneity.

scholarly journals Modeling the Municipal Waste Collection Using Genetic Algorithms

Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 513
Author(s):  
Elisabete Alberdi ◽  
Leire Urrutia ◽  
Aitor Goti ◽  
Aitor Oyarbide-Zubillaga
Keyword(s):  
Genetic Algorithm ◽  
Variable Neighborhood Search ◽  
Real Data ◽  
Municipal Waste ◽  
Neighborhood Search ◽  
Initial Population ◽  
Waste Collection ◽  
Reduction Of Emissions ◽  
Apparent Reasons ◽  
Unsolved Issue

Calculating adequate vehicle routes for collecting municipal waste is still an unsolved issue, even though many solutions for this process can be found in the literature. A gap still exists between academics and practitioners in the field. One of the apparent reasons why this rift exists is that academic tools often are not easy to handle and maintain by actual users. In this work, the problem of municipal waste collection is modeled using a simple but efficient and especially easy to maintain solution. Real data have been used, and it has been solved using a Genetic Algorithm (GA). Computations have been done in two different ways: using a complete random initial population, and including a seed in this initial population. In order to guarantee that the solution is efficient, the performance of the genetic algorithm has been compared with another well-performing algorithm, the Variable Neighborhood Search (VNS). Three problems of different sizes have been solved and, in all cases, a significant improvement has been obtained. A total reduction of 40% of itineraries is attained with the subsequent reduction of emissions and costs.

Camera Calibration and Improved Computation of Fundamental Matrix in Epipolar Geometry

2013 ◽  
Vol 347-350 ◽  
pp. 3624-3628 ◽  
Author(s):  
Jia Duan ◽  
Yuan Yan Tang ◽  
Zhen Chao Zhang ◽  
Chu Yu Guo ◽  
Chi Fang
Keyword(s):  
Visual System ◽  
Camera Calibration ◽  
Fundamental Matrix ◽  
3D Model ◽  
Epipolar Geometry ◽  
Camera Parameters ◽  
Computation Algorithms ◽  
Mathematical Derivation

In this paper, firstly we try to look for ways to avoid the camera parameters in order to reconstruct 3D model. We attempt to use the parallel stereo visual system and carry out the mathematical derivation of argumentation. Then we use epipolar geometry to solve this problem. And compare the computation algorithms of fundamental matrix. Then for the algorithm, we propose some improvement to compute the fundament matrix more precisely so that the algorithm is more stable and the robustness is stronger.

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