Intelligent robotic systems should be capable of performing a range of complex tasks in unstructured and dynamic environments. This chapter discusses a number of important items associated with intelligent robots, rol...
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Intelligent robotic systems should be capable of performing a range of complex tasks in unstructured and dynamic environments. This chapter discusses a number of important items associated with intelligent robots, role of vision sensing in intelligent robotics, and architecture for the design of an intelligent robot. The chapter also describes the computational task hierarchy that governs a model-based robot vision system, and presents a detailed tutorial on two important components of this hierarchy: image segmentation and matching. The main capabilities that intelligent robotic systems should possess include ability for perception of their environment, planning of their actions, and execution of the planned action. The two important components of a model-based vision system are image segmentation and the matching module. A procedure that allows transformation of 2D image coordinates into 3D world coordinates is also discussed in the chapter. The architecture and control aspects of an integrated sensor-based robotic system consist of six modules: supervisor, task planner, perception, motor, user interface, and knowledge-base. Intelligent, sensor-driven systems pose many challenging basic-research problems from a broad range of scientific and technological disciplines; however, there are many important application areas where such systems would be of significant value and utility, including hazardous environments such as nuclear plants, underwater, and space as well as many industrial-automation tasks. Recent advancements in the VLSI, computer-architectures, sensor-and robot-hardware, and artificial-intelligence fields may aid to the development of robust, practical, and cost-effective intelligent robotic systems for a wide variety of applications.
We exploit the relationship between a general Gibbs random field and a mutually compatible Gibbs random field image, we investigate the possibility of approximating a general Gibbs random field by a mutually compatibl...
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We exploit the relationship between a general Gibbs random field and a mutually compatible Gibbs random field image, we investigate the possibility of approximating a general Gibbs random field by a mutually compatible Gibbs random field, and we study the consequences of such an approximation. This study is important, since, in some cases, various problems related to the modeling of images via Gibbs random fields may be simplified by restricting our interest to a special and convenient class of Gibbs random fields, the class of mutually compatible Gibbs random fields. A general analysis is presented, which results in some interesting conclusions about the desired relationship. We consider two approaches to the approximation problem. The first approach, which is more accurate, can be easily applied to the case of binary images. For nonbinary images this approach becomes cumbersome, since it requires the solution of a large system of nonlinear equations. The second approach, which is simpler but less accurate, is based on the direct relationship between the parameters of a general Gibbs random field and the parameters of a mutually compatible Gibbs random field. Simulation examples demonstrate various aspects of our analysis.
This paper describes an approach of feature extraction from range and intensity data for three-dimensional model-based target recognition systems. The range image of the object under study is acquired using a methodol...
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Conventional subband coding for image data compression uses two-dimensional (2-D) separable QMF banks in which the analysis and synthesis filters are composed of 1-D filters. Such an implementation produces a large si...
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We present a method for making accurate, optimally localized measurements of the fractal dimension of images modeled as locally fractal Brownian surfaces. Fractal Brownian surfaces are good models for the multiscale a...
The authors present a new Monte Carlo simulation technique for the estimation of the partition function of a general Markov random field (MRF), which results in unbiased, consistent and asymptotically efficient estima...
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The authors present a new Monte Carlo simulation technique for the estimation of the partition function of a general Markov random field (MRF), which results in unbiased, consistent and asymptotically efficient estimates. This technique gives extremely accurate results, as demonstrated by simulations. Use of more efficient algorithms can boost the performance and accuracy of the method, and yield more reliable estimates.< >
An algorithm for detecting neural processes in serial optical sections for use in an automated three-dimensional neural reconstruction system is presented. This parsimonious, nonlinear, psychophysically motivated algo...
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An algorithm for detecting neural processes in serial optical sections for use in an automated three-dimensional neural reconstruction system is presented. This parsimonious, nonlinear, psychophysically motivated algorithm addresses the problems specific to neural element detection and localization, viz., images with minimal resolution, operators with small spatial supports, highly curved, filamentous features, large variation in feature intensity profile, poor signal-to-noise ratio, and determination of depth without stereo. One first finds the magnitude and orientation of the maximum intensity second directional derivative. A family of curves is locally fitted to these data, and the projections of the data on the curve family are found. If a pixel lies on a curve with sufficient total projection, it is labeled with the magnitude, orientation, curvature, spatial extent, and element displacement. Depth is interpolated from the spatial extent data for corresponding neighborhoods in three adjacent (in depth) images by using an approximation to the depth-dependent optical point spread function. Experimental results using photomicrographs of cat visual cortex are presented.< >
The paper presents a general framework for the analysis of time sequences. Features extracted include speed, acceleration and disparity/depth. The method uses spatio-temporal filtering in a hierarchical structure. Syn...
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The paper presents a general framework for the analysis of time sequences. Features extracted include speed, acceleration and disparity/depth. The method uses spatio-temporal filtering in a hierarchical structure. Synthetic and real world examples are included.< >
The authors develop a path metric for sequential search based on the linear model. The metric forms the heart of an edge-linking algorithm that combines edge elements enhanced by an optimal filter. From a starting nod...
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The authors develop a path metric for sequential search based on the linear model. The metric forms the heart of an edge-linking algorithm that combines edge elements enhanced by an optimal filter. From a starting node, transitions are made to the goal nodes by a maximum likelihood metric. This metric requires only local calculations on the search space and its use in edge linking provides more accurate results than other linking techniques.< >
Hierarchical visual pattern image coding (HVPIC) is a highly successful, low-computational-cost, pyramidal image coding scheme which utilizes human visual system (HVS) properties to achieve very low bit rates while ma...
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Hierarchical visual pattern image coding (HVPIC) is a highly successful, low-computational-cost, pyramidal image coding scheme which utilizes human visual system (HVS) properties to achieve very low bit rates while maintaining a good perceived image quality. A description is presented of an extension of HVPIC, termed adaptive VPIC (AVPIC), which results in a decreased bit rate for most images with very little additional computation and virtually no further image degradation. After coding but before transmission, the encoder analyzes what it has done, and reduces the transmission bit rate by eliminating high-resolution information about low-resolution portions of the image. The method is adaptive in the sense that the bit rate is locally adjusted to match the level of detail present in an image region.< >
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