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检索条件"任意字段=Conference on Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques"

共 375 条记录，以下是321-330 订阅

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Indoor Scene Recognition based on Weighted Voting Schemes

Indoor Scene Recognition based on Weighted Voting Schemes

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European conference on Mobile robots (ECMR)

作者： Hernandez, Alejandra C. Gomez, Clara Derner, Erik Barber, Ramon Carlos III Univ Madrid Dept Syst Engn & Automat Robot Lab Madrid Spain Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Czech Tech Univ Fac Elect Engn Dept Control Engn Prague Czech Republic

ISBN: (纸本)9781728136059

Scene understanding represents one of the most primary problems in computer vision. It implies the full knowledge of all the elements of the environment and the comprehension of the relationships between them. One of the major tasks in this process is the scene recognition, on which we focus in this work. Scene recognition is a relevant and helpful task in many robotic fields such as navigation, localization, manipulation, among others. The knowledge of the place (e.g. "office", "classroom" or "kitchen") can improve the performance of robots in indoor environments. This task can be difficult because of the variability, ambiguity, illumination changes, occlusions and scale variability present in this type of spaces. Commonly, this problem has been approached through the development of models based on local and global characteristics, incorporating context information and, more recently, using deep learning techniques. In this paper, we propose a multi-classifier model for scene recognition considering as priors the outcomes of independent base classifiers. We implement a weighted voting scheme based on genetic algorithms for the combination of different classifiers in order to improve the recognition performance. The results have proved the validity of our approach and how the proper combination of independent classifier models makes it possible to find a better and more efficient solution for the scene recognition problem.

关键词： Scene recognition multi-classification scene understanding mobile robots genetic algorithms

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intelligent robots and computer vision XXXIII: algorithms and techniques

IS and T International Symposium on Electronic Imaging Scien...

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IS and T International Symposium on Electronic Imaging Science and Technology 2016年

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The 23RD annual intelligent ground vehicle competition: Building engineering students into robotists 33

The 23RD annual intelligent ground vehicle competition: Buil...

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33rd intelligent robots and computer vision: algorithms and techniques conference

作者： Theisen, Bernard L. U.S. Army TARDEC 6501 E. Eleven Mile Road WarrenMl48397-5000 United States

The intelligent Ground Vehicle Competition (IGVC) is one of four, unmanned systems, student competitions that1were founded by the Association for Unmanned Vehicle Systems International (AUVSI). The IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics and mobile platform fundamentals to design and build an unmanned system. Teams from around the world fonts on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 23 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from over SO universities and colleges have participated. This paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. The primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the four-day competition are highlighted. Finally, an assessment of the competition based on participation is presented. © 2016 Society for Imaging Science and Technology.

关键词： Students

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7th Mexican conference on Pattern Recognition, MCPR 2015

7th Mexican Conference on Pattern Recognition, MCPR 2015

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7th Mexican conference on Pattern Recognition, MCPR 2015

ISBN: (纸本)9783319192635

The proceedings contain 34 papers. The special focus in this conference is on Pattern Recognition, Artificial intelligent techniques, Image Processing, Robotics and computer vision. The topics include: Recommendation of process discovery algorithms through event log classification;a new method based on graph transformation for FAS mining in multi-graph collections;classification of hand movements from non-invasive brain signals using lattice neural networks with dendritic processing;a different approach for pruning micro-clusters in data stream clustering;computing constructs by using typical testor algorithms;multitask reinforcement learning in nondeterministic environments;prototype selection for graph embedding using instance selection;correlation of resampling methods for contrast pattern based classifiers;boosting the permutation based index for proximity searching;similarity analysis of archaeological potsherds using 3D surfaces;automatic detection of clouds from aerial photographs of snowy volcanoes;a comparative study of robust segmentation algorithms for iris verification system of high reliability;vision-based humanoid robot navigation in a featureless environment;evaluation of local descriptors for vision-based localization of humanoid robots;sampled weighted min-hashing for large-scale topic mining;patterns used to identify relations in corpus using formal concept analysis;improving information retrieval through a global term weighting scheme;sentiment groups as features of a classification model using a Spanish sentiment lexicon;modified binary inertial particle swarm optimization for gene selection in DNA microarray data;encoding polysomnographic signals into spike firing rate for sleep staging and patrolling routes optimization using ant colonies.

