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Dive into the research topics where Chew Lim Tan is active.

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Featured researches published by Chew Lim Tan.


Neurocomputing | 2000

A case study on using neural networks to perform technical forecasting of forex

JingTao Yao; Chew Lim Tan

This paper reports empirical evidence that a neural network model is applicable to the prediction of foreign exchange rates. Time series data and technical indicators, such as moving average, are fed to neural networks to capture the underlying ‘rulesa of the movement in currency exchange rates. The exchange rates between American Dollar and ve other major currencies, Japanese Yen, Deutsch Mark, British Pound, Swiss Franc and Australian Dollar are forecast by the trained neural networks. The traditional rescaled range analysis is used to test the ‘e


IEEE Transactions on Pattern Analysis and Machine Intelligence | 2011

A Laplacian Approach to Multi-Oriented Text Detection in Video

Palaiahnakote Shivakumara; Trung Quy Phan; Chew Lim Tan

ciencya of each market before using historical data to train the neural networks. The results presented here show that without the use of extensive market data or knowledge, useful prediction can be made and signicant paper prots can be achieved for out-of-sample data with simple technical indicators. A further research on exchange rates between Swiss Franc and American Dollar is also conducted. However, the experiments show that with e


international world wide web conferences | 2005

A comprehensive comparative study on term weighting schemes for text categorization with support vector machines

Man Lan; Chew Lim Tan; Hwee-Boon Low; Sam Yuan Sung

cient market it is not easy to make prots using technical indicators or time series input neural networks. This article also discusses several issues on the frequency of sampling, choice of network architecture, forecasting periods, and measures for evaluating the model’s predictive power. After presenting the experimental results, a discussion on future research concludes the paper. ( 2000 Elsevier Science B.V. All rights reserved.


international symposium on neural networks | 2004

Initialization of cluster refinement algorithms: a review and comparative study

Ji He; Man Lan; Chew Lim Tan; Sam-Yuan Sung; Hwee-Boon Low

In this paper, we propose a method based on the Laplacian in the frequency domain for video text detection. Unlike many other approaches which assume that text is horizontally-oriented, our method is able to handle text of arbitrary orientation. The input image is first filtered with Fourier-Laplacian. K-means clustering is then used to identify candidate text regions based on the maximum difference. The skeleton of each connected component helps to separate the different text strings from each other. Finally, text string straightness and edge density are used for false positive elimination. Experimental results show that the proposed method is able to handle graphics text and scene text of both horizontal and nonhorizontal orientation.


international conference on document analysis and recognition | 2009

A Laplacian Method for Video Text Detection

Trung Quy Phan; Palaiahnakote Shivakumara; Chew Lim Tan

Term weighting scheme, which has been used to convert the documents as vectors in the term space, is a vital step in automatic text categorization. In this paper, we conducted comprehensive experiments to compare various term weighting schemes with SVM on two widely-used benchmark data sets. We also presented a new term weighting scheme tf-rf to improve the terms discriminating power. The controlled experimental results showed that this newly proposed tf-rf scheme is significantly better than other widely-used term weighting schemes. Compared with schemes related with tf factor alone, the idf factor does not improve or even decrease the terms discriminating power for text categorization.


international symposium on neural networks | 2005

A comparative study on term weighting schemes for text categorization

Man Lan; Sam-Yuan Sung; Hwee-Boon Low; Chew Lim Tan

Various iterative refinement clustering methods are dependent on the initial state of the model and are capable of obtaining one of their local optima only. Since the task of identifying the global optimization is NP-hard, the study of the initialization method towards a sub-optimization is of great value. This paper reviews the various cluster initialization methods in the literature by categorizing them into three major families, namely random sampling methods, distance optimization methods, and density estimation methods. In addition, using a set of quantitative measures, we assess their performance on a number of synthetic and real-life data sets. Our controlled benchmark identifies two distance optimization methods, namely SCS and KKZ, as complements of the k-means learning characteristics towards a better cluster separation in the output solution.


