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.
This paper considers the control of two robotic manipulators jointly grasping-with no slippage-a rigid object. Two conflicting control problems are addressed: Adaptively apportioning the object load between the manipu...
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This paper considers the control of two robotic manipulators jointly grasping-with no slippage-a rigid object. Two conflicting control problems are addressed: Adaptively apportioning the object load between the manipulators and application of commanded interactive forces (or bias forces) to the object. By controlling the interactive forces, damage to the manipulators and/or the object can be prevented. Additionally, desired tension, torsion, or compression can be applied to the object. Adaptive load apportioning (sharing) increases the carrying capacity of the system by using possible mechanical advantage. The Hopfield net is used to minimize a quadratic energy function in the joint torques. The results are the optimal joint torques required to drive the load and supply the commanded bias force. Simulations are presented that show effective control of interactive forces while sharing the load in an optimal fashion.
Arranging the photosensitive elements of an imaging sensor in a log-polar grid automatically samples an image in a log-polar space. The Retina project is a chip with such a spatially varying layout that can produce th...
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Arranging the photosensitive elements of an imaging sensor in a log-polar grid automatically samples an image in a log-polar space. The Retina project is a chip with such a spatially varying layout that can produce the advantages of image processing in the new space at real-time speeds. The actual chip is a small part of a complete imaging system. The system is part of a class of imagers called foveal sensors, and these sensors have distinct and significant computational savings over conventional imagers, as many as 3-10 orders of magnitude improvement in processing time and memory. The design maintains a large region of high-resolution data, although it is still only a fraction of the total photosensitive area.
A recognition system, which represents object categories by properties which can be deduced by analysis of 3-D shape, has been implemented and tested using the category 'chair' for case study. Functional descr...
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A recognition system, which represents object categories by properties which can be deduced by analysis of 3-D shape, has been implemented and tested using the category 'chair' for case study. Functional description is used to recognize classes and identify subclasses of known categories of objects, even if the specific object has never been encountered previously. Interpretation of the functionality of an object is accomplished through qualitative reasoning about its shape. This is, to our knowledge, the first implemented system to explore the use of purely function-based representation (that is, no geometric or structural object model) to recognize 3-D objects. During the recognition process, evidence is gathered as to how well the functional requirements are met by the input structure. This paper is concerned with choosing the type of operators that will be used in the combination or accrual of the functional evidence. Three pairs of conjunctive and disjunctive operators are evaluated. Each pair is used in the recognition process of the 100 + test objects. The results of all tests run are compared and differences are discussed.
Range images incorporate 3-D surface coordinates of a scene and are well suited for a variety of vision applications. For tasks, such as 3-D object recognition, a representation of the object(s) present in the image i...
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Range images incorporate 3-D surface coordinates of a scene and are well suited for a variety of vision applications. For tasks, such as 3-D object recognition, a representation of the object(s) present in the image is derived and then matched with stored models to determine the object(s) identity. Surface based representation is the most widely used representation in range image analysis. To generate surface based representation of an object, a segmentation of the object into a number of surfaces is needed. In this paper we present an approach for the segmentation of range images into a number of surfaces that, in turn, can be used to generate surface based representation. The approach integrates both edge detection and region growing techniques to achieve the segmentation. We start by detecting jump edges. Jump edge map is processed and regions surrounded by jump edges are isolated. Next, fold edges are detected iteratively using normals and residual. Fold edge map is processed to obtain the final segmented image. Jump and fold edge maps are processed using a Bayesian approach. The apriori knowledge is modeled using Markov Random Field. For jump edges we have used a coupled line and depth process. For fold edges we have combined line, residuals and normals to process the fold edge map. The performance of the algorithm on a number of range images is presented.
The authors ask what is the complexity of computing equilibria for physically realizable analog networks like those of J.J. Hopfield (1984) and T.J. Sejnowski (1981) with arbitrary connectivity. It is shown that, if t...
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The authors ask what is the complexity of computing equilibria for physically realizable analog networks like those of J.J. Hopfield (1984) and T.J. Sejnowski (1981) with arbitrary connectivity. It is shown that, if the amplifiers are piecewise-linear, then such networks are instances of a game-theoretic model known as polymatrix games. Equilibria for the latter may be computed by vertex pivoting algorithms similar to the simplex method for linear programming, which are in practice of low order polynomial complexity. These algorithms appear to be the only ones both guaranteed to work and which are polynomial in practice. These results suggest that networks with few nonstable equilibria would be computationally attractive.< >
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