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Method searching an optimal lighting: Application to defect detection by artificial vision in industrial field

Method searching an optimal lighting: Application to defect ...

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Proceedings of the 1998 conference on intelligent robots and computer vision XVII: algorithms, techniques, and Active vision

作者： Geveaux, P. Kohler, S. Miteran, J. Truchetet, F. Le2i Lab-Universite de Bourgogne Le Creusot France

Quality control in industrial application has greatly benefited from the development of tools like artificial vision. In order to obtain a good quality image of the object under investigation, the first step is to use a good lighting system. This paper presents a reliable method which allows to compare several lighting with respect to their capabilities of bringing out defects. This study has been led on textured industrial parts on which four types of defects have to be detected: smooth surfaces, bumps, lacks of material and hollows knocked surfaces. The aim is to determine the best lighting among various experimental sets. This method has two stages: the first one is a definition, according to the knowledge and the shape of the defects, of a pertinent attribute vector which components are defect sensitive. In the second step, discrimination power property of this vector is computed and compared under various illumination using Parzen's kernel. This method insures a well-suited illumination in numerous applications in defects detection and leads to an efficient set of lighting system and segmentation's parameters. Work is under way to generalize this method to multidimensional cases in order to allow interactions between components of the attribute vector.

关键词： computer vision

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Industrial 5.0 Aquaponics System Using Machine Learning techniques 7

Industrial 5.0 Aquaponics System Using Machine Learning Tech...

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7th International conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Venkatraman, M. Surendran, R. Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Science Chennai India

ISBN: (纸本)9798350340600

Due to the advancements in digital technology, the fifth industrial revolution, also known as aquaponics, has brought changes in the traditional manufacturing and industrial processes. The primary objective of the industry 5.0 initiative is to encourage customization and innovation while simultaneously increasing the productivity, efficiency, and adaptability of manufacturing processes. The industry's coming to terms with the 5.0 vision is underpinned by machine learning. It makes it possible for machines and systems to see patterns in data, make decisions based on that data, and learn without having to be explicitly programmed. algorithms that learn from machines are able to analyze vast amounts of data collected from sensors, machines, and other sources in order to make predictions, improve processes, and acquire insights. This capacity has applications in a variety of domains, including quality control, predictive maintenance, supply chain optimization, process optimization, and autonomous robots. Machine learning-based predictive maintenance reduces downtime and optimizes maintenance schedules by detecting industrial equipment faults. Machine learning-powered quality control systems monitor manufacturing, identify errors, and ensure product quality. U sing machine learning algorithms on supply chain data improves inventory management, logistics, and efficiency. Process optimization uses machine learning to find trends in production data to improve efficiency and energy use. Machine learning-enabled autonomous robots and intelligent decision- making systems help humans complete complicated tasks safely and efficiently. Aquaponics is a major step toward a more connected, intelligent, and data-driven industrial landscape. © 2023 IEEE.

关键词： Decision making

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Navigating a Path Delineated by Colored Flags: An Approach for a 2011 IGVC Requirement

Navigating a Path Delineated by Colored Flags: An Approach f...

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conference on intelligent robots and computer vision XXIX - algorithms and techniques

作者： Szmatula, Alex Parrish, Matt Krishnan, Mohan Paulik, Mark Mohammad, Utayba Luo, Chaomin Univ Detroit Mercy ECE Dept Detroit MI 48221 USA

ISBN: (纸本)9780819489487

In this work, we address a situation presented as a new requirement for the Autonomous Challenge portion of the 2011 intelligent Ground Vehicle Competition (IGVC). This new requirement is to navigate between red and green colored flags placed within the normal white painted lane lines. The regular vision algorithms had to be enhanced to reliably identify and localize the colored flags, while the Navigation algorithms had to be modified to satisfy the constraints placed on the robot while transiting through the flag region. The challenge in finding a solution was the size of the flags, the possibility of loosing them against the background, as well as their movement in the wind. The attendant possibility of false positives and negatives also needed to be addressed to increase reliability of detection. Preliminary tests on the robot have produced positive results.

