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

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Obstacle detection and terrain characterization using optical flow without 3-D reconstruction

Obstacle detection and terrain characterization using optica...

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intelligent robots and computer vision xI: algorithms, techniques, and Active vision

作者： Young, Gin-Shu Hong, Tsai-Hong Herman, Martin Yang, Jackson C. Natl. Inst. of Standards and Technology and Univ. of Maryla Gaithersburg MD USA

ISBN: (纸本)0819410268

For many applications in computer vision, it is important to recover range, 3-D motion, and/or scene geometry from a sequence of images. However, there are many robot behaviors which can be achieved by extracting relevant 2-D information from the imagery and using this information directly, without recovery of such information. In this paper, we focus on two behaviors, obstacle avoidance and terrain navigation. A novel method of these two behaviors has been developed without 3-D reconstruction. This approach is often called purposive active vision. A linear relationship, plotted as a line and called a reference flow line, has been found. The difference between a plotted line and the reference flow line can be used to detect discrete obstacles above or below the reference terrain. For terrain characterization, slopes of surface regions can be calculated directly from optical flow. Some error analysis is also done. The main features of this approach are that (1) discrete obstacles are detected directly from 2-D optical flow, no 3-D reconstruction is performed;(2) terrain slopes are also calculated from 2- D optical flow;(3) knowledge about the terrain model, camera-to-ground coordinate transformation, or vehicle (or camera) motion is not required;(4) the error sources involved are reduced to a minimum, since the only information required is a component of optical flow. An initial experiment using noisy synthetic data is also included to demonstrate the applicability and robustness of the method.

关键词： Mobile robots

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Boundary-based shape normalization technique

Boundary-based shape normalization technique

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intelligent robots and computer vision xI: algorithms, techniques, and Active vision

作者： Leu, Jia-Guu Wayne State Univ. Detroit MI USA

ISBN: (纸本)0819410268

When a camera's center axis is not parallel to the surface normal of a planar object, the perceived shape of the object will be skewed. Most existing shape analysis methods are sensitive to such shape distortions. That is, if a shape is skewed due to non-orthographic projection, it may not be correctly recognized. In this paper we present a shape normalization process which neutralizes such shape skewing effects. In our case, a perceived shape is represented by a list of corner points along its boundary. We first compute the six lower moments of the shape from its boundary. Then these moments are used to compute the shape's center location, orientation, and maximum and minimum moments of inertia. To normalize the shape we first translate the shape's center to the origin. Next we rotate the shape to align its major axis with the x-axis. Then we expand the shape along its minor axis to neutralize shape skewing. Lastly, we scale the size of the shape so it has a standard moment of inertia. The suggested method can be used as a preprocessing step for any planar shape analysis method which is sensitive to shape skewing, shape size change, and/or shape translation. Since the moments are computed from the shape's boundary instead of from all its interior pixels, the method is also efficient. Several experimental results are given to show the effectiveness of the approach.

关键词： computer vision

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Multiresolution stroke sketch adaptive representation and neural network processing system for gray-level image recognition

Multiresolution stroke sketch adaptive representation and ne...

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intelligent robots and computer vision xI: Biological, Neural Net, and 3-D Methods

作者： Meystel, Alexander Rybak, Ilya A. Bhasin, Sanjay Meystel, M. Drexel Univ. Philadelphia PA USA

ISBN: (纸本)0819410276

This paper describes a method for multiresolutional representation of gray-level images as hierarchial sets of strokes characterizing forms of objects with different degrees of generalization depending on the context of the image. This method transforms the original image into a hierarchical graph which allows for efficient coding in order to store, retrieve, and recognize the image. The method which is described is based upon finding the resolution levels for each image which minimizes the computations required. This becomes possible because of the use of a special image representation technique called Multiresolutional Attentional Representation for Recognition, based upon a feature which the authors call a stroke. This feature turns out to be efficient in the process of finding the appropriate system of resolutions and construction of the relational graph. Multiresolutional Attentional Representation for Recognition (MARR) is formed by a multi-layer neural network with recurrent inhibitory connections between neurons, the receptive fields of which are selectively tuned to detect the orientation of local contrasts in parts of the image with appropriate degree of generalization. This method simulates the 'coarse-to-fine' algorithm which an artist usually uses, making at attentional sketch of real images. The method, algorithms, and neural network architecture in this system can be used in many machine-vision systems with AI properties;in particular, robotic vision. We expect that systems with MARR can become a component of intelligent control systems for autonomous robots. Their architectures are mostly multiresolutional and match well with the multiple resolutions of the MARR structure.

