scholarly journals Firefly-Based Approaches of Image Recognition

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 881
Author(s):  
Catalina-Lucia Cocianu ◽  
Alexandru Daniel Stan ◽  
Mihai Avramescu
Keyword(s):  
Mutual Information ◽  
Firefly Algorithm ◽  
Fitness Function ◽  
Population Based ◽  
Evolutionary Strategy ◽  
Normalized Mutual Information ◽  
Principal Axes ◽  
The One ◽  
Computation Algorithms ◽  
Population Based Search

The main aim of the reported work is to solve the registration problem for recognition purposes. We introduce two new evolutionary algorithms (EA) consisting of population-based search methods, followed by or combined with a local search scheme. We used a variant of the Firefly algorithm to conduct the population-based search, while the local exploration was implemented by the Two-Membered Evolutionary Strategy (2M-ES). Both algorithms use fitness function based on mutual information (MI) to direct the exploration toward an appropriate candidate solution. A good similarity measure is the one that enables us to predict well, and with the symmetric MI we tie similarity between two objects A and B directly to how well A predicts B, and vice versa. Since the search landscape of normalized mutual information proved more amenable for evolutionary computation algorithms than simple MI, we use normalized mutual information (NMI) defined as symmetric uncertainty. The proposed algorithms are tested against the well-known Principal Axes Transformation technique (PAT), a standard evolutionary strategy and a version of the Firefly algorithm developed to align images. The accuracy and the efficiency of the proposed algorithms are experimentally confirmed by our tests, both methods being excellently fitted to registering images.

scholarly journals Cluster-Based Memetic Approach of Image Alignment

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2606
Author(s):  
Cătălina Lucia COCIANU ◽  
Cristian Răzvan USCATU
Keyword(s):  
Signal To Noise Ratio ◽  
Search Space ◽  
Population Based ◽  
Evolutionary Strategy ◽  
Dice Coefficient ◽  
Normalized Mutual Information ◽  
Binary Images ◽  
Information Measures ◽  
Population Based Search ◽  
Two Stages

The paper presents a new memetic, cluster-based methodology for image registration in case of geometric perturbation model involving translation, rotation and scaling. The methodology consists of two stages. First, using the sets of the object pixels belonging to the target image and to the sensed image respectively, the boundaries of the search space are computed. Next, the registration mechanism residing in a hybridization between a version of firefly population-based search procedure and the two membered evolutionary strategy computed on clustered data is applied. In addition, a procedure designed to deal with the premature convergence problem is embedded. The fitness to be maximized by the memetic algorithm is defined by the Dice coefficient, a function implemented to evaluate the similarity between pairs of binary images. The proposed methodology is applied on both binary and monochrome images. In case of monochrome images, a preprocessing step aiming the binarization of the inputs is considered before the registration. The quality of the proposed approach is measured in terms of accuracy and efficiency. The success rate based on Dice coefficient, normalized mutual information measures, and signal-to-noise ratio are used to establish the accuracy of the obtained algorithm, while the efficiency is evaluated by the run time function.

Computational Intelligence in Circuit Synthesis

2007 ◽  
Vol 11 (9) ◽  
pp. 1122-1127 ◽  
Author(s):  
Cecília Reis ◽  
◽  
J. A. Tenreiro Machado
Keyword(s):  
Evolutionary Algorithms ◽  
Computational Intelligence ◽  
Search Algorithm ◽  
Fitness Function ◽  
Population Based ◽  
Statistical Characteristics ◽  
Combinational Logic Circuits ◽  
The Individual ◽  
Population Based Search ◽  
Swarm Intelligence Algorithm

This paper is devoted to the synthesis of combinational logic circuits through computational intelligence or, more precisely, using evolutionary computation techniques. Are studied two evolutionary algorithms, the Genetic and the Memetic Algorithm (GAs, MAs) and one swarm intelligence algorithm, the Particle Swarm Optimization (PSO). GAs are optimization and search techniques based on the principles of genetics and natural selection. MAs are evolutionary algorithms that include a stage of individual optimization as part of its search strategy, being the individual optimization in the form of a local search. The PSO is a population-based search algorithm that starts with a population of random solutions called particles. This paper presents the results for digital circuits design using the three above algorithms. The results show the statistical characteristics of this algorithms with respect to the number of generations required to achieve the solutions. The article analyzes also a new fitness function that includes an error discontinuity measure, which demonstrated to improve significantly the performance of the algorithm.

scholarly journals Reduction dimensionality of hyperspectral imagery using genetic algorithm and mutual information and normalized mutual information as a fitness function

