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Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Qiang Bai, Shaobo Li, Jing Yang, Qisong Song, Zhiang Li, Xingxing Zhang
2020 IEEE Access  
[72] proposed a probabilistic framework for grasp modeling and stability assessment, which integrates supervised learning and unsupervised learning, and Bayesian networks are used to model the conditional  ...  By touching different objects, different pressure point cloud images are obtained and introduced into the neural network for training to realize object recognition and weight estimation without vision.  ... 
doi:10.1109/access.2020.3028740 fatcat:de4euqnb4ngsbclfob5izyeaxa

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
Li, J., +, TIP 2020 4461-4473 Multiple Cycle-in-Cycle Generative Adversarial Networks for Unsupervised Image Super-Resolution.  ...  ., +, TIP 2020 3612-3625 Learning Latent Global Network for Skeleton-Based Action Prediction. Learning Rich Part Hierarchies With Progressive Attention Networks for Fine-Grained Image Recognition.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Beyond Tracking: Modelling Activity and Understanding Behaviour

Tao Xiang, Shaogang Gong
2006 International Journal of Computer Vision  
In our approach, object-independent events are detected and classified by unsupervised clustering using Expectation-Maximisation (EM) and classified using automatic model selection based on Schwarz's Bayesian  ...  In this work, we present a unified bottom-up and top-down automatic model selection based approach for modelling complex activities of multiple objects in cluttered scenes.  ...  Acknowledgements We shall thank Huw Farmer and Mark Ealing at BAA for providing us with the aircraft cargo activity data under the DTI/EPSRC MI LINK project ICONS. Notes  ... 
doi:10.1007/s11263-006-4329-6 fatcat:jfg4mig2ureoxb5kbcocfvr5xm

Page 2896 of Mathematical Reviews Vol. , Issue 2003d [page]

2003 Mathematical Reviews  
, Bayesian approaches to unsupervised learning, and high level Bayesian image processing with a focus on shape analysis.  ...  Sastry, Adaptive stochastic algorithms for pattern classification (67-113); A. Pal, Unsupervised classifica- tion: some Bayesian approaches (115-145); K. V. Mardia, Shape in images (147-167); R.  ... 

Hierarchical Temporal Memory Network for Medical Image Processing

TAO ZHOU, ZHEN-ZHEN ZHANG, YUAN-YUAN CHEN
2018 DEStech Transactions on Computer Science and Engineering  
Medical image segmentation is a basic step in medical image analysis, especially for medical image sequences such as CT sequences.  ...  Secondly, create frames by animating gray images to train the HTM network. During the learning phase, the nodes in HTM network build its representations spatial pooler and temporal pooler for inputs.  ...  For object-recognition problem, only the feed-forward message propagation in network is considered.  ... 
doi:10.12783/dtcse/cmsms2018/25264 fatcat:2xjmkwaaxnfite7mhvscr52hgi

Data mapping by probabilistic modular networks and information-theoretic criteria

Yue Wang, Shang-Hung Lin, Huai Li, Sun-Yuan Kung
1998 IEEE Transactions on Signal Processing  
as an independent objective in real-world applications.  ...  Examples of the application of this framework to medical image quantification, automated face recognition, and featured database analysis, are presented as well.  ...  A probabilistic decision-based neural network (PDBNN) [6] is a probabilistic modular network designed especially for data classification where a Bayesian decomposition of the learning process provides  ... 
doi:10.1109/78.735311 fatcat:32mcfazlingbvhbqrwhwf6ubu4

Julia language in machine learning: Algorithms, applications, and open issues

Kaifeng Gao, Gang Mei, Francesco Piccialli, Salvatore Cuomo, Jingzhi Tu, Zenan Huo
2020 Computer Science Review  
Machine learning is driving development across many fields in science and engineering. A simple and efficient programming language could accelerate applications of machine learning in various fields.  ...  This paper summarizes the related research work and developments in the applications of the Julia language in machine learning.  ...  Project for Science and Technology (2020AA002).  ... 
doi:10.1016/j.cosrev.2020.100254 fatcat:gdt66djfvjfqpjou3lvemxsxfy

Video-understanding framework for automatic behavior recognition

François Brémond, Monique Thonnat, Marcos Zúñiga
2006 Behavior Research Methods  
A description of Bayesian networks for scenario recognition can be found in Moenne-Locoz, .  ...  The unsupervised behavior learning and recognition problem in the field of computer vision has been addressed in only a few works.  ... 
doi:10.3758/bf03192795 pmid:17186751 fatcat:3j3yyvjt65d43jro7gjhepvxfi

Julia Language in Machine Learning: Algorithms, Applications, and Open Issues [article]

