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Contextual fuzzy cognitive maps for geographic information systems

Contextual fuzzy cognitive maps for geographic information s...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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

Designing a system that is able to make use of quantitative and qualitative data for real world applications is a challenging problem. Traditional systems produce representational descriptions that are often not very useful to the human expert. To rectify this problem we propose a structure based on contextual fuzzy cognitive maps (CFCMs) for GIS systems. For a given goal, our system structure is hierarchical by context, multi-layered by variations in data over periods of time, and semi-qualitative in that the CFCMs build causal links and relationships between landmarks and concepts.

关键词： Fuzzy cognitive maps Geographic Information Systems Humans Computational intelligence Society computer vision machine intelligence computer science Software engineering Application software Communication cables

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Fuzzy cognitive map and its causal inferences

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Zhi-Qiang Liu Yuan Miao Department of Computer Science and Software Engineering Computer Vision and Machine Intelligence Laboratory University of Melbourne Parkville VIC Australia

ISBN: (纸本)0780354060

Fuzzy cognitive maps (FCM) is a powerful framework for representing structured human knowledge and causal inference. This paper presents a new and effective approach to analyzing causal inference mechanism of FCM. We focus on binary concept states proposed originally by Kosko (1986). Given initial conditions, FCM is able to reach only certain states in its state space. The problem of finding whether a state is reachable in the FCM is NP hard. In order to effectively carry out the design of fuzzy cognitive maps we propose to divide an FCM into basic FCM modules. This paper also presents a recursive formula for the calculation of FCM inference patterns in terms of key vertices.

关键词： Fuzzy cognitive maps State-space methods Fuzzy systems computer vision machine intelligence computer science Software engineering Humans Inference mechanisms Knowledge representation

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Classification-driven object-based image retrieval

Classification-driven object-based image retrieval

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IEEE International Conference on Multimedia Computing and Systems (ICMCS)

作者： L. Jia L. Kitchen Computer Vision and Machine Intelligence Lab Department of Computer Science and Software Engineering University of Melbourne Parkville VIC Australia

This paper describes an approach for object-based image retrieval based on classes of objects in images. In this approach, contours of objects are extracted from images and are represented under a scheme which satisfies scale, rotation and translation invariance. Classifier learning techniques are used to classify objects in images into different classes. Image similarity calculation is performed based on class information of objects. Experimental results show that the method is effective and efficient.

关键词： Image retrieval Shape Image databases Image segmentation Classification tree analysis Object recognition Decision trees computer vision machine intelligence computer science

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Shape recognition and vision-based robot control by shape morphing

Shape recognition and vision-based robot control by shape mo...

引用

International Conference on Information intelligence and Systems

作者： R. Singh R.M. Voyles D. Littau N.P. Papanikolopoulos Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota Minneapolis MN USA

The article presents a unified approach for object recognition and recognition based control of robotic interactions with objects. The approach is based on morphing the shape of objects. The morph of one object to another is treated as a deformation and is defined such that it can be quantified using a physics based model. This quantification serves as a dissimilarity measure and is used for shape recognition. By storing the views that represent the grasp positions for different object, the proposed framework is used to identify the desired end position a robot needs to attain for interacting with the given object. Furthermore, the images synthesized during the morph define a view based trajectory, starting with the view defining the initial robot-object orientation to the view defining the desired robot-object orientation. We propose a technique where these synthetic images are used to control the motion of a PUMA 560 eye-in-hand manipulator to execute alignment and grasping tasks. The proposed approach does not require complete calibration information, obviates manual feature selection and correspondence, can provide smooth trajectories, and needs a single image for each object (in a 4-DOF formulation). Potential applications range from recognition and positioning with respect to partially occluded or deformable objects as well as planning robotic grasping based on human demonstration.

关键词： Shape control Robot control Shape measurement Object recognition Physics Deformable models Motion control Manipulators Calibration Humans

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Shape recognition and vision-based robot control by shape morphing

Shape recognition and vision-based robot control by shape mo...

引用

1999 International Conference on Information intelligence and Systems, ICIIS 1999

作者： Singh, Rahul Voyles, Richard M. Littau, David Papanikolopoulos, Nikolaos P. Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States Bio-Informatics Division Exelixis Pharmaceuticals 280 E. Grand Avenue So. San FranciscoCA94080 United States

ISBN: (纸本)0769504469

In this paper we article presents a unified approach for object recognition and recognition based control of robotic interactions with objects. The approach is based on morphing the shape of objects. The morph of one object to another is treated as a deformation and is defined such that it can be quantified using a physics based model. This quantification serves as a dissimilarity measure and is used for shape recognition. By storing the views that represent the grasp positions for different object, the proposed framework is used to identify the desired end position a robot needs to attain for interacting with the given object. Furthermore, the images synthesized during the morph define a view based trajectory, starting with the view defining the initial robot-object orientation to the view defining the desired robot-object orientation. We propose a technique where these synthetic images are used to control the motion of a PUMA 560 eye-in-hand manipulator to execute alignment and grasping tasks. The proposed approach does not require complete calibration information, obviates manual feature selection and correspondence, can provide smooth trajectories, and needs a single image for each object (in a 4-DOF formulation). Potential applications range from recognition and positioning with respect to partially occluded or deformable objects as well as planning robotic grasping based on human demonstration. © 1999 IEEE.