IEEE Transactions on Circuits and Systems for Video Technology | 2010

New Fourier-Statistical Features in RGB Space for Video Text Detection

Palaiahnakote Shivakumara; Trung Quy Phan; Chew Lim Tan

In this paper, we propose an efficient text detection method based on the Laplacian operator. The maximum gradient difference value is computed for each pixel in the Laplacian-filtered image. K-means is then used to classify all the pixels into two clusters: text and non-text. For each candidate text region, the corresponding region in the Sobel edge map of the input image undergoes projection profile analysis to determine the boundary of the text blocks. Finally, we employ empirical rules to eliminate false positives based on geometrical properties. Experimental results show that the proposed method is able to detect text of different fonts, contrast and backgrounds. Moreover, it outperforms three existing methods in terms of detection and false positive rates.


international conference on document analysis and recognition | 2009

A Gradient Difference Based Technique for Video Text Detection

Palaiahnakote Shivakumara; Trung Quy Phan; Chew Lim Tan

The term weighting scheme, which is used to convert documents into vectors in the term spaces, is a vital step in automatic text categorization. The previous studies showed that term weighting schemes dominate the performance rather than the kernel functions of SVMs for the text categorization task. In this paper, we conducted experiments to compare various term weighting schemes with SVM on two widely-used benchmark data sets. We also presented a new term weighting scheme tf.rf for text categorization. The cross-scheme comparison was performed by using McNemars tests. The controlled experimental results showed that the newly proposed tf.rf scheme is significantly better than other term weighting schemes. Compared with schemes related with tf factor alone, the idf factor does not improve or even decrease the terms discriminating power for text categorization. The binary and tf.chi representations significantly underperform the other term weighting schemes.


Archive | 2014

Video Text Detection

Tong Lu; Shivakumara Palaiahnakote; Chew Lim Tan; Wenyin Liu

In this paper, we propose new Fourier-statistical features (FSF) in RGB space for detecting text in video frames of unconstrained background, different fonts, different scripts, and different font sizes. This paper consists of two parts namely automatic classification of text frames from a large database of text and non-text frames and FSF in RGB for text detection in the classified text frames. For text frame classification, we present novel features based on three visual cues, namely, sharpness in filter-edge maps, straightness of the edges, and proximity of the edges to identify a true text frame. For text detection in video frames, we present new Fourier transform based features in RGB space with statistical features and the computed FSF features from RGB bands are subject to K-means clustering to classify text pixels from the background of the frame. Text blocks of the classified text pixels are determined by analyzing the projection profiles. Finally, we introduce a few heuristics to eliminate false positives from the frame. The robustness of the proposed approach is tested by conducting experiments on a variety of frames of low contrast, complex background, different fonts, and sizes of text in the frame. Both our own test dataset and a publicly available dataset are used for the experiments. The experimental results show that the proposed approach is superior to existing approaches in terms of detection rate, false positive rate, and misdetection rate.


international conference on document analysis and recognition | 2011

A Gradient Vector Flow-Based Method for Video Character Segmentation

Trung Quy Phan; Palaiahnakote Shivakumara; Bolan Su; Chew Lim Tan

Text detection in video images has received increasing attention, particularly in scene text detection in video images, as it plays a vital role in video indexing and information retrieval. This paper proposes a new and robust gradient difference technique for detecting both graphics and scene text in video images. The technique introduces the concept of zero crossing to determine the bounding boxes for the detected text lines in video images, rather than using the conventional projection profiles based method which fails to fix bounding boxes when there is no proper spacing between the detected text lines. We demonstrate the capability of the proposed technique by conducting experiments on video images containing both graphics text and scene text with different font shapes and sizes, languages, text directions, background and contrasts. Our experimental results show that the proposed technique outperforms existing methods in terms of detection rate for large video image database.

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Man Lan

National University of Singapore

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Palaiahnakote Shivakumara

Information Technology University

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Trung Quy Phan

National University of Singapore

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Li Zhang

National University of Singapore

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Sam-Yuan Sung

National University of Singapore

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Wenyin Liu

City University of Hong Kong

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