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Conference

International Conference on Control, Automation, Robotics and Vision 

About: International Conference on Control, Automation, Robotics and Vision is an academic conference. The conference publishes majorly in the area(s): Control theory & Mobile robot. Over the lifetime, 3668 publications have been published by the conference receiving 23905 citations.


Papers
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Proceedings ArticleDOI
01 Jan 2014
TL;DR: A customized Convolutional Neural Networks with shallow convolution layer to classify lung image patches with interstitial lung disease and the same architecture can be generalized to perform other medical image or texture classification tasks.
Abstract: Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.

551 citations

Proceedings ArticleDOI
06 Dec 2004
TL;DR: This paper shows that ELM can be extended to radial basis function (RBF) network case, which allows the centers and impact widths of RBF kernels to be randomly generated and the output weights to be simply analytically calculated instead of iteratively tuned.
Abstract: A new learning algorithm called extreme learning machine (ELM) has recently been proposed for single-hidden layer feedforward neural networks (SLFNs) to easily achieve good generalization performance at extremely fast learning speed. ELM randomly chooses the input weights and analytically determines the output weights of SLFNs. This paper shows that ELM can be extended to radial basis function (RBF) network case, which allows the centers and impact widths of RBF kernels to be randomly generated and the output weights to be simply analytically calculated instead of iteratively tuned. Interestingly, the experimental results show that the ELM algorithm for RBF networks can complete learning at extremely fast speed and produce generalization performance very close to that of SVM in many artificial and real benchmarking function approximation and classification problems. Since ELM does not require validation and human-intervened parameters for given network architectures, ELM can be easily used.

263 citations

Proceedings ArticleDOI
01 Dec 2008
TL;DR: A set of methods for building informative and robust feature point representations, used for accurately labeling points in a 3D point cloud, based on the type of surface the point is lying on, are proposed.
Abstract: This paper proposes a set of methods for building informative and robust feature point representations, used for accurately labeling points in a 3D point cloud, based on the type of surface the point is lying on. The feature space comprises a multi-value histogram which characterizes the local geometry around a query point, is pose and sampling density invariant, and can cope well with noisy sensor data. We characterize 3D geometric primitives of interest and describe methods for obtaining discriminating features used in a machine learning algorithm. To validate our approach, we perform an in-depth analysis using different classifiers and show results with both synthetically generated datasets and real-world scans.

229 citations

Proceedings ArticleDOI
01 Dec 2006
TL;DR: An integrated survey of the field of multi-agent learning is presented, in which the issue of the multi- agent learning goal is discussed and a representative selection of algorithms is reviewed.
Abstract: Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, economics. Many tasks arising in these domains require that the agents learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. However, due to different viewpoints on central issues, such as the formal statement of the learning goal, a large number of different methods and approaches have been introduced. In this paper we aim to present an integrated survey of the field. First, the issue of the multi-agent learning goal is discussed, after which a representative selection of algorithms is reviewed. Finally, open issues are identified and future research directions are outlined

118 citations

Proceedings ArticleDOI
01 Nov 2016
TL;DR: A novel model-based reinforcement learning algorithm, TEXPLORE, is developed as a high level control method for autonomous navigation of UAVs and shows that it significantly outperforms Q-learning based method.
Abstract: Autonomous navigation in an unknown or uncertain environment is one of the challenging tasks for unmanned aerial vehicles (UAVs). In order to address this challenge, it is necessary to have sophisticated high level control methods that can learn and adapt themselves to changing conditions. One of the most promising frameworks for such a purpose is reinforcement learning. In this paper, a novel model-based reinforcement learning algorithm, TEXPLORE, is developed as a high level control method for autonomous navigation of UAVs. The developed approach has been extensively tested with a quadcopter UAV in ROS-Gazebo environment. The experimental results show that our method is able to learn an efficient trajectory in a few iterations and perform actions in real-time. Moreover, we show that our approach significantly outperforms Q-learning based method. To the best of our knowledge, this is the first time that TEXPLORE has been developed to achieve autonomous navigation of UAVs.

110 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
2022158
2020219
2018339
2016258
2014344
2012316