关键词： Object recognition

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vision-based monitoring of weaving sections

Vision-based monitoring of weaving sections

引用

International Conference on Intelligent Transportation

作者： O. Masoud N.P. Papanikolopoulos E. Kwon Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science University of Minnesota Minneapolis MN USA Center for Transportation Studies University of Minnesota Minneapolis MN USA

This paper presents algorithms for vision-based monitoring of weaving sections. These algorithms have been developed for the Minnesota department of Transportation in order to acquire data for several weaving sections in the Twin Cities Area. Unlike commercially available systems, the proposed algorithms can track and count vehicles as they change lanes. Furthermore, they provide the velocity and the direction of each vehicle in the weaving section. Experimental results from various weaving sections with various weather conditions are presented. The proposed methods are based on the establishment of correspondences among blobs and vehicles as the vehicles move through the weaving section. The blob tracking problem is formulated as a bipartite graph optimization problem.

关键词： Weaving Traffic control computerized monitoring Road vehicles Road transportation Vehicle detection Data mining Space vehicles Layout Artificial intelligence

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Planning of regrasp operations

Planning of regrasp operations

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： S.A. Stoeter S. Voss N.P. Papanikolopoulos H. Mosemann Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota Minneapolis MN USA Institute for Robotics and Process Control Technical University of Braunschweig Germany

Robotic manipulators in contemporary work-cells are often incapable of solving even simple pick-and-place operations. More specifically, most systems require each object to be supplied in the same and pre-defined way. Introducing regrasping to the production cycle relaxes the constraints imposed on supply mechanisms. Regrasping describes the operation that must be performed whenever an object's pick-up grasp is incompatible with its put-down grasp. The paper presents a novel approach to solve the regrasp problem for robots equipped with parallel-jaw end-effecters. The proposed method first evaluates the object's possible grasps and placements. These are subsequently combined to form grasp-placement-grasp triples. A regrasp sequence leading from the pick-up to the put-down grasp is generated by searching through the resulting space of compatible grasp-placement-grasp triples. The algorithm takes kinematic and geometric constraints of both the manipulator and the objects into account. Computational concerns are addressed by subdividing the calculation into an off-line and a fast online phase.

关键词： Intelligent robots Robot vision systems Belts Path planning Artificial intelligence Laboratories computer science Process control Manipulators Production

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A new thresholding algorithm based on all-pole model

A new thresholding algorithm based on all-pole model

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International Conference on Pattern Recognition

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

ISBN: (纸本)0818685123

A new thresholding algorithm based on all-pole model histogram is presented. The parameters of the all-pole model are estimated from the second derivative of log model histogram. The main merits are that the model can produce the desired number of peaks in model histogram and the algorithm is efficient in computation. The proposed algorithm can be used for binarisation and multilevel thresholding and good results have been achieved for a wide range of images.

关键词： Histograms Image segmentation Entropy computer vision machine intelligence computer science Read only memory Image converters Computational efficiency Computational complexity

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A novel fuzzy Hough transform for shape representation

A novel fuzzy Hough transform for shape representation

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： R. Soodamani Z.Q. Liu Computer Vision and Machine Intelligence Laboratory (CVMIL) Department of Computer Science University of Melbourne Carlton VIC Australia

Three techniques of straight line Hough transform are proposed by a generalised mathematical formulation of the Hough transform in terms of aggregation and weight functions. Amongst these techniques, inclusive of the conventional Hough transform, it is shown by experimental results on a set of 3D nonparametric objects, and in a controlled and inherent noise environment, that the fuzzy Hough transform approach performs the best, as a global shape descriptor.

关键词： Shape control Convolution Discrete transforms computer vision machine intelligence computer science Australia Working environment noise Noise shaping Fuzzy control

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Learning fuzzy modelling through genetic algorithm for object recognition

Learning fuzzy modelling through genetic algorithm for objec...

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IEEE International Conference on Evolutionary Computation

作者： R. Soodamani Z.Q. Liu Computer Vision and Machine Intelligence Lab Department of Computer Science University of Melbourne Australia

The paper proposes a genetic-algorithm-based learning strategy that models membership functions of the fuzzy attributes of surfaces in a model based machine vision system. The objective function aims at enhancing recognition performance in terms of maximizing the degree of discrimination among classes. As a result, the accuracy of recognizing known instances of objects and generalisation capability by recognizing unknown instances of known objects are greatly improved. The performance enhancement of a model based object recognition system consisting of a set of synthetic range images is established by incorporating a dynamic off-line learning mechanism using a genetic algorithm in the feedback path of the system.

关键词： Genetic algorithms Object recognition Fuzzy systems Fuzzy sets Shape Feedback System testing Image segmentation computer vision machine intelligence

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