In this paper, the authors describe the system they have implemented which automatically determines an optimized next range sensor position and orientation during the reconstruction of a three-dimensional model. The s...
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In this paper, the authors describe the system they have implemented which automatically determines an optimized next range sensor position and orientation during the reconstruction of a three-dimensional model. The system they have developed reconstructs a model consisting of surfaces which have been viewed and volumes occluded from the camera's view. Ideally, a sensor pose determined by a "best-next-view" system will reveal the greatest quantity of previously unknown scene information. We will present results from the most intelligent of the algorithms we have developed. This algorithm attempts to intelligently cluster the occluded data and orient the sensor on the centroid of the largest cluster. A system which finds a solution to the best-next-view problem may find application in the contexts of robot navigation, manufacturing and hazardous materials handling. The methods we implement take advantage of no a priori information in finding the best-next-view position.
In this paper, the authors study the use of an unsupervised neural network to perform the inspection of IC leadframes. The network used is the learning by experience (LBE) type. They show the steps of varying the tole...
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In this paper, the authors study the use of an unsupervised neural network to perform the inspection of IC leadframes. The network used is the learning by experience (LBE) type. They show the steps of varying the tolerance of acceptance whenever the network envisages some parts which cannot be classified. The Euclidean distance is used as the similarity measure. Here, two different types of unsupervised neural networks, namely adaptive resonance theory (ART2) and LBE, are compared. Experimental results on the classification of some patterns in a leadframe are also included.
Vision-based navigation is the primary motivation for the research effort narrated in this paper. In order to achieve reactive and reflexive motion for navigation, a robot must be equipped with a sensory system capabl...
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Vision-based navigation is the primary motivation for the research effort narrated in this paper. In order to achieve reactive and reflexive motion for navigation, a robot must be equipped with a sensory system capable of performing rapid analysis of sensory data such that the controller is fed with current information about the environment instantly. The information acquired need to be segmented into appropriate divisions and the segmented information need to be recognized properly. The authors' architecture for such a vision system separates these two major tasks-segmenting image information and recognizing the segmented information. There are two systems presented-one for region transmission in the global image, and the other for a neural network-based learning mechanism.
For realizing visual feedback control, vision chips are promising. However early-vision-like processing is not sufficient because its output is pattern information which cannot be directly used in actuator control. We...
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For realizing visual feedback control, vision chips are promising. However early-vision-like processing is not sufficient because its output is pattern information which cannot be directly used in actuator control. We propose a two-dimensional resistive network for extracting the zeroth- and first-order moments of two-dimensional coordinates in parallel. The network can detect the moments of nonlinear coordinates, so that it can be used to directly detect an object position not only in an imager coordinate system but also in a working coordinate system, or used for foveated vision. Through its use, a high-speed smart sensor for visual feedback is realized.
The success of CMAC (cerebellar model articulation control) for real-time dynamic manipulator control has been exploited by Miller etc. Fundamentally, this kind of neural network deals with discretized state variables...
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ISBN:
(纸本)0780327756
The success of CMAC (cerebellar model articulation control) for real-time dynamic manipulator control has been exploited by Miller etc. Fundamentally, this kind of neural network deals with discretized state variables and needs relatively large memory to store data. Impressed with the backpropagation's potential in learning complicated nonlinear mappings, Xu, proposed LBP (localized backpropagation network), which organizes many localized BP subnets into a whole network. In this paper, we discuss its principal further and the practicability of this kind of network in real-time robot manipulator control is also investigated. Simulation results prove this neural network's architecture has good prospects for applications.
This book constitutes the proceedings of the 22ndinternationalconference on Software engineering Research and Practice, SERP 2024, and the 23rd internationalconference on e-learning, e-Business, Enterprise Informat...
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ISBN:
(数字)9783031866449
ISBN:
(纸本)9783031866432
This book constitutes the proceedings of the 22ndinternationalconference on Software engineering Research and Practice, SERP 2024, and the 23rd internationalconference on e-learning, e-Business, Enterprise Information Systems, and e-Government, EEE 2024, held as part of the 2024 World Congress in Computer Science, Computer engineering and Applied Computing, in Las Vegas, USA, during July 22 to July 25, 2024.
