Search Results - (( motion estimation based algorithm ) OR ( label classification new algorithm ))
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1
Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence
Published 2011“…There are several block-matching algorithm based on block-based motion estimation techniques have been developed. …”
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2
Multi-label learning based on positive label correlations using predictive apriori
Published 2019“…Multi-label Learning (MLL) is a general task in data mining that consists of three main tasks: classification, label ranking, and multi-label ranking. …”
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3
Multi label ranking based on positive pairwise correlations among labels
Published 2020“…The first objective is to propose a new multi-label ranking algorithm based on the positive pairwise correlations among labels, while the second objective aims to propose new simple PTMs that are based on labels correlations, and not based on labels frequency as in conventional PTMs. …”
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4
An Adaptive Motion Estimation Algorithm for Irregular Motion of Objects
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5
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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6
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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7
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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8
Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique
Published 2015“…Motion estimation is a technique to reduce high information redundancy which exists between successive frames in a video sequences. …”
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9
Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…Here, there is a collection of classes with labels and the problem is to label a new observation or data point belonging to one or more classes of data. …”
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10
Content adaptive fast motion estimation based on spatio-temporal homogeneity analysis and motion classification
Published 2012“…In video coding, research is focused on the development of fast motion estimation (ME) algorithms while keeping the coding distortion as small as possible. …”
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11
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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12
Enhancing Classification Algorithms with Metaheuristic Technique
Published 2024“…For this reason, in this research,several auxiliary algorithms are introduced to improve the performance of the classification algorithm, namely the meta-heuristic algorithm. …”
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13
An adaptive motion estimation algorithm for irregular motion of objects
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14
Fast directional search algorithm for fractional pixel motion estimation for H.264 AVC
Published 2013“…Fractional pixel motion estimation (ME) is required to achieve more accurate motion vectors and higher compression efficiency. …”
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Citation Index Journal -
15
New Fast Block Matching Algorithm Using New Hybrid Search Pattern And Strategy To Improve Motion Estimation Process In Video Coding Technique
Published 2016“…Motion Estimation or ME is deemed as one of the effective and popular techniques in video compression. …”
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16
Multi-label risk diabetes complication prediction model using deep neural network with multi-channel weighted dropout
Published 2025“…Seven machine learning models—Artificial Neural Network (ANN), Random Forest (RF), Decision Tree (DTT), k-Nearest Neighbors (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM), and Deep Neural Network (DNN)—were used for multi-label classification of the complications. The study employed two MLC frameworks: Problem Transformation methods (Binary Relevance, Classifier Chains, Label Power Set, and Calibrated Label Ranking) and Algorithm Adaptation. …”
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17
Multilevel optimization for dense motion estimation
Published 2011“…This research has been oriented towards the design of a new technique for fast and reliable dense motion estimation. We used variational models of optical flow computation to estimate the dense motion in a sequence of images.We have been interested in developing a multilevel optimization solver to produce accurate optical flow estimation for real-time applications.To the best of our knowledge, two-ways multilevel optimization techniques are used for the first time in the context of a computer vision problem. …”
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18
Upper Limb Joint Angle Estimation Using Wearable IMUs and Personalized Calibration Algorithm
Published 2023“…Multiple trials using the proposed algorithm estimated the elbow joint angle of the rigid body with a maximum RMSE of 0.46◦ Using the IMU manufacturer’s (WitMotion) algorithm (Kalman filter), the maximum RMSE was 1.97◦ For the fourth trial, joint angles were also calculated with external acceleration, and the RMSE was 0.996◦ In all cases, the joint angles were within therapeutic limits.…”
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Upper Limb Joint Angle Estimation Using Wearable IMUs and Personalized Calibration Algorithm
Published 2023“…Multiple trials using the proposed algorithm estimated the elbow joint angle of the rigid body with a maximum RMSE of 0.46◦ Using the IMU manufacturer’s (WitMotion) algorithm (Kalman filter), the maximum RMSE was 1.97◦ For the fourth trial, joint angles were also calculated with external acceleration, and the RMSE was 0.996◦ In all cases, the joint angles were within therapeutic limits.…”
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20
Robust remote heart rate estimation from multiple asynchronous noisy channels using autoregressive model with Kalman filter
Published 2019“…Moreover, we investigate the behavior of our algorithm under challenging conditions including the subject's motions and illumination variation, which shows that our algorithm can reduce the influences of illumination interference and rigid motions significantly. …”
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