Scenes of polyhedral objects may be accurately represented in 2-D using line sketches. An aim of low level image processing is to generate useful binary images from grey scale images. The binary images generated by en...
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ISBN:
(纸本)0819413208
Scenes of polyhedral objects may be accurately represented in 2-D using line sketches. An aim of low level image processing is to generate useful binary images from grey scale images. The binary images generated by enhancement/threshold edge detectors are usually unrefined outlines of the underlying 3-D scene. Such images must be further processed to isolate and identify region boundaries; which, in the case of polyhedra, consist of line segments. The intersection or connection points of these line segments are known as vertices or corners. The work reported in this paper employs a decision theoretic approach to detect vertices in grey scale images.
Shape classification via linear granulometric moments is examined for patterns suffering varying degrees of edge noise. It is seen that recognition is quite poor even for modest amounts of noise and remains poor even ...
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ISBN:
(纸本)0819413208
Shape classification via linear granulometric moments is examined for patterns suffering varying degrees of edge noise. It is seen that recognition is quite poor even for modest amounts of noise and remains poor even when the patterns are first filtered by a close-open filter. Recognition accuracy is greatly improved, for both unfiltered and filtered images, by employing exterior granulometries. These are constructed by applying the various linear structuring-element sequences to the corresponding linear convex hulls of the noisy patterns. The resulting granulometric distributions are then not corrupted by noise-induced probability mass at the left of the pattern spectrum, thereby greatly diminishing the detrimental effects on the pattern spectrum moments.
By virtue of their functional approximation, learning and adaptive capabilities, the computational neural networks can be suitably employed for learning robot coordinate transformations. The major drawback of conventi...
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ISBN:
(纸本)0819413208
By virtue of their functional approximation, learning and adaptive capabilities, the computational neural networks can be suitably employed for learning robot coordinate transformations. The major drawback of conventional static feedforward neural networks based on back-propagation learning algorithm is in their very large convergence time for a given task. Any attempts to accelerate the learning process by increasing the values of learning constants in the algorithm often result in unstable systems. The intent of this paper is to describe a neural network structure called dynamic neural processor (DNP), and examine briefly how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. computer simulations are provided to demonstrate the effectiveness of the proposed learning scheme using the DNP.
vision guided mobile robot navigation is complex and requires analysis of tremendous amounts of information in real time. In order to simplify the task and reduce the amount of information, human preattentive mechanis...
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ISBN:
(纸本)0819413208
vision guided mobile robot navigation is complex and requires analysis of tremendous amounts of information in real time. In order to simplify the task and reduce the amount of information, human preattentive mechanism can be adapted [Nag90]. During the preattentive search the scene is analyzed rapidly but in sufficient detail for the attention to be focused on the `area of interest.' The `area of interest' can further be scrutinized in more detail for recognition purposes. This `area of interest' can be a text message to facilitate navigation. Gabor filters and an automated turning mechanism are used to isolate the `area of interest.' These regions are subsequently processed with optimal spatial resolution for perception tasks. This method has clear advantages over the global operators in that, after an initial search, it scans each region of interest with optimum resolution. This reduces the volume of information for recognition stages and ensures that no region is over or under estimated.
Most of the information regarding the shape of polyhedral objects is preserved in the edges and the vertices of these objects. Gray level images of scenes containing such objects are often processed to extract edge an...
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ISBN:
(纸本)0819413208
Most of the information regarding the shape of polyhedral objects is preserved in the edges and the vertices of these objects. Gray level images of scenes containing such objects are often processed to extract edge and vertex information to produce equivalent line sketches. An accurate line sketch of a scene serves as an effective input to high level vision systems concerned with scene understanding or object recognition. The performance of these systems is therefore greatly dependent on the accuracy of the line sketch. The work reported in this paper addresses the issues associated with generating accurate line sketches from gray level images. The methods described here have been implemented and tested with real and synthetic images and are compared to other vertex or corner detection techniques. The performance of the vertex detector is assessed using simulation runs on images with varied signal-to-noise ratios. The computational performance of this algorithm is evaluated and assessed by operating directly on the gray-scale image.