For SERP 2024, 52 submissions have been received and 9 papers have been accepted for publication in these proceedings; the 12 papers included from EEE 2024 have been carefully reviewed and selected from 55 submissions. They have been organized in topical sections as follows: software engineering research and practice; e-learning, e-business, enterprise information systems and e-government.
This book constitutes the refereed proceedings of the 14th internationalconference on Discovery Science, DS 2011, held in Espoo, Finland, in October 2011 - co-located with ALT 2011, the 22ndinternationalconference ...
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ISBN:
(数字)9783642244773
ISBN:
(纸本)9783642244766
This book constitutes the refereed proceedings of the 14th internationalconference on Discovery Science, DS 2011, held in Espoo, Finland, in October 2011 - co-located with ALT 2011, the 22ndinternationalconference on Algorithmic learning Theory. The 24 revised full papers presented together with 5 invited lectures were carefully revised and selected from 56 submissions. The papers cover a wide range including the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligentdata analysis, theory of learning, as well as their application to knowledge discovery.
The two-volume set LNCS 9516 and 9517 constitutes the thoroughly refereed proceedings of the 22ndinternationalconference on Multimedia Modeling, MMM 2016, held in Miami, FL, USA, in January 2016.;The ...
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ISBN:
(数字)9783319276748
ISBN:
(纸本)9783319276731
The two-volume set LNCS 9516 and 9517 constitutes the thoroughly refereed proceedings of the 22ndinternationalconference on Multimedia Modeling, MMM 2016, held in Miami, FL, USA, in January 2016.;The 32 revised full papers and 52 poster papers were carefully reviewed and selected from 117 submissions. In addition 20 papers were accepted for five special sessions out of 38 submissions as well as 7 demonstrations (from 11 submissions) and 9 video showcase papers.;The papers are organized in topical sections on video content analysis, social media analysis, object recognition and system, multimedia retrieval and ranking, multimedia representation, machine learning in multimedia, and interaction and mobile. The special sessions are: good practices in multimedia modeling; semantics discovery from multimedia big data; perception, aesthetics, and emotion in multimedia quality modeling; multimodal learning and computing for human activity understanding; and perspectives on multimedia analytics.
This book constitutes the proceedings of the 22ndinternational Semantic Web conference, ISWC 2023, which took place in October 2023 in Athens, Greece.;The 58 full papers presented in this double volume were thoroughl...
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ISBN:
(数字)9783031472404
ISBN:
(纸本)9783031472398
This book constitutes the proceedings of the 22ndinternational Semantic Web conference, ISWC 2023, which took place in October 2023 in Athens, Greece.;The 58 full papers presented in this double volume were thoroughly reviewed and selected from 248 submissions. Many submissions focused on the use of reasoning and query answering, witha number addressing engineering, maintenance, and alignment tasks for ontologies. Likewise, there has been a healthy batch of submissions on search, query, integration, and the analysis of knowledge. Finally, following the growing interest in neuro-symbolic approaches, there has been a rise in the number of studies that focus on the use of Large Language Models and Deep learning techniques such as Graph Neural Networks.
This book constitutes the proceedings of the 22ndinternational Semantic Web conference, ISWC 2023, which took place in October 2023 in Athens, Greece.;The 58 full papers presented in this double volume were thoroughl...
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ISBN:
(数字)9783031472435
ISBN:
(纸本)9783031472428
This book constitutes the proceedings of the 22ndinternational Semantic Web conference, ISWC 2023, which took place in October 2023 in Athens, Greece.;The 58 full papers presented in this double volume were thoroughly reviewed and selected from 248 submissions. Many submissions focused on the use of reasoning and query answering, witha number addressing engineering, maintenance, and alignment tasks for ontologies. Likewise, there has been a healthy batch of submissions on search, query, integration, and the analysis of knowledge. Finally, following the growing interest in neuro-symbolic approaches, there has been a rise in the number of studies that focus on the use of Large Language Models and Deep learning techniques such as Graph Neural Networks.
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