We consider analog neural network implementations (using VLSI or optical technologies) with limited accuracy and various noise and nonlinearity error sources. algorithms and techniques to achieve high performance (goo...
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ISBN:
(纸本)0819407453
We consider analog neural network implementations (using VLSI or optical technologies) with limited accuracy and various noise and nonlinearity error sources. algorithms and techniques to achieve high performance (good recognition P'c% and large storage capacity) on such systems are considered. The adaptive clustering neural net (ACNN) and robust Ho-Kashyap (HK-2) associative processor (AP) are the neural networks considered in detail.
Image segmentation, the partitioning of an image into meaningful parts, is a major concern of any computervision system. The meaningful parts of a text image are lines of text, words, and characters. In this paper, t...
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ISBN:
(纸本)0819407445
Image segmentation, the partitioning of an image into meaningful parts, is a major concern of any computervision system. The meaningful parts of a text image are lines of text, words, and characters. In this paper, the segmentation of pages of text into lines of text and lines of text into characters on a parallel machine are examined. Using a parallel machine for text image segmentation allows the use of techniques that are impractical on a serial machine due to the computation time needed. It is possible to use a parallel machine to segment text images of lines using spatial histograms with an accuracy of 97.9% at a speed of 30 milliseconds or less per character. Statistically adaptive rules based on dynamic adaptive sampling are used for line segmentation and also for improved accuracy of character segmentation. The segmentation of lines from a page can also be accomplished using a set of statistically adaptive rules which allow sloped lines of text to be segmented. The use of these statistical rules on a parallel machine increases processing time by no more than 1 millisecond per character. Using statistical rules in combination with knowledge about the printed style increases the segmentation accuracy to 99.2% correct for machine-printed text and 89.6% for hand-printed text.
The automation of rotorcraft low-altitude flight presents challenging problems in flight control and sensor systems. The currently explored approach uses one or more passive sensors, such as a television camera, to ex...
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ISBN:
(纸本)0819407453
The automation of rotorcraft low-altitude flight presents challenging problems in flight control and sensor systems. The currently explored approach uses one or more passive sensors, such as a television camera, to extract environmental obstacle information. Obstacle imagery can be processed using a variety of computervisiontechniques to produce a time-varying map of range to obstacles in the sensor's field of view along the helicopter flight path. To maneuver in tight space, obstacle-avoidance methods would need very reliable range map information by which to guide the helicopter through the environment. In general, most low level computervisiontechniques generate sparse range maps which include at least a small percentage of bad estimates (outliers). This paper examines two related techniques which can be used to eliminate outliers from a sparse range map. Each method clusters sparse range map information into different spatial classes relying on a segmented and labeled image to help in spatial classification within the image plane.
A method for recognizing closed containers based on features extracted from their circular tops is presented. The approach developed consists of obtaining images from two spatially separated cameras that utilize both ...
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ISBN:
(纸本)0819410268
A method for recognizing closed containers based on features extracted from their circular tops is presented. The approach developed consists of obtaining images from two spatially separated cameras that utilize both diffuse and specular light sources. The images thus obtained are used to segment target objects from the background and to extract representative features. The features utilized consist of container height as computed using stereopsis as well as the mean, variance, and second central moments of the intensities of the segmented caps. The recognition procedure is based on a minimum distance Mahalanobis classifier which takes feature covariance into account. The discussion that follows details the algorithmic approach for the entire system including image acquisition, object segmentation, feature extraction, and pattern classification. Result of test runs involving sets of several hundred training samples and untrained samples are presented.
An intelligent robot with a camera and a partial model of its environment should be able to determine where it is from what it sees. This goal, landmark based navigation, can be realized using geometric object recogni...
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ISBN:
(纸本)0819410268
An intelligent robot with a camera and a partial model of its environment should be able to determine where it is from what it sees. This goal, landmark based navigation, can be realized using geometric object recognition algorithms. An important problem that arises in the development of such algorithms concerns the role of full 3-D perspective projection. Much of the work on object recognition has focused upon simplified problems which are essentially 2- D. One such simplification uses weak-perspective: to test the alignment of matched features object models are rotated, translated, and scaled in the image plane. At increased computational cost, full-perspective can be incorporated into recognition using a family of probabilistic optimization procedures based upon local search. This paper considers two specific algorithms from this family: subset-convergent and variable-depth local search. Both approaches reliably recognize landmarks even when landmark appearance is sensitive to perspective. Results presented here suggest the relatively simpler variable-depth algorithm is preferable when errors in the robot pose estimate are smaller, but that at some point as uncertainty in the initial pose estimate increases the more sophisticated subset-convergent algorithm becomes preferable.
Constraints are mathematical mapping functions which transform from an attribute or feature space onto a score or measure of plausibility. The term plausible is used because this paper assumes one is looking to suppor...
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ISBN:
(纸本)0819407445
Constraints are mathematical mapping functions which transform from an attribute or feature space onto a score or measure of plausibility. The term plausible is used because this paper assumes one is looking to support a hypothesis rather than refute it. In this paper, a system is described which allows the algorithm developer to easily incorporate domain knowledge into an interpretation process through the graphical creation and editing of constraints. These constraints can be applied to multiple sets of data through the use of application programs. Groupings or spatial relationships such as collinearity or nearness are also attributes which may be constrained in an attempt to interpret image data. Model matches may likewise be written as constraint mappings. Primitive constraints may be combined to form compound constraints, and differing compounding weights may be assigned to primitive constraints. If these weights are written as functions dependent upon other information, the a system developed with this process can be made adaptive.
