Developing an OCR (optical character recognition/reader) system requires expertise in various fields of researches, and the fact has been recognized as an obstacle to expanding the range of OCR applications, improving...
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Developing an OCR (optical character recognition/reader) system requires expertise in various fields of researches, and the fact has been recognized as an obstacle to expanding the range of OCR applications, improving the recognition accuracy, and to providing end users with better OCR applications and services. this paper proposes a platform for distributed and cooperative OCR systems, called "OCRGrid", that allows end users to search for and use OCR servers over networks. We discuss the potential of the platform, useful applications, and problems that need to be addressed. this paper also introduces a toolkit for realizing Web-based OCRs and an implementation of the character recognition based on the majority logic
the genetic programming (GP) is a powerful technique for symbolic regression. However, because it is a new area, many improvements can be obtained changing the basic behavior of the method. In this way, this work deve...
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the genetic programming (GP) is a powerful technique for symbolic regression. However, because it is a new area, many improvements can be obtained changing the basic behavior of the method. In this way, this work develop a different genetic programming algorithm doing some modifications on the classical GP algorithm and adding some concepts of evolution strategies. the new approach was evaluated using two instances of symbolic regression problem - the binomial-3 problem (a tunably difficult problem), proposed in (J.M. Daida et al., 2001) and the problem of modelling software reliability growth (an application of symbolic regression). the discovered results were compared withthe classical GP algorithm. the symbolic regression problems obtained excellent results and an improvement was detected using the proposed approach
In this paper, we describe an interesting driver assignment problem that is computationally intensive to solve due to its combinatorial nature. A hybrid approach involving mixed integer programming (MIP) and a heurist...
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In this paper, we describe an interesting driver assignment problem that is computationally intensive to solve due to its combinatorial nature. A hybrid approach involving mixed integer programming (MIP) and a heuristic is used to give good solutions to the problem within reasonable computation time. this approach attempts to utilize the strengths of MIP to search for an optimal solution, while letting the heuristic component address the complexity involved in the driver assignment problem so as to improve the time required to obtain a solution. Computational results are used to illustrate the performance of the approach
We investigated the geometrical complexity of several high-dimensional, small sample classification problems and its changes due to two popular feature selection procedures, forward feature selection (FFS) and linear ...
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We investigated the geometrical complexity of several high-dimensional, small sample classification problems and its changes due to two popular feature selection procedures, forward feature selection (FFS) and linear programming support vector machine (LPSVM). We found that both procedures are able to transform the problems to spaces of very low dimensionality where class separability is improved over that in the original space. the study shows that geometrical complexities have good potentials for comparing different feature selection methods in aspects relevant to classification accuracy, yet independent of particular classifier choices
Image registration is the process of geometrically aligning two or more images. In this paper we describe a method for registering pairs of images based on thin-plate spline mappings. the proposed algorithm minimizes ...
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Image registration is the process of geometrically aligning two or more images. In this paper we describe a method for registering pairs of images based on thin-plate spline mappings. the proposed algorithm minimizes the difference in gray-level intensity over bijective deformations. By using quadratic sufficient constraints for bijectivity and a least squares formulation this optimization problem can be addressed using quadratic programming and a modified Gauss-Newton method. this approach also results in a very computationally efficient algorithm. Example results from the algorithm on three different types of images are also presented
this paper investigates improvements of an online handwriting stroke-order analysis algorithm - cube search, based on cube graph stroke-order generation model and dynamic programming (DP). By dividing character into r...
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ISBN:
(纸本)0769525210
this paper investigates improvements of an online handwriting stroke-order analysis algorithm - cube search, based on cube graph stroke-order generation model and dynamic programming (DP). By dividing character into radicals, the model is decomposed into intra-radical graphs and an inter-radical graph. this decomposition considerably reduces the time complexity of stroke-order search DP. Experimental results showed an significant improvements in operational speed. Additionally, recognition accuracy was also improved by prohibiting unnatural stroke-order
the state explosion problem is one of the core bottlenecks in the model checking of concurrent software. We show how to ameliorate the problem by combining the ability of partial order techniques to reduce the state s...
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ISBN:
(纸本)354037406X
the state explosion problem is one of the core bottlenecks in the model checking of concurrent software. We show how to ameliorate the problem by combining the ability of partial order techniques to reduce the state space of the concurrent program withthe power of symbolic model checking to explore large state spaces. Our new verification methodology involves translating the given concurrent program into a circuit-based model which gives us the flexibility to then employ any model checking technique of choice - either SAT or BDD-based - for verifying a broad range of linear time properties, not just safety. the reduction in the explored state-space is obtained by statically augmenting the symbolic encoding of the program by additional constraints. these constraints restrict the scheduler to choose from a minimal conditional stubborn set of transitions at each state. Another key contribution of the paper, is a new method for detecting transactions on-the-fly which takes into account patterns of lock acquisition and yields better reductions than existing methods which rely on a lockset based analysis. Moreover unlike existing techniques, identifying on-the-fly transactions does not require the program to follow a lock discipline in accessing shared variables. We have applied our techniques to the Daisy test bench and shown the existence of several bugs.
this work presents the application of a novel technique on dimensionality reduction to deal with multispectral images. A distance based on mutual information is used to construct a hierarchical clustering structure. E...
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this work presents the application of a novel technique on dimensionality reduction to deal with multispectral images. A distance based on mutual information is used to construct a hierarchical clustering structure. Experimental results show that the method provides a very suitable subset of multispectral bands for pixel classification purposes
We present a unified framework for modeling and solving invariant point pattern matching problems. Invariant features are encoded as potentials in a probabilistic graphical model. By using a specific kind of graph top...
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We present a unified framework for modeling and solving invariant point pattern matching problems. Invariant features are encoded as potentials in a probabilistic graphical model. By using a specific kind of graph topology, different types of invariant matching models can be implemented via tree-width selection. Models with tree-widths 1, 2, 3 and 4 implement translation, similarity, affine and protective invariant point matching, respectively. the optimal match is then found by exploiting the Markov structure of the graph through the generalized distributive law in a dynamic programming setting. In the absence of noise in the point coordinates, the solutions found are optimal. Our early experiments suggest the approach is robust to outliers and moderate noise
Foreground segmentation with moving camera is a challenging task due to the presence of parallax effect, registration error, scene variations in out door, and etc. Currently, background modeling techniques either assu...
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Foreground segmentation with moving camera is a challenging task due to the presence of parallax effect, registration error, scene variations in out door, and etc. Currently, background modeling techniques either assumes correspondence among pixels in concurrent frames or do not model it explicitly. the contribution by this paper is in two folds. First, we achieve a new background model by introducing correspondence into it. Second, we pose foreground segmentation and correspondence estimation as a labeling problem. Spatial context is enforced in shape of tree structure and global optimal label at each node is computed using dynamic programming. Finally, based on the optimal correspondence, background model is updated. Resultantly, parallax effect and registration error are reduced significantly. Primary experiments proved our algorithm to be robust in performance
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