Kaifeng Gao, Jingzhi Tu, Zenan Huo, Gang Mei, Francesco Piccialli, Salvatore Cuomo
2020 arXiv   pre-print
Machine learning is driving development across many fields in science and engineering. A simple and efficient programming language could accelerate applications of machine learning in various fields.  ...  This paper summarizes the related research work and developments in the application of the Julia language in machine learning.  ...  The Bayesian model is mainly used for image recognition and classification.There are some Bayesian model packages and algorithms developed in mature languages.  ... 
arXiv:2003.10146v1 fatcat:f2ocidpu4rchnokkc46qzrjgyu

Human-object-object-interaction affordance

Shaogang Ren, Yu Sun
2013 2013 IEEE Workshop on Robot Vision (WORV)  
The learned knowledge of the pair relationship is represented with a Bayesian Network and the trained network is used to improve recognition reliability of the objects. 978-1-4673-5647-3/12/$31.00 ©2012  ...  This paper presents a novel human-object-object (HOO) interaction affordance learning approach that models the interaction motions between paired objects in a humanobject-object way and use the motion  ...  The knowledge of object affordance is learned from labeled video sequences, and represented with a Bayesian Network.  ... 
doi:10.1109/worv.2013.6521912 fatcat:nmfoj5i7i5filhdp45ahxi4t54

Machine Learning Paradigms for Speech Recognition: An Overview

Li Deng, Xiao Li
2013 IEEE Transactions on Audio, Speech, and Language Processing  
sequence learning, Bayesian learning, and adaptive learning.  ...  ; and Bayesian learning.  ...  Jeff Bilmes for contributions during the early phase (2010) of developing this paper, and for valuable discussions with Geoff Hinton, John Platt, Mark Gales, Nelson Morgan, Hynek Hermansky, Alex Acero,  ... 
doi:10.1109/tasl.2013.2244083 fatcat:fv4qulshnrh4fgzmzb45mkqwmq

2020 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 42

2021 IEEE Transactions on Pattern Analysis and Machine Intelligence  
., Recognizing Material Properties from Images; 1981-1995 Sebe, N., see Pilzer, A., 2380-2395 Seddik, M., see Tamaazousti, Y., 2212-2224 Shah, M., see Kalayeh, M.M., TPAMI June 2020 1483-1500  ...  Deep Imbalanced Learning for Face Recognition and Attribute Prediction. Huang, C., +, TPAMI Nov. 2020 2781-2794 Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning.  ...  ., +, TPAMI April 2020 780-792 Entropy Context-Aware Query Selection for Active Learning in Event Recognition.  ... 
doi:10.1109/tpami.2020.3036557 fatcat:3j6s2l53x5eqxnlsptsgbjeebe

Guest editors' introduction to the special section on graphical models in computer vision

J.M. Rehg, V. Pavlovic, T.S. Huang, W.T. Freeman
2003 IEEE Transactions on Pattern Analysis and Machine Intelligence  
ae T HE last 10 years have witnessed rapid growth in the popularity of graphical models, most notably Bayesian networks, as a tool for representing, learning, and computing complex probability distributions  ...  The paper described the use of Bayesian inference in a hierarchical probability model to match 3D object models to groupings of curves in a single image. The following year marked the  ...  Perona describe, in their paper "Unsupervised Learning of Human Motion," a method for learning probabilistic models of human motion from video sequences in cluttered scenes.  ... 
doi:10.1109/tpami.2003.1206508 fatcat:gdvjd3vsgjgizc6lmcaueumpdm

Object recognition and performance bounds [chapter]

J. K. Aggarwal, Shishir Shah
1997 Lecture Notes in Computer Science  
In addition, it may involve the estimation of the pose of tile object and/or the track of the object in a sequence of images.  ...  Bayesian statistical pattern recognition, neural networks and rule based syst~ems have been used to address the object recognition problem.  ...  Learning in a neural network is usually performed using two distinct techniques: supervised and unsupervised.  ... 
doi:10.1007/3-540-63507-6_220 fatcat:phbkd4jtdbdyjcxieissdvmdhu

A self-organizing neural network architecture for learning human-object interactions [article]

Luiza Mici, German I. Parisi, Stefan Wermter
2018 arXiv   pre-print
In this paper, we present a self-organizing neural network for the recognition of human-object interactions from RGB-D videos.  ...  in an unsupervised fashion.  ...  For this experiment, we artificially created a test dataset, for which we replaced the image of the object being manipulated in each video sequence with the image of an incongruent object extracted from  ... 
arXiv:1710.01916v2 fatcat:eu7c7wn3anfx5hjzabufbrrdvq
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