2021 ◽  
Author(s):  
Merzouqi Maria ◽  
Sarhrouni El Kebir ◽  
Hammouch Ahmed
Keyword(s):  
Genetic Algorithm ◽  
Mutual Information ◽  
Fitness Function ◽  
Pearson Correlation ◽  
Hyperspectral Images ◽  
Normalized Mutual Information ◽  
Bhattacharyya Coefficient ◽  
Additional Information ◽  
Fitness Functions ◽  
Novel Method

AbstractHyperspectral images (HSI) present a wealth of information. It is distinguished by its high dimensionality. It served humanity in many fields. The quantity of HSI information represents a double-edged sword. As a consequence, their dimensionality must be reduced. Nowadays, several methods are proposed to overcome their duress. The most useful and essential solution is selection approaches of hyperspectral bands to analyze it quickly. Our work suggests a novel method to achieve this selection: we introduce a Genetic Algorithm (GA) based on mutual information (MI) and Normalized Mutual Information (NMI) as fitness functions. It selects the relevant bands from noisiest and redundant ones that don’t contain any additional information. .The proposed method is applied to three different HSI: INDIAN PINE, PAVIA, and SALINAS. The introduced algorithm provides a remarkable efficiency on the accuracy of the classification, in front of other statistical methods: the Bhattacharyya coefficient as well as the inter-bands correlation (Pearson correlation). We conclude that the measure of information (MI, NMI) provides more efficiency as a fitness function for GA selection applied to HSI; it must be more investigated.

scholarly journals Method for assessment of matching of total ankle joint endoprostheses based on CT data

2020 ◽  
Vol 6 (3) ◽  
pp. 396-397
Author(s):  
Heiner Martin ◽  
Josephine Wittmüß ◽  
Thomas Mittlmeier ◽  
Niels Grabow
Keyword(s):  
Cross Sections ◽  
Spline Functions ◽  
Moments Of Inertia ◽  
Cad System ◽  
Principal Moments Of Inertia ◽  
Principal Axes ◽  
Ct Data ◽  
The One ◽  
Relative Differences ◽  
Systemic Design

AbstractThe investigation of matching of endoprosthesis tibial components to the bone cross section is of interest for the manufacturer as well as for the surgeon. On the one hand, a systemic design of the prosthesis and the assortment is possible, on the other hand, a better matching implantation is enabled on the basis of experience of this study. CT sections were segmented manually using a CAD system and fitted by spline functions, then superseded with cross sections of the tibial component of a modified Hintermann H3 prosthesis. The principal moments of inertia, the direction of the principal axes and the area of the section were evaluated. Based on the relative differences of the principal moments of inertia, recommendations for application of the different prosthesis size and its selection with the surgery can be made.

scholarly journals Constructing Large-Scale Genetic Maps Using an Evolutionary Strategy Algorithm

Genetics ◽  
2003 ◽  
Vol 165 (4) ◽  
pp. 2269-2282
Author(s):  
D Mester ◽  
Y Ronin ◽  
D Minkov ◽  
E Nevo ◽  
A Korol
Keyword(s):  
Discrete Optimization ◽  
High Performance ◽  
Large Scale ◽  
Simulated Data ◽  
Real Data ◽  
Genetic Maps ◽  
Chromosome 1 ◽  
Evolutionary Strategy ◽  
Group A ◽  
The One

Abstract This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with ∼50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology.

Prostate Cancer: Population-Based Survival Rates in Central Italy

1995 ◽  
Vol 81 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Emanuele Crocetti ◽  
Eva Buiatti ◽  
Andrea Amorosi
Keyword(s):  
Prostate Cancer ◽  
Central Italy ◽  
Survival Rates ◽  
Relative Survival ◽  
Tumor Grade ◽  
Population Based ◽  
Prognostic Effect ◽  
Disease Extension ◽  
The One ◽  
Histological Verification

Aims To evaluate survival in prostate cancer patients in the Province of Florence where the Tuscany Cancer Registry is active. Methods The survival of 777 patients with prostate cancer diagnosed in the period 1985-87 was evaluated. The observed and relative survival rates 1, 3 and 5 years after diagnosis were computed. Also the prognostic effect of age, disease extension, tumor grade, histological verification, place of residence and year of diagnosis were evaluated using univariate and multivariate analysis. Results The observed survival was 73.4% 1 year, 42.5% 3 years and 29.2% 5 years after diagnosis. The relative survival was respectively 78.7%, 53.0% and 43.0%. Significant independent risks were evident when the disease was extended out of the prostate, for patients older than 80 years, for high grade tumors and for patients without histological verification. Conclusion The 5-year relative survival rate in the province of Florence is similar to those from other European Registries and the Latina Registry, but much lower than the one reported by the SEER program in the US. Data on histological verification percentage, availability of information on disease extension, and tumor grade are discussed as indicators of the quality of the diagnostic approach in comparison with other registries.

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