关键词： computer vision

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Calibration of the spherical pointing motor

Calibration of the spherical pointing motor

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intelligent robots and computer vision xI: algorithms, techniques, and Active vision

作者： Bederson, Benjamin B. Wallace, Richard S. Schwartz, Eric L. Bell Communications Research New York NY USA

ISBN: (纸本)0819410268

We have built a new miniature pan-tilt actuator, the spherical pointing motor (SPM). The SPM is an absolute positioning device, designed to orient a small camera sensor in two degrees of rotational freedom. The basic idea is to orient a permanent magnet to the magnetic field induced by three orthogonal coils by applying the appropriate ratio of currents to the coils. The function describing the relation between the coil currents and the resultant motor position can be calculated, but it is not very accurate as the actual coils do not exactly satisfy the assumptions made in these calculations. The motor must be calibrated to find the coil currents accurately. This paper describes a procedure for automatic calibration of the SPM. It is based on image feedback from a camera returning space-variant images, mounted on the rotor of the motor. It assumes that a calibrated image sensor and lens are used, i.e., that it is known how many degrees each pixel subtends. It also assumes that the camera rotates about its focal point. The calibration algorithm uses a scene of black dots on a white background. For each motor position that is to be calibrated, the algorithm moves the motor approximately to that position using the calculated currents. The algorithm analyzes the image, and uses the position of the relevant dot to calculate the actual position of the motor. It then associates this position with the coil currents and stores it in a look-up-table. Finally, we interpolate between calibrated points to move to other positions.

关键词： computer vision

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Behaviours for active object recognition 12

Behaviours for active object recognition

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intelligent robots and computer vision xII: algorithms and techniques 1993

作者： Wilkes, David Tsotsos, John Department of Computer Science University of Toronto 6 King's College Rd. TorontoONM5S1A4 Canada

The concept of active object recognition is introduced and a proposal for its solution is described. It is argued that single-view object recognition is fraught with problems, mainly due to viewpoint-related ambiguities, occlusions and coincidences. Recognition which is active, that is, that has the ability to vary viewpoint according to the interpretation status, is both more viable and more closely related to the manner in which humans recognize different objects. An active system may be achieved via a simple modification of the recognition hardware. The camera is mounted on the end of a robot arm on a mobile base. The system exploits the mobility of the camera by using low-level image data to drive the camera to a special viewpoint with respect to an unknown object. From such a viewpoint, the object recognition task is reduced to a two-dimensional pattern recognition problem. This paper describes the behaviour-based approach to camera motion, that ensures robust acquisition of special views of the object to be recognized. © 1993 SPIE.

关键词： Object recognition

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vision system based on a classification connexionist algorithm 12

Vision system based on a classification connexionist algorit...

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intelligent robots and computer vision xII: algorithms and techniques 1993

作者： Oliver, Gabriel A. Piera, Nüria Dept. de Matemàtiques i Informàtica Universitat de les Illes Balears Cra. de Validemossa km 7.5 Palma07071 Spain Dept. de Matemàtiques Universitat Politècnica de Catalunya c/Pau Gargallo 5 Barcelona08028 Spain

The representation and recognition of object shapes are important tasks of image analysis and computer vision systems. In the last years, a lot of effort has been made in these fieds and there are many applications in object recognition, scene analysis, object tracking or stereo correspondence. The way used to modelize and treat knowledge, and its uncertainty, is a fact that can deeply mark out some systems from others. In this work, we assume that the degree of confidence in the information provided by a TV camera is a value composed of different aspects of the original scene and image. This fact affects all the vision system, from the acquisition step to the classification or rejection of the objects observed. The vision system presented in this paper is a simple but efficient system for the classification of plane objects. Images are recorded by a TV camera. After carrying out the contour detection of the objects present in the scene, we go through a process of obtention of some singular points on these contours with which the descriptors of the objects will be built up. Due to the loss of information introduced in the digitalization and in the segmentation of the image, some uncertainty is introduced in the process that has to be taken into account in the classification process. In that sense, it is interesting to use a classification algorithm that can accept some kind of imprecision in the representation of the objects to be classified. The classification algorithm used is based on an incremental clustering methodology termed LAMDA (Learning Algorithm for Multivariate Data Analysis). Such a system takes into consideration the interaction of two processes, learning and recognition. It gives a representation of each class and enables us, if it is deemed necessary, to look for a specification or generalization of previously constructed classes. Examples and experimental results are presented to illustrate the performance of the system. © 1993 SPIE.

关键词： Classification (of information)

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Application of a single-instruction computer to dilation and erosion of gray-valued images

Application of a single-instruction computer to dilation and...

