A PDA-based sign translator
We propose an effective approach for a PDA-based sign system and present the sign translator. Its main functions include three parts: detection, recognition and translation. Automatic detection and recognition of text in natural scenes is a prerequisite for the automatic sign translator. In order to make the system robust for text detection in various natural scenes, the detection approach efficiently embeds multi-resolution, adaptive search in a hierarchical framework with different emphases at each layer. We also introduce an intensity-based OCR method to recognize characters in various fonts and lighting conditions, where we employ the Gabor transform to obtain local features, and LDA for selection and classification of features. The recognition rate is 92.4% for the testing set obtained from the natural sign. A sign is different from the normal used sentence. It is brief with a lot of abbreviations and place nouns. We only briefly introduce a rule-based place name translation. We have integrated all these functions in a PDA, which can capture sign images, auto segment and recognize the Chinese sign, and translate it into English.

title: Automatic detection and recognition of signs from natural scenes
abstract: In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.

title: Automatic detection of signs with affine transformation
abstract: In this paper, we propose an approach for detecting signs from natural scenes. The approach efficiently embeds multiresolution, adaptive search, and affine rectification algorithms in a hierarchical framework, with different emphases at each layer. We combine in multi-resolution and multi-scale edge detection techniques to effectively detect text in different sizes. By using the cites from text inside the image, we introduce affine rectification transformation to recover deformation of the text region caused by air inappropriate camera view angle. This procedure can significantly improve text detection rate and OCR (Optical Character Recognition) accuracy. Experimental results have demonstrated feasibility of the proposed algorithms. We have applied the proposed approach to a Chinese sign translation system, which can automatically detect Chinese text input from a camera, recognize the text, and translate the recognized text into English or voice stream.

title: A robust approach for recognition of text embedded in natural scenes
abstract: In this paper, we propose a robust approach for recognition of text embedded in natural scenes. Instead of using binary information as most other OCR systems do, we extract features from intensity of an image directly. We utilize a local intensity normalization method to effectively handle lighting variations. We then employ Gabor transform to obtain local features, and use the linear discriminant analysis (LDA) for selection and classification of features. The proposed method has been applied to a Chinese sign recognition task. The system can recognize a vocabulary of 3755 level I Chinese characters in the Chinese national standard character set GB2312-80 with various print fonts. We tested the system on 1630 test characters in sign images captured from the natural scenes, and the recognition accuracy was 92.46%. We have integrated the system into our automatic Chinese sign translation system.

title: Eigenspectra Versus Eigenfaces: Classification with a Kernel-Based Nonlinear Representor
abstract: This short paper proposes a face recognition scheme, wherein features called eigenspectra are extracted successively by the fast Fourier transform (FFT) and the principle component analysis (PCA) and classification results are obtained by a classifier called kernel-based nonlinear representor (KNR). Its effectiveness is shown by experimental results on the Olivetti Research Laboratory (ORL) face database.

title: SPOT: A System for Detecting Projected Outliers From High-dimensional Data Streams
abstract: In this paper, we present a new technique, called stream projected ouliter detector (SPOT), to deal with outlier detection problem in high-dimensional data streams. SPOT is unique in a number of aspects. First, SPOT employs a novel window-based time model and decaying cell summaries to capture statistics from the data stream. Second, sparse subspace template (SST), a set of top sparse subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively. Multi-Objective genetic algorithm (MOGA) is employed as an effective search method in unsupervised learning for finding outlying subspaces from training data. Finally, SST is able to carry out online self- evolution to cope with dynamics of data streams. This paper provides details on the motivation and technical challenges of detecting outliers from high-dimensional data streams, present an overview of SPOT, and give the plans for system demonstration of SPOT.

title: A Framework for Efficient Association Rule Mining in XML Data
abstract: In this article, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document first are preprocessed to transform either to an Indexed XML Tree (IX-tree) or to Multirelational Databases (Multi-DB), depending on the size of the XML document and the memory constraint of the system, for efficient data selection and AR mining. Concepts that are relevant to the AR mining task are generalized to produce generalized metapatterns. A suitable metric is devised for measuring the degree of concept generalization in order to prevent undergeneralization or overgeneralization. Resulting generalized metapatterns are used to generate large ARs that meet the support and confidence levels. A greedy algorithm is also presented in order to integrate data selection and large itemset generation to enhance the efficiency of the AR mining process. The experiments conducted show that XAR-Miner is more efficient in performing a large number of AR mining tasks from XML documents than the state-of-the-art method of repetitively scanning through XML documents in order to perform each of the mining tasks.

title: On the Integration Frame of Collaborative Innovation Support System for Telecom Operators
abstract: As the industry value-chain getting complicated, collaborative innovation becomes an important competitive factor for telecom operators. However, collaborative innovation means more participators and greater uncertainty in the innovation procedure. As a result, collaborative innovations usually cost more in time and money, which could make the innovations lose or bring serious problems even when new services have been launched into the market. In this paper, the integration frame of collaborate innovation support system (CISS) is put forward. Firstly, the participants and resource flows in the collaborative innovation in telecom industry are analyzed. Based on that, the requirements and functions of CISS are summarized. The system should help to connect the necessary resources for innovations and implement process management in an efficient way. Secondly, the framework of CISS is designed, defining functional construction and information flows. Then the key technologies supporting CISS are illustrated. Functionally, the CISS should be a part of BSS/OSS (Business/Operating Support System), and the relationship between CISS and BSS/OSS is explained. Finally, the organizational and managerial supports for the system are discussed.

title: A new edge-based text verification approach for video
abstract: In this paper, we propose a new edge-based text verification approach for video. Based on the investigation of the relation between candidate blocks and their neighbor areas, the proposed approach first detects background edges in candidate blocks, then erases them by an edge tracking technique, and finally the candidate blocks containing too few remaining edges are eliminated as false alarms. Three measures for text detection evaluation in video were used to assess the performance of the proposed text verification approach. Experimental results on 50 broadcast news video clips demonstrate the validity of our approach.