Fu Chang

A linear-time component-labeling algorithm using contour tracing technique

A new linear-time algorithm is presented in this paper that simultaneously labels connected components (to be referred to merely as components in this paper) and their contours in binary images. The main step of this algorithm is to use a contour tracing technique to detect the external contour and possible internal contours of each component, and also to identify and label the interior area of each component. Labeling is done in a single pass over the image, while contour points are revisited more than once, but no more than a constant number of times. Moreover, no re-labeling is required throughout the entire process, as it is required by other algorithms. Experimentation on various types of images (characters, half-tone pictures, photographs, newspaper, etc.) shows that our method outperforms methods that use the equivalence technique. Our algorithm not only labels components but also extracts component contours and sequential orders of contour points, which can be useful for many applications.

The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method

In this paper, we propose a new data reduction algorithm that iteratively selects some samples and ignores others that can be absorbed, or represented, by those selected. This algorithm differs from the condensed nearest neighbor (CNN) rule in its employment of a strong absorption criterion, in contrast to the weak criterion employed by CNN; hence, it is called the generalized CNN (GCNN) algorithm. The new criterion allows GCNN to incorporate CNN as a special case, and can achieve consistency, or asymptotic Bayes-risk efficiency, under certain conditions. GCNN, moreover, can yield significantly better accuracy than other instance-based data reduction methods. We demonstrate the last claim through experiments on five datasets, some of which contain a very large number of samples

A binarization method with learning-built rules for document images produced by cameras

In this paper, we propose a novel binarization method for document images produced by cameras. Such images often have varying degrees of brightness and require more careful treatment than merely applying a statistical method to obtain a threshold value. To resolve the problem, the proposed method divides an image into several regions and decides how to binarize each region. The decision rules are derived from a learning process that takes training images as input. Tests on images produced under normal and inadequate illumination conditions show that our method yields better visual quality and better OCR performance than three global binarization methods and four locally adaptive binarization methods.

Estimation of skew angles for scanned documents based on piecewise covering by parallelograms

We propose a fast and robust skew estimation method for scanned documents that estimates skew angles based on piecewise covering of objects, such as textlines, figures, forms, or tables. The method first divides a document image into a number of non-overlapping slabs in which each object is covered by parallelograms. It then estimates the skew angle based on these parallelograms or, equivalently, their complementary regions. Putting our method to a systematic test and comparing it with some alternatives, we find that it yields favorable results in terms of accuracy, sensitivity to non-textual objects, effectiveness in dealing with documents of unspecified reading order, and computational efficiency. Some work is also conducted to find an effective way to further shorten its computation time at the expense of an extremely small loss of accuracy.

A component-labeling algorithm using contour tracing technique

A new method for finding connected components frombinary images is presented in this article. The main stepof this method is to use a contour tracing technique todetect component contours and also to fill in interior areas.All the component points are traced by this algorithmin a single pass and are assigned either a new label orthe same label as their neighboring pixels. Experimentingon various types of document images (characters, pictures,newspapers, etc.), we find that our method outperformsthe other sequential methods using the equivalencetechnique. Our algorithm, moreover, is a method that notonly labels components but also extracts component contoursat the same time, which proves to be more usefulthan those algorithms that only label components.

A prototype classification method and its use in a hybrid solution for multiclass pattern recognition

In this paper, we propose a prototype classification method that employs a learning process to determine both the number and the location of prototypes. This learning process decides whether to stop adding prototypes according to a certain termination condition, and also adjusts the location of prototypes using either the K-means (KM) or the fuzzy c-means (FCM) clustering algorithms. When the prototype classification method is applied, the support vector machine (SVM) method can be used to post-process the top-rank candidates obtained during the prototype learning or matching process. We apply this hybrid solution to handwriting recognition and address the convergence behavior and runtime consumption of the prototype construction process, and discuss how to combine our prototype classifier with SVM classifiers to form an effective hybrid classifier.

Feature analysis using line sweep thinning algorithm

In this article, we propose a new thinning algorithm based on line sweep operation. A line sweep is a process where the plane figure is divided into parallel slabs by lines passing through certain ...

A prototype classification method and its application to handwritten character recognition

We propose a new prototype classification method that can be combined with support vector machines (SVM) (Cortes, C and Vapnik, V, 1995) for recognizing handwritten numerals and Chinese characters. This method employs a learning process for determining both the number and location of prototypes. The possible techniques used in this process for adjusting the location of prototypes include the K-means (KM) algorithm and the fuzzy c-means (FCM) algorithm (Bezdek, J. C., 1981). When the prototype classification method is applied, the SVM method can be used to process top rank candidates obtained in the prototype learning or matching process. We apply this hybrid method to the recognition of handwritten numerals and Chinese characters. Experiment results show that this hybrid method saves great amount of training and testing time when the number of character types is large, and achieves comparable accuracy rates to those achieved by using SVM solely. Our results also show that the proposed method performs better than the nearest neighbor (NN) classification method. These outcomes suggest that the proposed method can serve as an effective solution for large-scale multiclass classification

Robust traffic design for dynamic routing networks

The authors present traffic restoration design algorithms to attain a robust network for any facility link or node failure. Some of these algorithms were implemented on network models to compute the cost for doing traffic restoration for different traffic restoration level objectives. From their simulation studies, the authors found that with efficient use of trunk group diversity and at a reasonable incremental cost, one can obtain a robust network to respond to a major facility link or a node failure and still meet the network objectives. A similar approach based on the algorithms presented in this work has been proposed for design of the worldwide intelligent network.<<ETX>>

Caption analysis and recognition for building video indexing systems

Abstract.In this paper, we propose several methods for analyzing and recognizing Chinese video captions, which constitute a very useful information source for video content. Image binarization, performed by combining a global threshold method and a window-based method, is used to obtain clearer images of characters, and a caption-tracking scheme is used to locate caption regions and detect caption changes. The separation of characters from possibly complex backgrounds is achieved by using size and color constraints and by cross examination of multiframe images. To segment individual characters, we use a dynamic split-and-merge strategy. Finally, we propose a character recognition process using a prototype classification method, supplemented by a disambiguation process using support vector machines, to improve recognition outcomes. This is followed by a postprocess that integrates multiple recognition results. The overall accuracy rate for the entire process applied to test video films is 94.11%.