40810024
Page segmentation using document model
Transforming a paper document to its electronic version in a form suitable for efficient storage, retrieval and interpretation continues to be a challenging problem. An efficient document model is necessary to solve this problem. Document modeling involves techniques of thresholding, skew detection, geometric layout analysis and logical layout analysis. The derived model can then be used in document storage and retrieval. We use the traditional bottom-up approach based on the connected component extraction to efficiently implement page segmentation and region identification. A new document model which preserves top-down generation information is proposed based on which a document is logically represented for interactive editing, storage, retrieval, transfer and logical analysis.
34534588
Lane boundary detection using a multiresolution Hough transform
Lane boundary detection is the problem of estimating the geometric structure of the lane boundaries of a road based on the images grabbed by a camera on board a vehicle. We use the Hough transform to detect lane boundaries with a parabolic model under a variety of road pavement types, lane structures and weather conditions. In the three-dimensional Hough space, a parabolic curve is represented as a straight line. To simplify the computation, the parametric space can be divided into (i) a two-dimensional space measured by the parameters which are shared by all the lane edges, and (ii) a one-dimensional space of the parameter which makes a distinction among different edges in an image. A multiresolution strategy is used to improve both the speed and accuracy of the Hough transform. Experimental results show that the proposed method is relatively less prone to the image noise and is computationally tractable.
39489918
An economical contour extraction algorithm for understanding large-size engineering drawings
An algorithm is described which produces the extraction of an object's contours in a binary image while storing only two raster lines at a time during processing. The major data structure used is a block adjacency graph which occupies much less space (typically one hundredth) than the original image. Because this algorithm has very low costs of memory space and processing time, it is a powerful tool for processing large-size engineering drawings. Applications of the method to the recognition of some loop-structure logical symbols are described.<<ETX>>
27633879
A more efficient branch and bound algorithm for feature selection
Abstract An algorithm is presented to be able to select the globally optimal subset of d features from a larger D -feature set. This is a fundamental problem in statistical pattern recognition and combinatorial optimization. Exhaustive enumeration is computationally unfeasible in most applications. This algorithm dynamically searches for the globally optimal solution on a minimum solution tree which is a subtree of the solution tree used in the traditional branch and bound algorithm. The new algorithm is compared theoretically with the branch and bound algorithm. The analysis and the experimental results show that it is more efficient than the traditional algorithm.
37838547
Automatic understanding of symbol-connected diagrams
A symbol-connected diagram is mainly composed of symbols and their connections. All symbols can be categorized into two groups: shape symbols and structure symbols. This paper proposes a system for automatic understanding of symbol-connected diagrams based on a graph, called the consistent attributed graph (CAG), which represents the diagram in a bottom-up fashion. The shape symbol processing is carried out by means of shape processing, followed by shape symbol identification with the use of two symbol windows in the CAG. Some experimental results for understanding logic circuit diagrams are given.
38916969
A Generic System for Form Dropout
Recent advances in intelligent character recognition are enabling us to address many challenging problems in document image analysis. One of them is intelligent form analysis. This paper describes a generic system for form dropout when the filled-in characters or symbols are either touching or crossing the form frames. We propose a method to separate these characters from form frames whose locations are unknown. Since some of the character strokes are either touching or crossing the form frames, we need to address the following three issues: 1) localization of form frames; 2) separation of characters and form frames; and 3) reconstruction of broken strokes introduced during separation. The form frame is automatically located by finding long straight lines based on the block adjacency graph. Form frame separation and character reconstruction are implemented by means of this graph. The proposed system includes form structure learning and form dropout. First, a form structure-based template is automatically generated from a blank form which includes form frames, preprinted data areas and skew angle. With this form template, our system can then extract both handwritten and machine-typed filled-in data. Experimental results on three different types of forms show the performance of our system. Further, the proposed method is robust to noise and skew that is introduced during scanning.
14866925
A new edge detector based on Fresnel diffraction
This paper proposed an edge detection scheme which is deduced from Fresnel diffraction. Analysis in this paper shows that Fresnel convolution kernel function performs well on edge enhancement when images are transformed into complex functions. Due to its mathematical complexity, the method is simplified into a linear convolution filter. The new edge detector is designed based on the simplified linear filter. Experimental results indicate that the new detector gives quantitative results equal to the Canny detector while it is more simple to be implemented.
11424736
Extending the ONESAF Testbed into a C4ISR Testbed
This article describes how the modeling and simulation environment of the OneSAF Testbed Baseline (OTB) v1.0 has been extended to enable the testing of heterogeneous algorithms that are being designed for real-world C4ISR applications. This has been accomplished by building an architecture that extends functional and logical components of the OTB system in the following ways: the use of the OTB Compact Terrain Database for terrain analysis and preliminary threat assessment, the addition of the RETSINA-OTB Bridge for the real-time query and control of OTB entities, and the addition of new DIS-based sensor entities for interoperation with Command and Control algorithms, to name a few. This article illustrates how to make a few small but general extensions to a modeling and simulation system to create a larger testbed system with minimum impact on the native system and with great potential for the range of applications that can exploit it.
42842563
A WSRF Based Shopping Cart System
Web Services Resource Framework (WSRF) is a set of specifications that represents a convergence point of the Web services and the Grid services communities. This paper presents our early experience with WSRF. A shopping cart system has been implemented with WSRF supported Globus toolkit 3.9.2 (GT3.9.2). Based on the system, the performance of the WSRF core in GT3.9.2 has also been evaluated.
17350574
Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images
We propose a novel hierarchical, nonlinear model that predicts brain activity in area V1 evoked by natural images. In the study reported here brain activity was measured by means of functional magnetic resonance imaging (fMRI), a noninvasive technique that provides an indirect measure of neural activity pooled over a small volume (≈ 2mm cube) of brain tissue. Our model, which we call the V-SPAM model, is based on the reasonable assumption that fMRI measurements reflect the (possibly nonlinearly) pooled, rectified output of a large population of simple and complex cells in V1. It has a hierarchical filtering stage that consists of three layers: model simple cells, model complex cells, and a third layer in which the complex cells are linearly pooled (called "pooled-complex" cells). The pooling stage then obtains the measured fMRI signals as a sparse additive model (SpAM) in which a sparse nonparametric (nonlinear) combination of model complex cell and model pooled-complex cell outputs are summed. Our results show that the V-SPAM model predicts fMRI responses evoked by natural images better than a benchmark model that only provides linear pooling of model complex cells. Furthermore, the spatial receptive fields, frequency tuning and orientation tuning curves of the V-SPAM model estimated for each voxel appears to be consistent with the known properties of V1, and with previous analyses of this data set. A visualization procedure applied to the V-SPAM model shows that most of the nonlinear pooling consists of simple compressive or saturating nonlinearities.
15309050
Prediction of Transmembrane Helical Segments in Membrane Proteins Using Back Propagation Neural Network with Wavelet Analysis
Transmembrane proteins possess important physiological function. The increasing transmembrane protein sequences from genome projects raise the needs for theoretical methods to predict their structures, especially transmembrane helical segments (TMHs). wnnTM, a new method of BP neural network based on wavelet multiresolution analysis (MRA) is initially developed to predict the number and location of TMHs in membrane proteins. 80 proteins with known 3D-structure randomly selected from Mptopo database are used as test set to evaluate the prediction accuracy of the method. The final results indicate that the proposed method is more effective than BP neural network model only.