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

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SEGMENTING TEXT IMAGES WITH MASSIVELY PARALLEL MACHINES

SEGMENTING TEXT IMAGES WITH MASSIVELY PARALLEL MACHINES

引用

CONF ON intelligent robots AND computer vision 10 : algorithms AND techniques

作者： WILKINSON, RA National Institute of Standards and Technology (United States)

ISBN: (纸本)0819407445

Image segmentation, the partitioning of an image into meaningful parts, is a major concern of any computer vision 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.

关键词： Image segmentation Image processing Binary data Robot vision robots computer vision technology Machine vision Computing systems Image processing algorithms and systems Optical character recognition

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CLUSTERING METHODS FOR REMOVING OUTLIERS FROM vision-BASED RANGE ESTIMATES 10

CLUSTERING METHODS FOR REMOVING OUTLIERS FROM VISION-BASED R...

引用

CONF ON intelligent robots AND computer vision 10 : NEURAL, BIOLOGICAL, AND 3-D METHODS

作者： HUSSIEN, B SUORSA, R NASA/Ames Research Ctr. (United States)

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 computer vision techniques 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 computer vision techniques 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.

关键词： Image segmentation Fuzzy logic computer vision technology Machine vision Image processing Image processing algorithms and systems robots Robot vision algorithms Annealing

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RECOGNITION OF CONTAINERS USING A MULTIDIMENSIONAL PATTERN CLASSIFIER

RECOGNITION OF CONTAINERS USING A MULTIDIMENSIONAL PATTERN C...

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11TH CONF ON intelligent robots AND computer vision : algorithms, techniques, AND ACTIVE vision

作者： MAGEE, M WENIGER, R WENZEL, D PIRASTEH, R Univ. of Wyoming (United States) Southwest Research Institute (United States) Sky Chefs (United States)

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.

关键词： Image segmentation Machine vision computer vision technology Robot vision robots Cameras Feature extraction Mahalanobis distance Light sources Image processing

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COMPARING SUBSET-CONVERGENT AND VARIABLE-DEPTH LOCAL SEARCH ON PERSPECTIVE-SENSITIVE LANDMARK RECOGNITION PROBLEMS

COMPARING SUBSET-CONVERGENT AND VARIABLE-DEPTH LOCAL SEARCH ...

引用

11TH CONF ON intelligent robots AND computer vision : algorithms, techniques, AND ACTIVE vision

作者： BEVERIDGE, JR Univ. of Massachusetts/Amherst (United States)

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.

关键词： Image segmentation 3D modeling Robot vision Algorithm development Detection and tracking algorithms Data modeling Visual process modeling computer vision technology Machine vision Cameras

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USING CONSTRAINTS TO INCORPORATE DOMAIN KNOWLEDGE

USING CONSTRAINTS TO INCORPORATE DOMAIN KNOWLEDGE

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CONF ON intelligent robots AND computer vision 10 : algorithms AND techniques

作者： EGGLESTON, P Amerinex Artificial Intelligence Inc. (United States)

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.

关键词： Image processing Image segmentation Machine vision Databases Fuzzy logic Algorithm development computer vision technology Robot vision robots Intelligence systems

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HOW I HANDLED AMBIGUITY IN A SYSTEM TO READ MUSIC SCORES

HOW I HANDLED AMBIGUITY IN A SYSTEM TO READ MUSIC SCORES

引用

11TH CONF ON intelligent robots AND computer vision : algorithms, techniques, AND ACTIVE vision

作者： RUTTENBERG, A Media Lab./MIT (United States)

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.

关键词： Sensors computer vision technology Head Machine vision Robot vision robots Composites Databases Computing systems Reconstruction algorithms

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GAZE CONTROL FOR AN ACTIVE CAMERA SYSTEM BY MODELING HUMAN PURSUIT EYE-MOVEMENTS

GAZE CONTROL FOR AN ACTIVE CAMERA SYSTEM BY MODELING HUMAN P...

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11TH CONF ON intelligent robots AND computer vision : algorithms, techniques, AND ACTIVE vision

作者： TOLG, S Ruhr Univ. Bochum 1 Federal Republic of Germany

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.

关键词： computer vision

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EXTRACTING A SYMBOLIC OBJECT DESCRIPTION FROM GRAY-LEVEL IMAGES USING A KALMAN-FILTER-BASED CONTOUR TRACER COMBINED WITH A COMPLEX EDGE-OPERATOR

EXTRACTING A SYMBOLIC OBJECT DESCRIPTION FROM GRAY-LEVEL IMA...

引用

11TH CONF ON intelligent robots AND computer vision : algorithms, techniques, AND ACTIVE vision

作者： BECKER, M LEY, D Univ. Siegen (Germany)

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).

关键词： Image segmentation computer vision technology Machine vision Robot vision robots Edge detection Image processing Mathematical modeling Computing systems Systems modeling

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PATTERN-RECOGNITION USING HILBERT-SPACE

PATTERN-RECOGNITION USING HILBERT-SPACE

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11TH CONF ON intelligent robots AND computer vision : algorithms, techniques, AND ACTIVE vision

作者： LIU, Y Savannah State College (United States)

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.

关键词： Fractal analysis computer vision technology Machine vision Robot vision robots Space operations computer programming Quantization Neural networks Pattern recognition

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ROBUST OBJECT DETECTION UNDER VARIOUS ENVIRONMENTAL-CONDITIONS

ROBUST OBJECT DETECTION UNDER VARIOUS ENVIRONMENTAL-CONDITIO...

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CONF ON intelligent robots AND computer vision 10 : algorithms AND techniques

作者： MAEDA, E ISHII, K NTT Human Interface Labs. (Japan)

ISBN: (纸本)0819407445

We present a method which is robust to environmental conditions, that detects the existence of space filling objects such as cars, people, etc. This method is unaffected by uniform or local variations in image brightness induced by lighting conditions, weather, shadow of other objects such as buildings, trees, clouds, etc. Moreover, it does not depend on the position, size, or background pattern of the regions, or the shapes and color of the objects to be detected. The method consists of the following processes. Normalize brightness of the target and the reference image using mean and variance of the brightness in respective regions. The reference image is the image which represents the background scene without objects and is taken from the same camera position as the target images. Calculate normalized principal component features from both sets of normalized image brightness. Use the features to construct a classifier by statistical learning. The proposed features are determined by the variance and covariance of brightness of both images. They are a better measure of the correspondence of two images than conventional features such as the correlation coefficient of image brightness or statistics calculated from a difference image. The method is applied to car detection in a parking lot. The experimental images were collected over a one year period under various conditions. At least 98% of the cars were always correctly detected. Application to moving-car detection and person detection in a hall are presented. Since the proposed algorithm for object detection is robust under various environmental conditions and is object independent, it is well suited to a wide range of facilities offering automatic surveillance, automatic counting, automatic recognition of scene situation, etc.

关键词： Detection and tracking algorithms Machine vision Environmental sensing robots Robot vision computer vision technology Mahalanobis distance Cameras Error analysis Infrared imaging

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