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检索条件"任意字段=Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3-D Methods"

共 113 条记录，以下是71-80 订阅

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ESTIMATION OF MOTION PARAMETERS USING BINOCULAR CAMERA CONFIGURATIONS 10

ESTIMATION OF MOTION PARAMETERS USING BINOCULAR CAMERA CONFI...

引用

CONF ON intelligent robots ANd computer vision 10 : neural, biological, and 3-d methods

作者： SUdHAKAR, R ZHUANG, HQ HALIYUR, P SHIEH, JY Florida Atlantic Univ. (United States)

ISBN: (纸本)0819407453

This paper is concerned with the estimation of rotational and translational motion parameters of planar and nonplanar object surfaces viewed with a binocular camera configuration. Possible application of this method includes autonomous guidance of a moving platform (AGVs) via imaging, and segmentation of moving objects by the use of information concerning motion and structure. Assuming that the brightness of a moving patch is invariant to small motion, a pair of equation, involving the object depth and the spatio-temporal derivatives of left and right images, is developed. The depth map is estimated by matching and triangulating contours in the stereo images and interpolating over internal regions. Considering the entire image points, a system linear equations in the unknown motion parameters is obtained and solved using the singular value decomposition technique. The algorithm was tested for simulated images as well as real images. Accurate results were obtained in case of simulated images. In the case of real image, the errors obtained with different levels of image spatial sampling, were within reasonable limits.

关键词： Cameras Motion estimation computer vision technology Machine vision Robot vision robots Optical flow device simulation computer simulations Stereoscopy

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ARTIFICIAL neural netWORK MOdELS FOR TEXTURE CLASSIFICATION VIA - THE RAdON-TRANSFORM 92

ARTIFICIAL NEURAL NETWORK MODELS FOR TEXTURE CLASSIFICATION ...

引用

CONF ON intelligent robots ANd computer vision 10 : neural, biological, and 3-d methods

作者： KULKARNI, Ad BYARS, P University of Texas at Tyler

ISBN: (纸本)0819407453

Texture is generally recognized as being fundamental to perception. A taxonomy of problems encountered within the context of texture analysis could be that of classification/discrimination, description, and segmentation. In this paper we suggest a novel artificial neural network (ANN) architecture for features extraction and texture recognition. There is evidence which suggests that the analysis of stimulus by visual system might involve a set of quasi-independent mechanisms called channels which could be conveniently characterized in the spatial frequency domain. In our model we use an FT feature space with angular and radial bins that characterize spatial domain filters to extract features. The extracted features are then used as input for the recognition stage. In order to evaluate the 2-d FT coefficients we use the Radon transform. The usage of the Radon transform simplifies the ANN model significantly. We suggest an electronic implementation of the ANN model for feature extraction, using a Connected network Adaptive ProcessorS (CNAPS) chip designed by Adaptive Solutions Inc. We also develop software to simulate the ANN model with the Radon transform. We use a three stage back-propagation network as a classifier. We have used ten different texture patterns to test our ANN model.

关键词： Radon transform Feature extraction Fourier transforms Artificial neural networks computer vision technology detection and tracking algorithms Machine vision Robot vision robots Visual process modeling

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FAST 2-d HARTLEY TRANSFORM IN 3-d OBJECT REPRESENTATION ANd RECOGNITION 10

FAST 2-D HARTLEY TRANSFORM IN 3-D OBJECT REPRESENTATION AND ...

引用

CONF ON intelligent robots ANd computer vision 10 : neural, biological, and 3-d methods

作者： LEE, dJ RAMIREZ, M MITRA, S Texas Tech Univ. (United States)

ISBN: (纸本)0819407453

In image processing or computer vision, Fourier transform is widely used for frequency- domain analysis. However, Hartley transform can be a very good substitute for the more commonly used Fourier transform when the real input data are concerned. A two-dimensional butterfly algorithm for fast Fourier transform has been modified to calculate the Hartley transform faster than could be done using row-column decomposition. This paper presents three different frequency-domain registration techniques, power cepstrum, complex cepstrum and phase correlation. These techniques not only are capable of precise registration of images but also lead to three-dimensional (3-d) reconstruction of real objects by finding the corresponding points and disparities of an image pair. Use of these recently developed techniques allows one to obtain a precise displacement between two images and a quantitative measurement of 3-d information in a relatively faster computation time. Hartley transform can be used to implement all of these three techniques instead of using complex number computation required by Fourier transform. An additional 35 percent saving of the computation time is achieved by implementing the two-dimensional butterfly algorithm for computing Hartley transform. This reduction in computation time makes the use of Hartley transform in frequency-domain analysis more attractive.

