Journal ArticleDOI
A robust modified Gaussian mixture model with rough set for image segmentation
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TLDR
A robust modified Gaussian mixture model combining with rough set theory is proposed for image segmentation that is more robust to noise, and a novel prior factor is proposed by incorporating the spatial information amongst neighborhood pixels.About:
This article is published in Neurocomputing.The article was published on 2017-11-29. It has received 30 citations till now. The article focuses on the topics: Scale-space segmentation & Image segmentation.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning
TL;DR: The results show that semi-supervised learning is a promising approach for the automatic certification of AM builds that can be implemented at a fraction of the cost currently required.
Journal ArticleDOI
A review on classifying abnormal behavior in crowd scene
A. A. Afiq,Mohd Azman Zakariya,Mohamad Naufal Mohamad Saad,A. A. Nurfarzana,M. H. Md Khir,A. F. Fadzil,A. Jale,W. Gunawan,Z. A. A. Izuddin,M. Faizari +9 more
TL;DR: A review of crowd behavior analysis methods including Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), Optical Flow method and Spatio-Temporal Technique (STT) to provide insight on several detection methods.
Journal ArticleDOI
Fault diagnosis of VRF air-conditioning system based on improved Gaussian mixture model with PCA approach
Yabin Guo,Huanxin Chen +1 more
TL;DR: The fault diagnosis model combining the Gaussian mixture model and principal component analysis is established, which is evaluated using the four types of faults of the variable refrigerant flow system and shows that the PCA-GMM approach can effectively reduce the running time.
Journal ArticleDOI
Unsupervised change detection using fast fuzzy clustering for landslide mapping from very high-resolution images
TL;DR: The experiments show that the proposed CDFFCM is superior to three comparative approaches in terms of higher accuracy, fewer parameters, and shorter execution time.
References
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Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
BookDOI
Finite mixture models: McLachlan/finite mixture models
Geoffrey J. McLachlan,David Peel +1 more
TL;DR: The important role of finite mixture models in statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the statistical and geospatial literature.
Book
Finite Mixture Models
Geoffrey J. McLachlan,David Peel +1 more
TL;DR: The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the mathematical and statistical literature.