关键词： Autonomous robots Color Image Processing VFH Navigation Lane Following IGVC

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vision-based approach for learning an elementary navigation behavior

Vision-based approach for learning an elementary navigation ...

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Proceedings of the 1998 conference on intelligent robots and computer vision XVII: algorithms, techniques, and Active vision

作者： D'Orazio, T. Cicirelli, G. Distante, C. Istituto Elaborazione Segnali ed Immagini - C.N.R. Bari Italy

Developing elementary behavior is the starting point for the realization of complex systems. We present a learning algorithm that realizes a simple goal-reaching behavior for an autonomous vehicle when no a-priori knowledge of the environment is provided. Information coming from a visual sensor is used to detect a general state of the system. To each state an optimal action is associated using a Q-learning algorithm. As sets of states and actions are limited, a few training trials are sufficient in simulation to learn the optimal policy. During test trials (both in simulated and real environment) fuzzy sets with membership functions are introduced to compute the state of the system and the proper action at the extent of tackling errors in state estimation due to noise in vision measures. Experimental results both in simulated and real environment are shown.

关键词： Robotics

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Diffraction of light by 3D hexagonal phase gratings: Applications for robotic color vision

Diffraction of light by 3D hexagonal phase gratings: Applica...

引用

Proceedings of the 1998 conference on intelligent robots and computer vision XVII: algorithms, techniques, and Active vision

作者： Carbon, Margarita A. Boeing Co Canoga Park CA United States

Diffraction of light by 3D phase grating layers could be effectively used for color image processing in robotic vision. Gratings with hexagonal close-packed structures have the maximum amount of cells per volume unit, which leads to an advantage for color image processing. Using the 4D spectral method, we solve the wave equation for diffraction of light by a 3D hexagonal phase grating layer of spherical particles. Both ABCA and ABAB structures are considered. Distribution of diffracted light intensity is calculated in the Fraunhofer and Fresnel diffraction zones. For particular gating distances, the incident white light diffracts in three spatially separated maximums with the central wavelengths corresponding to the three primary colors. The wavelength dependence of diffracted light intensity, for incident white light, is calculated for the three maximums. In general case, by using these three primary curves one can reconstruct the color of incident light from corresponding values of light intensities measured in the three diffracted maximums. The conditions for self-imaging of 3D grating layers are formulated and investigated. Intensity distributions for diffracted light in planes of positive and negative self-imaging, and in a plane of lowest contrast are computed.

关键词： Robotics

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Active vision System integrating fast and slow processes

Active Vision System integrating fast and slow processes

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Proceedings of the 1998 conference on intelligent robots and computer vision XVII: algorithms, techniques, and Active vision

作者： Castrillon-Santana, M. Guerra-Artal, C. Hernadez-Sosa, J. Dominguez-Brito, A. Isern-Gonzalez, J. Cabrera-Gamez, J. Hernadez-Tejera, F.M. Universidad de Las Palmas de Gran Canaria Spain

The paper describes an Active vision System whose design assumes a distinction between fast or reactive and slow or background processes. Fast processes need to operate in cycles with critical timeouts that may affect system stability. While slow processes, though necessary, do not compromise system stability if its execution is delayed. Based on this simple taxonomy, a control architecture has been proposed and a prototype implemented that is able to track people in real-time with a robotic head while trying to identify the target. In this system, the tracking module is considered as the reactive part of the system while person identification is considered a background task. This demonstrator has been developed using a new generation DSP (TMS320C80) as a specialized coprocessor to deal with fast processes, and a commercial robotic head with a dedicated DSP-based motor controller. These subsystems are hosted by a standard Pentium-Pro PC running Windows NT where slow processes are executed. The flexibility achieved in the design phase and the preliminary results obtained so far seem to validate the approach followed to integrate time-critical and slow tasks on a heterogeneous hardware platform.

关键词： computer vision

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