关键词： Fourier transforms Image registration computer vision technology Machine vision 3d image processing Robot vision robots Silicon Image processing Image restoration

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EARLY PERCEPTION ANd STRUCTURAL IdENTITY - neural IMPLEMENTATION 10

EARLY PERCEPTION AND STRUCTURAL IDENTITY - NEURAL IMPLEMENTA...

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CONF ON intelligent robots ANd computer vision 10 : neural, biological, and 3-d methods

作者： LIGOMENIdES, PA Univ. of Maryland and CAELUM Research Corp. (United States)

ISBN: (纸本)0819407453

It is suggested that there exists a minimal set of rules for the perceptual composition of the unending variety of spatio-temporal patterns in our perceptual world. driven by perceptual discernment of "sudden change" and "unexpectedness", these rules specify conditions (such as co-linearity and virtual continuation) for perceptual grouping and for recursive compositions of perceptual "modalities" and "signatures". Beginning with a smallset of primitive perceptual elements, selected contextually at some relevant level of abstraction, perceptual compositions can graduate to an unlimited variety of spatiotemporal signatures, scenes and activities. Local discernible elements, often perceptually ambiguous by themselves, may be integrated into spatiotemporal compositions, which generate unambiguous perceptual separations between "figure" and "ground". The definition of computational algorithms for the effective instantiation of the rules of perceptual grouping remains a principal problem. In this paper we present our approach for solving the problem of perceptual recognition within the confines of one-d variational profiles. More specifically, concerning "early" (pre-attentive) recognition, we define the "structural identity of a k-norm, k ∈ K,"--SkId--as a tool for discerning and locating the instantiation of spatiotemporal objects or events. The SkId profile also serves a s a reference coordinate framework for the "perceptual focusing of attention" and the eventual assessment of resemblance. neural network implementations of pre-attentive and attentive recognition are also discussed briefly. Our principles are exemplified by application to one-d perceptual profiles, which allows simplicity of definitions and of the rules of perceptual composition.

关键词： Image segmentation Sensors robots computer vision technology Machine vision Robot vision Visual process modeling Pattern recognition detection and tracking algorithms neural networks

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TRINOCULAR CORRESPONdENCE FOR PARTICLES ANd STREAKS 10

TRINOCULAR CORRESPONDENCE FOR PARTICLES AND STREAKS

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CONF ON intelligent robots ANd computer vision 10 : neural, biological, and 3-d methods

作者： KEARNEY, JK Univ. of Iowa (United States)

ISBN: (纸本)0819407453

This paper examines the problem of matching corresponding object points in three views of a scene. The matching problem is a critical step in the recovery of 3-dimensional position by stereo image processing and has important application in the analysis of particle distributions and fluid flows. We introduce an algorithms that iteratively searches for all possible pairwise matches using trinocular consistency constraints. The algorithm is shown to be equivalent to a search for edges that belong to all perfect matchings in a bipartite graph that links consistent matches between two views. Three such problems are solved in parallel. An attractive property of the matching algorithm is its graceful degradation in response to image distortion and modeling error. Although measurement errors may prevent successful matching, wrong matches can almost always be avoided if the error in the image position of a particle can be bounded. Thus, noise can cause a loss of acuity but should not cause the introduction of gross misinformation that could result from incorrect matching. Experiments with synthetic particle images demonstrate that this approach can lead to significantly greater numbers of matches than algorithms that match in a single direction.

