Search Results - (( motion estimation method algorithm ) OR ( swarm optimization _ algorithm ))*
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1
Hexagon pattern particle swarm optimization based block matching algorithm for motion estimation / Siti Eshah Che Osman
Published 2019“…Block Matching Algorithm (BMA) is a technique used to minimize the computational complexity of motion estimation in video coding application. …”
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2
SLOW DRIFT MOTIONS IDENTIFICATION OF FLOATING STRUCTURES USING TIME-VARYING INPUT -OUTPUT MODELS
Published 2015“…The first step is presenting the backward estimator and combined forward-backward estimator instead of the only forward estimator in the original input-output models; the second step is reformulating the input-output models into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the model coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Bee Colony (ABC) to form the PSO-KS, GA-KS and ABC-KS as estimation methods.…”
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3
An Adaptive Motion Estimation Algorithm for Irregular Motion of Objects
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4
ADAPTIVE MOTION ESTIMATION ALGORITHM FOR CROWDED SCENES
Published 2015“…Lots of crowd motion estimation algorithms have been presented in the last years comprising pedestrian motion. …”
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5
Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence
Published 2011“…Block-matching algorithm is the most common technique applied in block-based motion estimation technique. …”
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Book Chapter -
6
Fast adaptive motion estimation search algorithm for H.264 encoder
Published 2012“…Simulation result shows that as compared to previous research, this algorithm achieves up to average 60% reduction in motion estimation time without degrading the video quality.…”
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7
Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique
Published 2015“…There are many types of motion estimation method but the most used method is the block matching method which is the fixed block matching and the variable block matching. …”
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8
New Fast Block Matching Algorithm Using New Hybrid Search Pattern And Strategy To Improve Motion Estimation Process In Video Coding Technique
Published 2016“…These 6 algorithms are divided into 3 main methods namely Method A, Method B, and Method C depending on their search patterns and strategies. …”
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9
Normative Fish Swarm Algorithm For Global Optimization With Applications
Published 2019“…Referred to as Normative Fish Swarm Algorithm (NFSA), the proposed Fish Swarm Algorithm, Optimized by Particle Swarm Optimization with Extended Memory (PSOEM-FSA) is expanded by amalgamating the normative knowledge to provide supplementary guidelines for better global optimum achievement and convergence rate. …”
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10
An adaptive motion estimation algorithm for irregular motion of objects
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Conference or Workshop Item -
11
Empirical Study of Segment Particle Swarm Optimization and Particle Swarm Optimization Algorithms
Published 2019“…In this paper, the performance of segment particle swarm optimization (Se-PSO) algorithm was compared with that of original particle swarm optimization (PSO) algorithm. …”
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12
Improving Ant Swarm Optimization With Embedded Vaccination For Optimum Reducts Generation
Published 2011“…Ant Swarm Optimization refers to the hybridization of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms to enhance optimization performance. …”
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13
An adaptive block-based matching algorithm for crowd motion sequences
Published 2018“…For crowd analytics and surveillance systems, motion estimation is an essential first step. Lots of crowd motion estimation algorithms have been presented in the last years comprising pedestrian motion. …”
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14
An adaptive block-based matching algorithm for crowd motion sequences
Published 2018“…For crowd analytics and surveillance systems, motion estimation is an essential first step. Lots of crowd motion estimation algorithms have been presented in the last years comprising pedestrian motion. …”
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15
HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION
Published 2023“…In this paper, we study the advantages of fusing the Meta-Heuristic Bat Algorithm with Heuristic Optimization. We have implemented the Meta- Heuristic Bat Algorithm and tested it on a heterogeneous swarm. …”
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Enhanced Particle Swarm Optimization Algorithms With Robust Learning Strategy For Global Optimization
Published 2014“…Particle Swarm Optimization (PSO) is a metaheuristic search (MS) algorithm inspired by the social interactions of bird flocking or fish schooling in searching for food sources. …”
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17
Multi-State Particle Swarm Optimization for Discrete Combinatorial Optimization Problem
Published 2014“…The binary-based algorithms including the binary particle swarm optimization (BPSO) algorithm are proposed to solve discrete optimization problems. …”
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18
Improved particle swarm optimization by fast annealing algorithm
Published 2019“…The proposed algorithm is meant to solve high dimensional optimization problems based on two strategies, which are utilizing the particle swarm optimization to define the global search area and utilizing the fast-simulated annealing to refine the visited search area. …”
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Proceeding Paper -
19
Improving robot Darwinian particle swarm optimization using quantum-behaved swarm theory for robot exploration and communication / Duaa Mehiar
Published 2021“…Despite the significance of the Robotic Darwinian Particle Swarm Optimization (RDPSO) algorithm on swarm-robot exploration and communication, there remain notable gaps such as premature and slow convergence, collisions between robots, and communication breaks and constraints. …”
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20
Comparison of swarm intelligence algorithms for high dimensional optimization problems
Published 2018“…This paper presents a comprehensive study of two swarm intelligence based algorithms: 1- particle swarm optimization (PSO), 2-cuckoo search (CS).The two algorithms are analyzed and compared for problems consisting of high dimensions in respect of solution accuracy, and runtime performance by various classes of benchmark functions.…”
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