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intelligent robots and computer vision xI: Biological, Neural Net, and 3-D Methods

作者： Laplante, Phillip A. Fairleigh Dickinson Univ. West Milford NJ USA

ISBN: (纸本)0819410276

In this paper, the single instruction architecture is used to construct circuitry to perform dilation and erosion of gray valued images, where the gray values are discrete but limited only by the number of bits chosen for the binary encoding. In addition, methods for minimizing the number of cells needed, using basic digital techniques, are discussed. While others have constructed architectures for gray valued dilation and erosion, these are based on non- homogeneous circuits, and typically use Umbra transformations to handle the gray values, rather than binary encoding. Finally, it is shown that the half-adder elements used in the single instruction architecture can easily be replaced with uniform multiplexer cells in deference to the McCulloch-Pitts model of the neuron. This analogy between the single instruction architecture and the neuronal construction of the brain is intentional.

关键词： computer vision

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Prediction models from CAD models of 3-D objects

Prediction models from CAD models of 3-D objects

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intelligent robots and computer vision xI: Biological, Neural Net, and 3-D Methods

作者： Camps, Octavia I. The Pennsylvania State Univ. University Park PA USA

ISBN: (纸本)0819410276

In this paper we present a probabilistic prediction based approach for CAD-based object recognition. Given a CAD model of an object, the PREMIO system combines techniques of analytic graphics and physical models of lights and sensors to predict how features of the object will appear in images. In nearly 4,000 experiments on analytically-generated and real images, we show that in a semi-controlled environment, predicting the detectability of features of the image can successfully guide a search procedure to make informed choices of model and image features in its search for correspondences that can be used to hypothesize the pose of the object. Furthermore, we provide a rigorous experimental protocol that can be used to determine the optimal number of correspondences to seek so that the probability of failing to find a pose and of finding an inaccurate pose are minimized.

关键词： computer vision

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Automated recognition and precise estimation of orientation of objects for industrial applications

Automated recognition and precise estimation of orientation ...

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intelligent robots and computer vision xI: algorithms, techniques, and Active vision

作者： Orbert, Curt L. Bengtsson, Ewert W. Nordin, Bo G. Uppsala Univ. Glunten Uppsala Sweden

ISBN: (纸本)0819410268

We present a solution to a common problem in industrial machine vision, to identify and estimate the orientation of touching mechanical parts on a plane surface. The algorithm is based on watershed segmentation and can handle cases where objects touch. After an initial thresholding step, we extract the edges of the binary image, the outer edge as well as edges around holes inside the object. Then we use a distance transformation to create a distance map, i.e., an image where each pixel value represents the distance to the nearest edge pixel. The watershed algorithm is applied on the distance map and we get an image where some objects may be segmented into several parts. For every segment we calculate the center of gravity for its surrounding edge pixels. The different centers of gravity are enough for estimating the orientation of objects that have been segmented into more than one segment. By also calculating the center of gravity for holes of the object and using them in the same way we can estimate the orientation of objects having holes. To recognize the mechanical parts we use the distances between the center of gravity of its segments and holes together with the greatest maximum of the distance map that we find inside each of them. We also calculate the length of the peripheries of the segments and use them to distinguish the objects. We can perhaps recognize, and certainly locate, but not estimate the rotation of the mechanical parts that consist of only one segment without holes. For those objects we construct a circle around the center of gravity with the corresponding greatest maximum as radius. We collect the values of the distance map on this circle line and plot them as a function of the angle to the horizontal axis. We can identify the maxima and minima of this function, from which we estimate the rotation of the object. This information can also be used to identify the object. For overall control algorithm we are using fuzzy logics. As a final step to

关键词： computer vision

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Phase method for binocular vergence control and depth reconstruction

Phase method for binocular vergence control and depth recons...

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intelligent robots and computer vision xI: Biological, Neural Net, and 3-D Methods

作者： Theimer, Wolfgang M. Mallot, Hanspeter A. Toelg, Sebastian Ruhr Univ. Bochum Ger

ISBN: (纸本)0819410276

We present a technique to guide vergence movements for an active stereo camera system and to construct dense disparity maps. Both processes are described in the same theoretical framework based on phase differences in complex Gabor filter responses, modelling receptive field properties in the visual cortex. While the camera movements are computed with coarse spatial resolution input images, disparity calculation uses finer resolutions in a scale space. The correspondence problem is solved implicitly by restricting the disparity range around zero disparity to the filter kernel sizes (Panum's area in the human visual system). The method contrasts to matching algorithms - that require an explicit search for correspondence - and to correlation, needing a maximum detection in the correlation function. The vergence process is interpreted as a mechanism to minimize global disparity, thereby setting a 3D region of interest for subsequent disparity detection. This mall volume is centered around the fixation point where both optical axes intersect. Additionally it produces a scalar distance measure via vergence angles and camera base. The disparity map represents smaller local disparities as an important cue for depth perception. The vergence control works in a real-time feedback loop. Quantitative results are presented.

关键词： computer vision

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