关键词： Particles Cameras robots computer vision technology Machine vision Robot vision distortion Image processing 3d image processing Chromium

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RECOGNITION OF A TRANSLATIONAL PULSE IN NOISE 10

RECOGNITION OF A TRANSLATIONAL PULSE IN NOISE

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CONF ON intelligent robots ANd computer vision 10 : neural, biological, and 3-d methods

作者： PARTEN, ME KWAN, YM ULUTAS, M dAVIS, JP Texas Tech Univ. (United States) Naval Air Development Ctr. (United States)

ISBN: (纸本)0819407453

One of the basic problems in pattern recognition is the detection of a pattern in noise. This problem becomes particularly difficult if the pattern varies in position and size. A system necessary to achieve this result can be modeled in a number of different ways. One currently popular approach is to use a neural network.(1,2) The advantage to using a neural network is that once the basic structure is assumed the characteristics of the network, described by it's weights, can then be learned. The learning or training process involves developing a training set of known inputs and outputs for the system and adapting the internal weights of the network so that the inputs will yield the desired outputs. The weights are adjusted to minimize the error, according to some criteria, between the actual outputs and the desired outputs. Most neural networks are composed of first order terms, that is, z = f{ w0 + wij xj } where xj are the inputs, z are the first level (or hidden) outputs, w are weight terms and the functional relationship is normally a sigmoid function for inputs between zero and one. Usually, there are at least two levels of this type. In other words, the output, yi, would be given by yk = f{ u0 + uik Zj } where y are the final outputs, u are the weights and the other terms are as before. This type of network is trained using a back-propagation technique. neural networks offer hope in the possible solution of detecting an object in noise by proper training of the network to recognize the characteristics of the object and ignoring the noise. Unfortunately, most neural networks cannot be trained to detect an object that appears in different positions. In other words, most neural networks are not translationally invariant. However, some special higher order neural networks have been shown to posses translational invariance

关键词： neural networks Target detection computer vision technology Machine vision Robot vision robots Target recognition Environmental sensing Error analysis Multilayers

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neural netWORK BASEd MOTION STEREO methods 10

NEURAL NETWORK BASED MOTION STEREO METHODS

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CONF ON intelligent robots ANd computer vision 10 : neural, biological, and 3-d methods

作者： ZHOU, YT HNC Inc. (United States)

ISBN: (纸本)0819407453

This paper presents neural network based lateral and longitudinal motion stereo methods. Lateral motion stereo infers depth information from a lateral motion. Existing lateral motion stereo algorithms use either a Kalman filter or recursive least square algorithm to update the disparity values. due to the unmeasurable estimation error, the estimated disparity values at each recursion are unreliable, yielding a noisy disparity field. Instead of updating the disparity values, we recursively update the bias inputs of the network. The disparity field is then computed by using a neural network. Since the recursive algorithm implements the matching algorithm only once, and the bias input updating scheme can be accomplished in real time, a vision system employing such an algorithm is feasible. For the purpose of handling batch data, we have also designed a batch algorithm. The batch algorithm integrates information from all images by embedding them into the bias inputs of the network. Then a static matching procedure is used to compute the disparity values. Longitudinal motion stereo infers depth information from a forward or backward motion. Existing longitudinal stereo algorithms have some problems associated with the location of the focus of expansion (FOE), and with the camera and surface orientations. Instead, our approach allows the camera to move along its optical axis forward or backward, requires no information on the FOE, and makes no assumption about the object surface. The algorithm uses a Gabor correlation operator to extract image features and employs the neural network to compute the disparity field based on the Gabor features. It produces multiple dense disparity fields and recovers the depth map very efficiently.

