A learning procedure is described for the recognition of 3D industrial objects from 2d images. It is assumed that the objects are solid and have well defined edges and that viewpoint and lighting are well defined, but...
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A learning procedure is described for the recognition of 3D industrial objects from 2d images. It is assumed that the objects are solid and have well defined edges and that viewpoint and lighting are well defined, but that there is no information available on the orientation distribution of future objects to be classified. The presented learning procedure covers all orientations by an initial sampling, detects gaps and deletes superfluous orientations. An example is presented.
The classification of complicated 2-D shapes such as cracks, fractures and the abnormal patterns found on the surfaces of materials is very important in automated surface inspection. As a method of facilitating the cl...
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The classification of complicated 2-D shapes such as cracks, fractures and the abnormal patterns found on the surfaces of materials is very important in automated surface inspection. As a method of facilitating the classification of these shapes, we report a strategy of extracting the hierarchical structures from these complex patterns using branch cutting, boundary analysis, and others. Experimental results are given with random statistical patterns and with the images taken from the surfaces of some materials.
To avoid the excessive computation of testing all combinations of feature matches, groups of model features can be arranged in an index space offline (hashed). Ideally, each image group should index into the space and...
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To avoid the excessive computation of testing all combinations of feature matches, groups of model features can be arranged in an index space offline (hashed). Ideally, each image group should index into the space and find only those model groups that could have formed that image group. We prove an unexpected tight lower bound on the space required for such an indexing scheme for point features, 3D models, and 2D images, and consider some implementation issues.
Individual facial features such as the eyes or nose may not be as important to human face recognition as the overall pattern capturing a more holistic encoding of the face. This paper describes 'face space', a...
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Individual facial features such as the eyes or nose may not be as important to human face recognition as the overall pattern capturing a more holistic encoding of the face. This paper describes 'face space', a subspace of the space of all possible images, which can be described as linear combinations of a small number of characteristic face-like images. The construction of face space and its use in the detection and identification of faces is explained in the context of a working face recognition system. The effects of illumination changes, scale, orientation, and the image background are discussed.
In this paper, the ability of the retina to detect motion in the retinal peripheral visual field is emulated. The process of emulation utilizes a special receptive field defined in the spatial and temporal domains. Th...
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In this paper, the ability of the retina to detect motion in the retinal peripheral visual field is emulated. The process of emulation utilizes a special receptive field defined in the spatial and temporal domains. The simulation studies on this emulated peripheral visual field show that it is possible to acquire certain robust abilities in artificial systems which are inherent in their corresponding biological processes. Abilities such as noise suppression were evident in the emulated motion detection system presented in this paper.
In previous work by ourselves and others, it has been argued that the best algorithm for fusing multiple fixations of the same scene is to simply choose the maximum resolution information available at each position in...
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In previous work by ourselves and others, it has been argued that the best algorithm for fusing multiple fixations of the same scene is to simply choose the maximum resolution information available at each position in the reconstructed scene. We can show, under simple assumptions of pixel distributions, that this algorithm is indeed optimal in a least-squared-error sense. In the presence of noise, however, optimality no longer holds;a certain degree of averaging of lower resolution information is required to obtain optimal reconstruction. We also present empirical demonstrations of fused images using maximum-resolution and averaged blending techniques, and we illustrate the effects of noise on the quality of reconstruction.
This paper describes a way of applying statistical estimation techniques to the model matching problem of computervision. A detailed metrical object model is assumed. A simple MAP model matching method is described t...
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This paper describes a way of applying statistical estimation techniques to the model matching problem of computervision. A detailed metrical object model is assumed. A simple MAP model matching method is described that captures important aspects of recognition in controlled situations. A probabilistic model of image features is combined with a simple prior on the pose and feature interpretations to yield a mixed objective function. Extremizing the objective function yields an optimal matching between model and image features. Good models of feature uncertainty allows for robustness with respect to inaccuracy in feature detection. Additionally, the relative likelihood of a feature arising from the object or the background can be evaluated in a rational way. The parameters that appear in the probabilistic models may easily be derived from images in the application domain. The objective function takes a particularly simple form when feature deviations are modeled by Normal densities and the projection model is linear. Several linear projection and feature models are discussed. Evidence is provided to show that Normal feature deviation models can be appropriate for computervision matching problems. Relation to other work and possible extensions and application areas are discussed.
A system for identifying complex space objects from a sequence of wideband radar images is presented in this paper. The system is referred to as the Complex Space Object Recognition System (CSORS) and uses a data-driv...
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A system for identifying complex space objects from a sequence of wideband radar images is presented in this paper. The system is referred to as the Complex Space Object Recognition System (CSORS) and uses a data-driven approach to object recognition. The input to the system is a time sequence of range/Doppler radar images of an object in orbit as the object passes overhead. The system first processes the individual images to improve the signal-to-noise-ratio and then further processes each image to derive a set of features. The sequence of feature sets for each pass of the object is then processed to produce a three-dimensional wireframe of the object. Finally, a symbolic model representing the object is generated from the wireframe. This model is matched against a library of models and appended to the library if a match is not found.
We look at structure of the fuzzy logic controller from the point of view of a function relating input to output. This view in turn can be generalized to that of looking at the fuzzy logic controller as a mathematical...
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We look at structure of the fuzzy logic controller from the point of view of a function relating input to output. This view in turn can be generalized to that of looking at the fuzzy logic controller as a mathematical relationship. In this view the problem of finding fuzzy logic controller outputs becomes equivalent to solving mathematical relationships. Using this motivation we suggest an alternative approach to the calculation of the output of fuzzy logic controllers. This approach is based upon the use of level sets and is called the level set method. This method provides a very efficient way of calculating fuzzy logic controller outputs that is easily implemented in hardware.
For practical use of robotics in space machine vision is required to automatically locate objects in order to guide an autonomous control system or to assist a human teleoperator. The vision task is facilitated by pla...
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ISBN:
(纸本)0819404543
For practical use of robotics in space machine vision is required to automatically locate objects in order to guide an autonomous control system or to assist a human teleoperator. The vision task is facilitated by placing optical targets or markings in advance on the objects to be located. We have experimented with techniques to locate such optical targets from monocular images. These include camera calibration determination of the camera to tool endpoint transformation feature extraction and pose estimation. This paper describes our experiences with these techniques as they were implemented in an integrated robot-vision system to perform tasks characteristic of space construction and servicing. The accuracies of the target location techniques were also verified separately using an optical bench and positioning devices. The usefulness of the optical target location system was demonstrated in the autonomous control of two Cincinnati Milacron T3-726 robot arms. 1 .
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