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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： C.E. Smith N.P. Papanikolopoulos Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science University of Minnesota Minneapolis MN USA

Recent work combining robotics with vision has emphasized an active vision paradigm where the system changes the pose of the camera to improve environmental knowledge or to establish and presence a desired relationship between the robot and objects in the environment. Much of this work has concentrated upon the active observation of objects by the robotic agent. In this paper we present extensions to the controlled active vision framework that focus upon the autonomous grasping of a moving or static object in the manipulator's workspace. Our work extends the capabilities of an eye-in-hand system beyond those as a "pointer" or a "camera orienter" to provide the flexibility required to robustly interact with the environment in the presence of uncertainty. The proposed work is experimentally verified using the Minnesota robotic visual tracker to automatically select object features, to derive estimates of unknown environmental parameters, and to supply a control vector based upon these estimates to guide the manipulator in the grasping of a moving or static object.

关键词： Robot sensing systems Robot vision systems Cameras Manipulators Intelligent robots Grippers Charge-coupled image sensors Artificial intelligence Laboratories computer science

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A system for automatically detecting and tracking objects using a visually-guided manipulator

A system for automatically detecting and tracking objects us...

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IEEE International Conference on Systems, Man and Cybernetics

作者： C.A. Richards N.P. Papanikolopoulos Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science University of Minnesota Minneapolis MN USA Department of Computer Science University of Stanford Stanford CA USA

This paper presents a framework for the automatic visual detection of moving objects in robotic servoing tasks. The paper describes a "figure/ground" scheme which is able to perform detection without making many assumptions that would limit the generality of the approach. We describe optimizations which allow for real-time execution of the frame-differencing that is the basis of our framework. Experimentation has demonstrated that the results of the visual detection can provide information helpful in focusing attention on a specific subset of objects. Furthermore, we show how the detection technique can be integrated with existing methods for visual tracking. We mention some details that have been addressed in order to apply these theories to an actual robotic system, including the use of an optimal controller Our paper explains how this framework has been implemented in our experimental robotic system MRVT, and it describes several results obtained from this experimentation.

关键词： Object detection Pixel Robotics and automation Manipulators Intelligent robots Robot vision systems computer science Visual servoing Optical computing Artificial intelligence

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Using vision-based control techniques for grasping objects

Using vision-based control techniques for grasping objects

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IEEE International Conference on Systems, Man and Cybernetics

作者： C.E. Smith N.P. Papanikolopoulous Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science University of Minnesota Minneapolis MN USA University of Minnesota Minneapolis MN US Dept. of Comput. Sci. Minnesota Univ. Minneapolis MN USA

We present additions to the controlled active vision framework that focus upon the autonomous grasping of a moving object in the manipulator's workspace. Our work extends the capabilities of an eye-in-hand robotic system beyond those as a "pointer" or a "camera orienter" to provide the flexibility required to robustly interact with the environment in the presence of uncertainty. The proposed work is experimentally verified using the Minnesota Robotic Visual Tracker (MRVT) to automatically select object features, to derive estimates of unknown environmental parameters, and to supply a control vector based upon these estimates to guide the manipulator in the grasping of a moving object. The system grasps objects in the manipulator's workspace without requiring the object to follow a specific trajectory and without requiring the object to maintain a specific orientation.

关键词： Robot vision systems Manipulators Robotics and automation Cameras Grippers Robustness Uncertainty Automatic control Optical distortion Artificial intelligence

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Rotation invariant texture classification using covariance

Rotation invariant texture classification using covariance

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The 1994 1st IEEE International Conference on Image Processing

作者： Madiraju, S.V.R. Liu, Chih-Chiang Computer Vision and Machine Intelligence Laboratory Department of Computer Science University of Melbourne ParkvilleVIC3052 Australia

Proposes a simple and powerful approach for texture classification using the eigenfeatures of local covariance measures. A texton encoder produces a texture code which is invariant to local and global textural rotations. The proposed method uses six statistical features obtained from two scales of this invariant encoder to result in indices for roughness, anisotropy, and other higher-order textural features. Classification results for synthetic and natural textures are presented. The authors also discuss the effect of window sizes used at local and global scales on the performance of the classifier. © 1994 IEEE.

关键词： Signal encoding

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Feature extraction and analysis of handwritten words in grey-scale images using Gabor filters

Feature extraction and analysis of handwritten words in grey...

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The 1994 1st IEEE International Conference on Image Processing

作者： Buse, R. Liu, Zhi-Qiang Computer Vision and Machine Intelligence Lab. Department of Computer Science University of Melbourne ParkvilleVIC3052 Australia

We present a new off-line word recognition system that is able to recognise unconstrained handwritten words from their grey-scale images, and is based on structural and relational information in the handwritten word. We use Gabor filters to extract features from the words, and then use an evidence-based approach for word classification. A solution to the Gabor filter parameter estimation problem is given, enabling the Gabor filter to be automatically tuned to the word image properties. Our experiments show that the proposed method achieves reasonably high recognition rates compared to standard classification methods. © 1994 IEEE.