关键词： Cameras Robot vision Error analysis Imaging systems neural networks computer vision technology Filtering (signal processing) Machine vision Computing systems Evolutionary algorithms

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LABELING SCHEME FOR SURFACE dETECTION IN 3-d IMAGES 10

LABELING SCHEME FOR SURFACE DETECTION IN 3-D IMAGES

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CONF ON intelligent robots ANd computer vision 10 : neural, biological, and 3-d methods

作者： AdISESHAN, P FABER, TL Univ. of Texas Southwestern Medical Ctr./Dallas (United States) Digital Design (United States)

ISBN: (纸本)0819407453

This work addresses the problem of finding surfaces in 3-d images. This problem involves segmentation and parameterization. The solution method we propose is designed to answer the following questions: (1) Is a point (x,y,z) in 3-space a surface point? (2) If so, what is the surface normal at that point, and (3) What are the neighboring voxels in the u and v directions on the surface? We use a labeling scheme to define a surface element, at a point, in (u, v) directions. By restricting the neighborhood volume at a point, we have a small set of labels in u and v directions. A cost functional is set up, in terms of surface normal and labels at a point, following the calculus of variations methodology. Estimation of surface normal at a point depends on normals at neighboring points and label connectivity criteria defined. This also facilitates handling of surfaces that are not smoothly varying. The final solution is found by finding the set of normal and labels at every point that minimize the cost functional. Finally, we apply our method to find surfaces in 3-d synthetic and medical image data.

关键词： 3d image processing Image segmentation Chemical elements Medical imaging Single photon emission computed tomography Visualization Calculus computer vision technology Machine vision Heart

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PLANNING OF AN ACTIVE RANGE SENSOR STRUCTURE FOR POSE ESTIMATION OF 3-d REGULAR OBJECTS 10

PLANNING OF AN ACTIVE RANGE SENSOR STRUCTURE FOR POSE ESTIMA...

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CONF ON intelligent robots ANd computer vision 10 : neural, biological, and 3-d methods

作者： MARSZALEC, JA JARVILUOMA, M HEIKKILA, TA Technical Research Ctr. of Finland (Finland) Univ. of Oulu (Finland)

ISBN: (纸本)0819407453

In some 3-d object perception and pose estimation tasks an active range sensor structure is important for obtaining high accuracy results. In this paper an active triangulation photoelectric range sensor based on the electronic scanning of an integrated LEd array is described and the sensor structure planning for pose estimation of 3-d regular objects is discussed. A simulation of the effect on the accuracy of a 3-d object's pose estimation of the location of the measured points on the object's surface constitute the background for the planning. Taking into consideration the results of the theoretical analysis, an approach to design the desired sensor for pose estimation of a certain set of objects is presented. The whole sensor structure planning process is illustrated with an example of pose estimation of cylindrical objects. Benefits of the approach presented to a certain class of pose estimation tasks, which are met in real industrial environments, include decreasing the costs of the range data acquisition hardware and the time consumed for range data reduction.

关键词： Light emitting diodes Sensors Scanners Transmitters Robot vision Receivers Light sources Active sensors Electronics data acquisition

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APPLYING GEOMETRIC SENSOR ANd SCENE MOdELS FOR RANGE IMAGE UNdERSTANdING 10

APPLYING GEOMETRIC SENSOR AND SCENE MODELS FOR RANGE IMAGE U...

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CONF ON intelligent robots ANd computer vision 10 : neural, biological, and 3-d methods

作者： WALKER, EL Rensselaer Polytechnic Institute (United States)

ISBN: (纸本)0819407453

For a robot to operate intelligently under sensor control in the real world, it must interpret its sensory inputs to create a model of its environment. Because the data is generally incomplete and errorful, additional knowledge must be applied to derive useful interpretations. One source of such data is knowledge about the sensors themselves; the ideal data returned from a sensor is a function of the environment and the pose and other parameters of the sensor (such as the focal length of a camera). The actual result is some corruption of the ideal result by noise or other error processes. In this work, the functions 'computed' by a range sensor are explicitly represented as geometric objects and relationships in the 3d FORM geometric reasoning system. Models for the output of independent edge segmentation and surface segmentation are described, and unified into an overall range-object model. In addition, visibility and projection relationships are used to predict the visibility of hypothesized object parts, and to refine the estimates of the cameras' positions. The resulting object description can be specialized, completed, and matched with other objects using existing 3d FORM capabilities.

关键词： Sensors 3d modeling Image understanding Cameras Environmental sensing Visibility Computing systems

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