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Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Research Of Unsupervised Posture Modeling And Action Recognition Based On Spatial-Temporal Interesting Points, Chuan-Xu Wang, Yun Liu, Wanqing Li
Research Of Unsupervised Posture Modeling And Action Recognition Based On Spatial-Temporal Interesting Points, Chuan-Xu Wang, Yun Liu, Wanqing Li
Associate Professor Wanqing Li
Posture modeling is critical for action description and recognition,a posture modeling and action recognition method is proposed in this paper.Spatial Temporal Interesting Points (STIPs) are extracted from learning samples,in fact,one posture consists of a set of STIPs;a unsupervised clustering method is adopted to classify salient postures from these posture samples,then a GMM model is established for each clustering result;transitional probability among salient postures are calculated,and a Visible state Markov Model(VMM) is learnt to describe various actions.Bi-gram method is put forward for action recognition,Extensive experiments are conducted and the results prove its robustness and validity.
Modeling Mobile Learning System Using Anfis, Ahmed Al-Hmouz, Jun Shen, Jun Yan, Rami Al-Hmouz
Modeling Mobile Learning System Using Anfis, Ahmed Al-Hmouz, Jun Shen, Jun Yan, Rami Al-Hmouz
Dr Jun Yan
Personalisation is becoming more important in the area of mobile learning. Learner model is logically partitioned into smaller elements or classes in the form of learner profiles, which can represent the entire learning process. Machine learning techniques have the ability to detect patterns from complicated data and learn how to perform activities based on learner profiles. This paper focuses on a systematic approach in reasoning the learner contexts to deliver adaptive learning content. A fuzzy rule base model that has been proposed in related work is found insufficient in deciding all possible conditions. To tackle this problem, this paper adopts …
Modeling Mobile Learning System Using Anfis, Ahmed Al-Hmouz, Jun Shen, Jun Yan, Rami Al-Hmouz
Modeling Mobile Learning System Using Anfis, Ahmed Al-Hmouz, Jun Shen, Jun Yan, Rami Al-Hmouz
Dr Jun Shen
Personalisation is becoming more important in the area of mobile learning. Learner model is logically partitioned into smaller elements or classes in the form of learner profiles, which can represent the entire learning process. Machine learning techniques have the ability to detect patterns from complicated data and learn how to perform activities based on learner profiles. This paper focuses on a systematic approach in reasoning the learner contexts to deliver adaptive learning content. A fuzzy rule base model that has been proposed in related work is found insufficient in deciding all possible conditions. To tackle this problem, this paper adopts …
3d Geometric And Haptic Modeling Of Hand-Woven Textile Artifacts, Hooman Shidanshidi, Fazel Naghdy, Golshah Naghdy, Diana Wood Conroy
3d Geometric And Haptic Modeling Of Hand-Woven Textile Artifacts, Hooman Shidanshidi, Fazel Naghdy, Golshah Naghdy, Diana Wood Conroy
Associate Professor Golshah Naghdy
Haptic Modeling of textile has attracted significant interest over the last decade. In spite of extensive research, no generic system has been proposed. The previous work mainly assumes that textile has a 2D planar structure. They also require time-consuming objective measurement of textile propel1ies in mechanicaVphysjcal model constrUction. A novel approach for haptic modeling of textile is proposed to overcome the existing shortcomings. The method is generic, assumes a 3D structure textile artifact, and deploys computational intelligence to estimate textile mechanical and physical properties. The approach is designed primarily for display of textile artifacts in museums. The haptic model is …
Joint Modeling Of Additive And Non-Additive Genetic Line Effects In Single Field Trials, H Oakey, A Verbyla, Brian Cullis, W. Pitchford, H. Kuchel
Joint Modeling Of Additive And Non-Additive Genetic Line Effects In Single Field Trials, H Oakey, A Verbyla, Brian Cullis, W. Pitchford, H. Kuchel
Professor Brian Cullis
A statistical approach is presented for selection of best performing lines for commercial release and best parents for future breeding programs from standard agronomic trials. The method involves the partitioning of the genetic effect of a line into additive and non-additive effects using pedigree based inter-line relationships, in a similar manner to that used in animal breeding. A difference is the ability to estimate non-additive effects. Line performance can be assessed by an overall genetic line effect with greater accuracy than when ignoring pedigree information and the additive effects are predicted breeding values. A generalized definition of heritability is developed …
Joint Modeling Of Additive And Non-Additive (Genetic Line) Effects In Multi-Environment Trials, H Oakey, A Verbyla, Brian Cullis, X. Wei, W. Pitchford
Joint Modeling Of Additive And Non-Additive (Genetic Line) Effects In Multi-Environment Trials, H Oakey, A Verbyla, Brian Cullis, X. Wei, W. Pitchford
Professor Brian Cullis
A statistical approach for the analysis of multienvironment trials (METs) is presented, in which selection of best performing lines, best parents, and best combination of parents can be determined. The genetic effect of a line is partitioned into additive, dominance and residual nonadditive effects. The dominance effects are estimated through the incorporation of the dominance relationship matrix, which is presented under varying levels of inbreeding. A computationally efficient way of fitting dominance effects is presented which partitions dominance effects into between family dominance and within family dominance line effects. The overall approach is applicable to inbred lines, hybrid lines and …
Joint Modeling Of Spatial Variability And Within-Row Interplot Competition To Increase The Efficiency Of Plant Improvement, J. Stringer, Brian Cullis, R Thompson
Joint Modeling Of Spatial Variability And Within-Row Interplot Competition To Increase The Efficiency Of Plant Improvement, J. Stringer, Brian Cullis, R Thompson
Professor Brian Cullis
Trials in the early stages of selection are often subject to variation arising from spatial variability and interplot competition, which can seriously bias the assessment of varietal performance and reduce genetic progress. An approach to jointly model both sources of bias is presented. It models genotypic and residual competition and also global and extraneous spatial variation. Variety effects were considered random and residual maximum likelihood was used for parameter estimation. Competition at the residual level was examined using two special simultaneous autoregressive models. An equal-roots second-order autoregressive (EAR(2)) model is proposed for trials where competition is dominant. An equal-roots third-order …
A Gaussian-Rayleigh Mixture Modeling Approach For Through-The-Wall Radar Image Segmentation, Cher Hau Seng, Abdesselam Bouzerdoum, Moeness Amin, F Ahmad
A Gaussian-Rayleigh Mixture Modeling Approach For Through-The-Wall Radar Image Segmentation, Cher Hau Seng, Abdesselam Bouzerdoum, Moeness Amin, F Ahmad
Professor Salim Bouzerdoum
In this paper, we propose a Gausssian-Rayleigh mixture modeling approach to segment indoor radar images in urban sensing applications. The performance of the proposed method is evaluated on real 2D polarimetric data. Experimental results show that the proposed method enhances image quality by distinguishing between target and clutter regions. The proposed method is also compared to an existing Neyman-Pearson (NP) target detector that has been recently devised for through-the-wall radar imaging. Performance evaluation of both methods shows that the proposed method outperforms the NP detector in enhancing the input images.