In a program for reading printed music, a variety of low level feature detectors were used to extract sufficient information to reconstruct the score. All feature detectors were unreliable to some extent, and were bia...
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ISBN:
(纸本)0819410268
In a program for reading printed music, a variety of low level feature detectors were used to extract sufficient information to reconstruct the score. All feature detectors were unreliable to some extent, and were biased towards yielding false positives rather than missing features. In order to reconstruct the score, conflicting information from the feature detectors needed to be recognized and eliminated. All objects as well as their geometric and semantic relations were represented in an object oriented framework. Ambiguity (implemented as a generic predicate) was defined -- and explicitly represented -- in terms of these relationships. Examples of ambiguous relationships include: An accidental and a note head having an on-top-of geometric relationship, or the total duration of notes in a measure not being equal to the notated time signature. A method inspired by Waltz filtering was used to produce a consistent, unambiguous interpretation. Waltz filtering is a symbolic constraint propagation technique which has been applied to line drawings. During interpretation attention was focused on objects which had ambiguous relations. Ambiguity was iteratively reduced or removed by using a variety of methods employing information gathered from local unambiguous relations.
The ability to stabilize the image of one moving object in the presence of others by active movements of the visual sensor is an essential task for biological systems, as well as for autonomous mobile robots. An algor...
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ISBN:
(纸本)0819410268
The ability to stabilize the image of one moving object in the presence of others by active movements of the visual sensor is an essential task for biological systems, as well as for autonomous mobile robots. An algorithm is presented that evaluates the necessary movements from acquired visual data and controls an active camera system (ACS) in a feedback loop. No a priori assumptions about the visual scene and objects are needed. The algorithm is based on functional models of human pursuit eye movements and is to a large extent influenced by structural principles of neural information processing. An intrinsic object definition based on the homogeneity of the optical flow field of relevant objects, i.e., moving mainly fronto- parallel, is used. Velocity and spatial information are processed in separate pathways, resulting in either smooth or saccadic sensor movements. The program generates a dynamic shape model of the moving object and focuses its attention to regions where the object is expected. The system proved to behave in a stable manner under real-time conditions in complex natural environments and manages general object motion. In addition it exhibits several interesting abilities well-known from psychophysics like: catch-up saccades, grouping due to coherent motion, and optokinetic nystagmus.
Usually, gray-level images are arranged as two-dimensional (NxM)-matrices. Tracing its contours is a common way to obtain information about an imaged object, such as position and orientation, or for purposes of object...
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ISBN:
(纸本)0819410268
Usually, gray-level images are arranged as two-dimensional (NxM)-matrices. Tracing its contours is a common way to obtain information about an imaged object, such as position and orientation, or for purposes of object recognition. This paper describes the generation of contour-based object descriptions by edge-detection and contour-tracing. A complex differential operator is used to detect edges in the image. In addition to the gradient, also the local orientation of edges can be computed with an accuracy of approximately 5 degree(s). This edge-oriented description, which is still arranged as a two-dimensional matrix, occupies twice as much memory as the original gray-level image (gradient plus orientation) and there is no knowledge about the course of the contour. In addition to that, in most cases this edge-oriented image is fragmentary, due to illumination restrictions and shades. For this reason the imaged contour is traced using a Kalman-filter-based algorithm. The contour tracer connects and completes these edge fragments. The algorithm is able to follow the course of a contour without any prior knowledge, even if its direction changes erratically. It has been tested successfully in several applications in industrial production testing (for example for controlling an optical range sensor of a 3-D-measurement system in assembly lines).
In this paper, we develop a new learning approach, the Hilbert learning. This approach is similar to Fractal learning, but the Fractal part is replaced by Hilbert space. Like the Fractal learning, the first stage is t...
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ISBN:
(纸本)0819410268
In this paper, we develop a new learning approach, the Hilbert learning. This approach is similar to Fractal learning, but the Fractal part is replaced by Hilbert space. Like the Fractal learning, the first stage is to encode an image to a small vector in the internal space of a learning system. The next stage is to quantize the internal parameter space. The internal space of a Hilbert learning system is defined as follows: A pattern can be interpreted as a representation of a vector in a Hilbert space. Any vectors in a Hilbert space can be expanded. If a vector happens to be in a subspace of a Hilbert space where the dimension L of the subspace is low (order of 10), the vector can be specified by its norm, an L-vector, and the Hermitian operator which spans the Hilbert space. This establishes a mapping from an image space to the internal space P. This mapping converts an input image to a 4-tuple: t (epsilon) P equals (Norm, T, N, L-vector), where T is an operator parameter space, N is a set of integers which specifies the boundary condition. The encoding is implemented by mapping an input pattern into a point in its internal space. We assume that a system uses local search algorithm, i.e., the system adjusts its internal data locally. The search is first conducted for an operator in a parameter space of operators, then an error function (delta) (t) is computed. The algorithm stops at a local minimum of (delta) (t). Finally, the input training set divides the internal space by a quantization procedure.
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