We describe an implemented computer program that recognizes the occurrence of simple spatial motion events in simulated video input. The program receives an animated line-drawing as input and produces as output a sema...
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ISBN:
(纸本)0819413208
We describe an implemented computer program that recognizes the occurrence of simple spatial motion events in simulated video input. The program receives an animated line-drawing as input and produces as output a semantic representation of the events occurring in that movie. We suggest that the notions of support, contact, and attachment are crucial to specifying many simple spatial motion event types and present a logical notation for describing classes of events that incorporates such notions as primitives. We then suggest that the truth values of such primitives can be recovered from perceptual input by a process of counterfactual simulation, predicting the effect of hypothetical changes to the world on the immediate future. Finally, we suggest that such counterfactual simulation is performed using knowledge of naive physical constraints such as substantiality, continuity, gravity, and ground plane. We describe the algorithms that incorporate these ideas in the program and illustrate the operation of the program on sample input.
There has recently been growing interest in exploiting the concept of reasoning about function for object recognition. In a function-based approach to object recognition, recognition of an object means labeling it as ...
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ISBN:
(纸本)0819413208
There has recently been growing interest in exploiting the concept of reasoning about function for object recognition. In a function-based approach to object recognition, recognition of an object means labeling it as belonging to some category of objects according to the function that it could serve. The few function-based recognition systems which have so far been described in the literature have all assumed that the input to the problem is a pure static shape description. By `pure' shape we mean that the only object property that the systems have reasoned about is their abstract shape. By `static' shape we mean that the systems have reasoned about an object from only a single (assumed rigid) abstract shape instance. This paper discusses some of the issues which must be addressed in extending the function-based approach to handle non-shape properties (such as material properties) and dynamic shape descriptions.
In this paper I will describe work in progress on a low cost vision-based robot designed to give primitive tours. The system is very simple, robust and efficient, and runs on a hardware platform which could be duplica...
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This paper describes a fuzzy logic controller (FLC) designed and implemented to control the yaw angle of a 10 kW fixed speed teetered-rotor wind turbine presently being commissioned at the University of Texas at El Pa...
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ISBN:
(纸本)0819413208
This paper describes a fuzzy logic controller (FLC) designed and implemented to control the yaw angle of a 10 kW fixed speed teetered-rotor wind turbine presently being commissioned at the University of Texas at El Paso. The technical challenge of this project is that the wind turbine represents a highly stochastic nonlinear system. The problems associated with the wind turbine yaw control are of a similar nature as those experienced with position control of high inertia equipment like tracking antenna, gun turrets, and overhead cranes. Furthermore, the wind turbine yaw controller must be extremely cost-effective and highly reliable in order to be economically viable compared to the fossil fueled power generators.
This paper describes a model based vision system that has been developed which is able to perform model based reasoning at real-time (or near real-time) rates and for which both the hardware and prototyping costs are ...
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ISBN:
(纸本)0819413208
This paper describes a model based vision system that has been developed which is able to perform model based reasoning at real-time (or near real-time) rates and for which both the hardware and prototyping costs are low. The basic approach taken is to extract a set of useful features from observed models using a library of feature primitive operators. Scale and orientation invariant combinations of these features are used as indices into a hardware lookup table to establish initial correspondence between similar combinations that will be encountered when examining unknown objects. When performing initial recognition of an unknown object, evidence for an object in a particular spatial pose is accumulated, giving rise to an initial set of hypotheses. The strongest hypotheses are then refined by iteratively hypothesizing new (previously uninstantiated) model/object feature matches and computing a confidence measure associated with the current instantiation set. If confidence increases the newly hypothesized instantiation is retained, otherwise it is discarded.
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