关键词： Gabor filters

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Multiagent fault‐diagnosis expert system in power systems

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European Transactions on Electrical Power 1995年第5期5卷 341-347页

作者： Wang, J.P. Trécat, J. Dr. Jianping Wang is a doctor fellow in the Faculte Polytechnique de Mons in Belgium. His current research field is parallel processing applications in power systems. Dr. Wang obtained his BS degree from Southwest Jiaotong University in P.R. Chinain 1982. From 1982 to 1990 he was appointed as an assistant lecturer. lecturer and associate professor respectively in the electrical engineering department of Southwest Jiaotong University. He obtained his Ph.D in the FacultC Polytechnique de Mons in July of 1993. (BD Dolez 3 1 FPMS B-7000 Mons Belgium T +32-65/37 41 87 Fax +32-65/37 43 00) Professor J. Trécat is a director in the power system laboratory in the Faculty Polytechnique de Mons. He obtained his Ph.D in the University of Manchester Institute of Science and Technology(UM1ST) in U.K. in 1970. He has been working in the fields of power system automation artificial intelligence application and computer vision in the Facult6 Polytechnique de Mons since 1970. He is a senior member of IEEE and he has made much contribution both in electrical engineering education and research. (BD Dolez 3 I FPMS B-7000 Mons T +32-651374185. Fax +32-6513743 00)

In order to solve the speed problem and shallow reasoning problem met in current research in fault diagnosis expert system, this paper presents a model based parallel fault diagnosis expert system for energy management systems (EMS). The Petri net model, which corresponds to the relationship between power system components and their protection systems, has been used for the inference in fault diagnosis. A multiagent parallel processing expert system, which is based on five transputers in Sun workstation, has been built for fault diagnosis purposes. The test results show that this expert system is not only fast but also has better performances. Copyright © 1995 John Wiley & Sons, Ltd.

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Rotation invariant texture classification using covariance

Rotation invariant texture classification using covariance

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IEEE International Conference on Image Processing

作者： S.V.R. Madiraju Chih-Chiang Liu The Computer Vision and Machine Intelligence Laboratory Department of Computer Science University of Melbourne Parkville Australia

关键词： Eigenvalues and eigenfunctions Covariance matrix Feature extraction Symmetric matrices computer vision Laboratories computer science Rotation measurement Anisotropic magnetoresistance Computational complexity

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Feature extraction and analysis of handwritten words in grey-scale images using Gabor filters

Feature extraction and analysis of handwritten words in grey...

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IEEE International Conference on Image Processing

作者： R. Buse Zhi-Qiang Liu Computer Vision and Machine Intelligence Laboratory Department of Computer Science University of Melbourne Parkville VIC Australia

关键词： Feature extraction Image analysis Gabor filters Skeleton Image segmentation computer vision Handwriting recognition Image recognition Frequency Data mining

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Off-line recognition of signatures using revolving active deformable models

Off-line recognition of signatures using revolving active de...

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IEEE International Conference on Systems, Man and Cybernetics

作者： I. Pavlidis R. Mavuduru N. Papanikolopoulos Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science University of Minnesota Minneapolis MN USA

This paper presents a novel approach to the problem of signature recognition. We introduce the use of revolving active deformable models as a powerful way of capturing the unique characteristics of a signature's silhouette. Experimental evidence shows that the silhouette of a signature uniquely determines the signature in the majority of cases. The objective of our method is to recognize signatures based on the spatial properties of the signature boundaries. Our active deformable models originate from the snakes introduced to computer vision by Kass et al. (1987), but their implementation has been tailored to the task at hand. These computer-generated models interact with the virtual gravity field created by the image gradient. Ideally, the uniqueness of this interaction mirrors the uniqueness of the signature's silhouette. The proposed method obviates the use of a computationally expensive segmentation approach and yields satisfactory results regarding performance, without compromising the accuracy rate. Interestingly, the active deformable models have been implemented in such a way, that the method is potentially fully parallelizable. The experiments performed with a signature database show that the proposed method is promising.< >

关键词： Deformable models Databases Character recognition Handwriting recognition Artificial intelligence Intelligent robots Robot vision systems Laboratories computer science computer vision

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Computation of shape through controlled active exploration

Computation of shape through controlled active exploration

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IEEE International Conference on Robotics and Automation (ICRA)

作者： C.E. Smith N.P. Papanikolopoulos Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science University of Minnesota Minneapolis MN USA

Accurate knowledge of depth continues to be of critical importance in robotic systems. Without accurate depth knowledge, tasks such as inspection, tracking, grasping, and collision-free motion planning prove to be difficult and often unattainable. Traditional visual depth recovery has relied upon techniques that require the solution of the correspondence problem or require known lighting conditions and Lambertian surfaces. In this paper, we present a technique for the derivation of depth from feature points on a target's surface using the controlled active vision framework. We use a single visual sensor mounted on the end-effector of a robotic manipulator to automatically select feature points and to derive depth estimates for those features using adaptive control techniques. Movements of the manipulator produce displacements that are measured using a sum-of-squared difference (SSD) optical flow. The measured displacements are fed into the controller to alter the path of the manipulator and to refine the depth estimate.< >

关键词： Shape control Manipulators Automatic control Displacement measurement Inspection Tracking Motion planning Sensor phenomena and characterization Robot sensing systems Robotics and automation

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