A new edge-based text verification approach for video
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.

title: Adaptive Noise Reduction for Engineering Drawings Based on Primitives and Noise Assessment
abstract: In this paper, a novel adaptive noise reduction method for engineering drawings is proposed based on the assessment of both primitives and noise. Unlike the current approaches, our method takes into account the special features of engineering drawings and assesses the characteristics of primitives and noise such that adaptive procedures and parameters are applied for noise reduction. For this purpose, we first analyze and categorize various types of noise in engineering drawings. The algorithms for average linewidth assessment, noise distribution assessment and noise level assessment are then proposed. These three assessments are combined to describe the features of the noise of each individual engineering drawing. Finally, median filters and morphological filters, which can adjust their template size and structural element adaptively according to different noise level and type, are used for adaptive noise reduction. Preliminary experimental results show that our approach is effective for noise reduction of engineering drawings.

title: A Pixel-level Statistical Structural Descriptor for Shape Measure and Recognition
abstract: A novel shape descriptor based on the histogram matrix of pixel-level structural features is presented. First, length ratios and angles between the centroid and contour points of a shape are calculated as two structural attributes. Then, the attributes are combined to construct a new histogram matrix in the feature space statistically. The proposed shape descriptor can measure circularity, smoothness, and symmetry of shapes, and be used to recognize shapes. Experimental results demonstrate the effectiveness of our method.

title: Extraction of Text Objects in Video Documents: Recent Progress
abstract: Text extraction in video documents, as an important research field of content-based information indexing and retrieval, has been developing rapidly since 1990s. This has led to much progress in text extraction, performance evaluation, and related applications. By reviewing the approaches proposed during the past five years, this paper introduces the progress made in this area and discusses promising directions for future research.

title: Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol
abstract: Common benchmark data sets, standardized performance metrics, and baseline algorithms have demonstrated considerable impact on research and development in a variety of application domains. These resources provide both consumers and developers of technology with a common framework to objectively compare the performance of different algorithms and algorithmic improvements. In this paper, we present such a framework for evaluating object detection and tracking in video: specifically for face, text, and vehicle objects. This framework includes the source video data, ground-truth annotations (along with guidelines for annotation), performance metrics, evaluation protocols, and tools including scoring software and baseline algorithms. For each detection and tracking task and supported domain, we developed a 50-clip training set and a 50-clip test set. Each data clip is approximately 2.5 minutes long and has been completely spatially/temporally annotated at the I-frame level. Each task/domain, therefore, has an associated annotated corpus of approximately 450,000 frames. The scope of such annotation is unprecedented and was designed to begin to support the necessary quantities of data for robust machine learning approaches, as well as a statistically significant comparison of the performance of algorithms. The goal of this work was to systematically address the challenges of object detection and tracking through a common evaluation framework that permits a meaningful objective comparison of techniques, provides the research community with sufficient data for the exploration of automatic modeling techniques, encourages the incorporation of objective evaluation into the development process, and contributes useful lasting resources of a scale and magnitude that will prove to be extremely useful to the computer vision research community for years to come.

title: A Novel Video Searching Model Based on Ontology Inference and Multimodal Information Fusion
abstract: Video comprises multiple types of textual, audio and visual information, and each of them contains abundant semantic information. Therefore multimodal features query and fusion are necessary in video retrieval. In this paper, we propose a new video retrieval model, which adopts multi-model including text, image, semantic concept and camera motion to query video. Then relation algebra expression is advanced to fuse multimodal information instead of traditional linear fusion method. In semantic concept detection model, Bayesian network based ontology is proposed to extract concepts. The experiments on TRECVID 2005 corpus have demonstrated a superior performance compared with exiting key approaches of video retrieval by multimodal information fusion.

title: Detecting Projected Outliers in High-Dimensional Data Streams
abstract: In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector (SPOT), to identify outliers embedded in subspaces. Sparse Subspace Template (SST), a set of 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 for finding outlying subspaces from training data to construct SST. SST is able to carry out online self-evolution in the detection stage to cope with dynamics of data streams. The experimental results demonstrate the efficiency and effectiveness of SPOT in detecting outliers in high-dimensional data streams.

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: htSNPer1.0: software for haplotype block partition and htSNPs selection
abstract: BackgroundThere is recently great interest in haplotype block structure and haplotype tagging SNPs (htSNPs) in the human genome for its implication on htSNPs-based association mapping strategy for complex disease. Different definitions have been used to characterize the haplotype block structure in the human genome, and several different performance criteria and algorithms have been suggested on htSNPs selection. heuristic algorithm, generalized branch-and-bound algorithm, is applied to the searching of minimal set of haplotype tagging SNPs (htSNPs) according to different htSNPs performance criteria. We develop a software htSNPer1.0 to implement the algorithm, and integrate three htSNPs performance criteria and four haplotype block definitions for haplotype block partitioning. It is a software with powerful Graphical User Interface (GUI), which can be used to characterize the haplotype block structure and select htSNPs in the candidate gene or interested genomic regions. It can find the global optimization with only a fraction of the computing time consumed by exhaustive searching algorithm.ConclusionhtSNPer1.0 allows molecular geneticists to perform haplotype block analysis and htSNPs selection using different definitions and performance criteria. The software is a powerful tool for those focusing on association mapping based on strategy of haplotype block and htSNPs.

title: Reliability Modeling and Analysis of Service-Oriented Robot Management System
abstract: It is an obvious trend that computer network and robot technology will more and more closely combine with each other. And the robot, especially the service robot such as house robot, entertainment robot, etc, will provide different kinds of services to the mankind. This paper introduces a service-oriented network robotic management system (SONRMS) to coordinate and manage the behaviors between robots and their users so as to provide high reliability and safety measures to users. Then SONRMS' architecture is put forward. SONRMS reliability model is described by use of stochastic reward net, reliability block datagram and hierarchical model. The influence among parameters of the system upon SONRMS reliability is analyzed in the end