The minirhizotron technique has provided agricultural scientists the opportunity of observing rhizosphere activities without destroying root structures. Nonetheless, the laborious analysis of the data still prohibits ...
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The minirhizotron technique has provided agricultural scientists the opportunity of observing rhizosphere activities without destroying root structures. Nonetheless, the laborious analysis of the data still prohibits its wide applications. Advanced image understanding techniques are needed to derive satisfactory descriptions of plant root networks in an efficient and robust way. The paper presents a plant root image analysis system designed as a blackboard architecture with a hierarchy of data abstractions. Important properties of plant roots are used throughout the processing and multiple sources of information are combined to resolve uncertainties in image interpretation. Experimental results from some stages of the research are given which support the overall processing scheme.< >
Scanning Auger microscopy (SAM) and AES investigations of the segregation of non-metal impurity atoms (sulphur, nitrogen, carbon and phosphorus) in polycrystalline high-purity (>99.99%) α-iron samples were perform...
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A theoretical framework based on attributed elementary programmed graph grammars (GRAPE grammars) that turns out to be equally well-suited for describing network dynamics, weight learning algorithms, and topology lear...
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A theoretical framework based on attributed elementary programmed graph grammars (GRAPE grammars) that turns out to be equally well-suited for describing network dynamics, weight learning algorithms, and topology learning algorithms is proposed. It is shown how GRAPE grammars can be used to model neural networks. Special emphasis is placed on topology learning. It is concluded that GRAPE grammars offer a great potential for neural networks, giving the possibility of a common language suited for all kinds of neural networks.< >
The discrete cosine transform plays an important role in rectangularly sampled image coding for its excellent performance in information compaction. Hexagonal sampling is the optimal sampling strategy for two-dimensio...
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The discrete cosine transform plays an important role in rectangularly sampled image coding for its excellent performance in information compaction. Hexagonal sampling is the optimal sampling strategy for two-dimensional signals in the sense that exact reconstruction of the waveform requires a lower sampling density than with the alternative schemes. In this Letter, a hexagonal discrete cosine transform (HDCT) for encoding the hexagonally sampled signals is presented.
Based on the assumption that most probability densities in real life can be approximated by a mixture of Gaussian densities, we propose here a three-layer adaptive network with each neuron in the lower hidden layer re...
Based on the assumption that most probability densities in real life can be approximated by a mixture of Gaussian densities, we propose here a three-layer adaptive network with each neuron in the lower hidden layer representing a Gaussian basis function (covariance matrix equal to where I is a unit matrix) to estimate various probability densities and serve as a Bayes classifier. The width of the basis function may be the same for all neurons in this layer or it may vary from one neuron to another. This paper investigates the effectiveness of the network for both cases and presents a localized learning algorithm to adjust the network parameters. The network was trained with artificial data derived from known mixtures of memoryless Gaussian sources as well as exponential and Gamma densities. The performance of the network as a pattern density estimator was measured in terms of the relative difference between the target probability density function (p.d.f.) which generates the training and testing data and the network output representing the estimation. Samples from two mixtures corresponding to two classes were used to test the network capability as a classifier by comparing its error rate against that of a Bayes classifier. Both one- and two-dimensional cases were explored. The successfulness of the network depended on how well the target p.d.f.’s were represented by the training samples, the number of hidden neurons employed in the network and how thoroughly the network was trained. It was also found that allowing each basis function to have an independent width had a predominant effect on the network performance.
An efficient discrete cosine transform technique using a new adaptive feature for bandwidth compression is described. Taking account of human visual characteristics and transform coefficient statistics, the higher act...
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An efficient discrete cosine transform technique using a new adaptive feature for bandwidth compression is described. Taking account of human visual characteristics and transform coefficient statistics, the higher activity region is further classified into four subclasses according to four proposed basic patterns, while the lower activity region is assigned to four subclasses according to its AC-energy (image activity) distribution. Computer simulations shows that the proposed adaptive coder exhibits a performance improvement of 1 dB or more over conventional adaptive coders at the same coding rates.< >
The authors outline their approach for automatic translation of geometric entities produced by a CAD system into a relational graph structure. They have developed a system which uses 3-D object descriptions created on...
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The authors outline their approach for automatic translation of geometric entities produced by a CAD system into a relational graph structure. They have developed a system which uses 3-D object descriptions created on a commercial CAD system and expressed in the industry-standard IGES form, and performs geometric inferencing to object a relational graph representation of the object which can be stored in a database of models of object recognition. Details of the IGES standard, the geometric inference engine, and some formal properties of 3-D models are discussed. In addition to the process of translation from one data format to another, the interference engine extracts higher-level information from the CAD model and stores it explicitly in the new data structure. The higher-level features will allow the search space explored during the object recognition stage to be pruned early.< >
An improved method for shape from shading is presented. With introduction of the adaptive attenuated factors, the results of the initial iterations fall into the region of the solution values as much as possible. Simu...
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ISBN:
(纸本)0818608781
An improved method for shape from shading is presented. With introduction of the adaptive attenuated factors, the results of the initial iterations fall into the region of the solution values as much as possible. Simulated tests show that the method makes a notable improvement over the well-known approach proposed by K. Ikeuchi and B.K.P. Horn (1981), not only on the correctness of the solution but also on the speed of the convergence. The case of the actual object is also discussed. The result is satisfactory.< >
The Laplacian of Gaussian operator, Del /sup 2/G, is very important as an edge detector in the theory of computer vision. The bias of zero-crossing and output signal-to-noise-ratio of Del /sup 2/G under the models of ...
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ISBN:
(纸本)0818608781
The Laplacian of Gaussian operator, Del /sup 2/G, is very important as an edge detector in the theory of computer vision. The bias of zero-crossing and output signal-to-noise-ratio of Del /sup 2/G under the models of four typical kinds of edges corrupted by white noise are given, and these theoretical results are confirmed by experiments. The relations among bias of zero-crossing, output and input signal-to-noise-ratio and parameter sigma of Del /sup 2/G are presented.< >
The intention of this paper is to help bridging the gap between knowledge base and computer vision system. A knowledge-based vision system for identification of overlapping objects is presented. The authors place emph...
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ISBN:
(纸本)7800030393
The intention of this paper is to help bridging the gap between knowledge base and computer vision system. A knowledge-based vision system for identification of overlapping objects is presented. The authors place emphasis on the reasoning strategy based on knowledge base for recognizing of occluded workpieces to provide information with an education Robot. The experimental results are given and some problem are discussed.
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