Character Matching Research Articles

Human motion capture data retrieval based on semantic thumbnail

We present a method for the efficient retrieval and browsing of immense amounts of realistic 3D human body motion capture data. The proposed method organizes motion capture data based on statistical K-means (SK---means), democratic decision making, unsupervised learning, and visual key frame extraction, thus achieving intuitive retrieval by browsing thumbnails of semantic key frames. We apply three steps for the efficient retrieval of motion capture data. The first is obtaining the basic type clusters by clustering motion capture data using the novel SK-means algorithm, and after which, immediately performing character matching. The second is learning the retrieval information of users during the retrieval process and updating the successful retrieval rate of each data; the search results are then ranked on the basis of successful retrieval rate by democratic decision making to improve accuracy. The last step is generating thumbnails with semantic generalization, which is conducted by using a novel key frame extraction algorithm based on visualized data analysis. The experiment demonstrates that this method can be utilised for the efficient organization and retrieval of enormous motion capture data.

DBpedia-Based Fuzzy Query Recommenda-tion Algorithm and Its Applications in the Resource-Sharing Platform of Polar Samples

In order to continuously promote the polar sample resource services in China and effectively guide the users to access such information as needed, a fuzzy algorithm based on DBpedia has been proposed through the analysis of the characteristics of the query recommendations in search engines, namely, to search similar entry queues by constructing a DBpedia category tree, then use the fuzzy matching algorithm to work out the entry similarity, and then present the example query applications of this algorithm on the resource-sharing platform of polar samples. Comparing the traditional literal character matching method and DBpedia semantic similarity algorithm, the experimental results show that the fuzzy query algorithm based on DBpedia features has a higher search accuracy rate, stronger anti-interference capability, and more flexible algorithm use by virtue of its fuzzy weight adjustment.

Similar ventral occipito-temporal cortex activations in literate and illiterate adults during the Chinese character matching task: An fMRI study

Visual word expertise is typically associated with enhanced ventral occipito-temporal (vOT) cortex activation in response to written words. Previous study utilized a passive viewing task and found that vOT response to written words was significantly stronger in literate compared to the illiterate subjects. However, recent neuroimaging findings have suggested that vOT response properties are highly dependent upon the task demand. Thus, it is unknown whether literate adults would show stronger vOT response to written words compared to illiterate adults during other cognitive tasks, such as perceptual matching. We addressed this issue by comparing vOT activations between literate and illiterate adults during a Chinese character and simple figure matching task. Unlike passive viewing, a perceptual matching task requires active shape comparison, therefore minimizing automatic word processing bias. We found that although the literate group performed better at Chinese character matching task, the two subject groups showed similar strong vOT responses during this task. Overall, the findings indicate that the vOT response to written words is not affected by expertise during a perceptual matching task, suggesting that the association between visual word expertise and vOT response may depend on the task demand.

Automatic License Plate Recognition System using SURF Features and RBF Neural Network

Automated License plate recognition is technology used in traffic management techniques allowing us to recognize identity of a vehicle through its registration plate information usually by videos or still images. It can be used for Parking assistance, automatic toll collection, vehicle theft management etc for improving manual traffic management error rate .In this paper we are proposing a method for implementing Automatic License Plate Recognition using Speed up robust feature matching SURF technique for plate detection and Advanced Radial basis function for matching characters. Simulation through popular tool Matlab ver 2011 shows 95% correctly detected plates and over 90% of character matching..

실내 공간에서의 문자매칭 기반 지오코딩 기법

최근 위치 정보의 이용이 여러 분야에 걸쳐 급격하게 증가하고 있다. 실외에서는 위치 정보의 획득을 위해 일반적으로 GPS 기술을 사용하였으나, 복잡한 실내 공간에서는 벽, 기둥과 같은 물리적인 장애물들로 인해 발생한 다중경로 간섭으로 무선 근거리통신망, RFID, 블루투스 등의 무선 네트워크 기술을 적용한 연구가 진행되었다. 그러나 이러한 위치 측정 기술들은 센싱 인프라스트럭쳐 구축비용이 많이 들며, 측위에 있어 계산 집약적이고, 실내 구조에 따른 정확도의 변화가 발생하는 한계가 존재한다. 본 연구에서는 이러한 한계점을 보완하고자 복합용도건물 내에서 쉽게 획득 및 식별이 가능한 상가, 컨벤션 센터 및 오피스의 전화번호, 방 번호, 상호명과 같은 서술 데이터를 이용하여 위치를 추출한다. 이 과정에서 문자 매칭을 활용하며, 위치 추정에 있어 신속한 계산과 실내 환경에 따른 정확도 변화를 배제하기 위해 지오코딩 방법을 적용한다. 본 연구에서 제안된 방법을 아키텍쳐로 설계하며, 구현을 위해 3차원 가시화 프로그램을 개발한다. 또한 제한된 기법에서의 매칭률, 프로세싱 시간을 통하여 정략적으로 평가한다. Recently, the use of locational information is growing rapidly. GPS technology has been adopted generally for obtaining locational information in outdoor spaces. In the other hand, the researches on indoor positioning have been carried out applying WLAN, RFID or Bluetooth technology because of the multi-path interference of GPS signal caused by the physical obstacles such as walls or columns in buildings. However, such technologies for indoor positioning cost too much to build sensing infrastructure and compute-intensive processes are involved. Furthermore, the accuracy of location estimation is variable caused by interior structures in buildings. In this study, to make up for the limitations, descriptive data such as phone number, unique room numbers, or business names readily available in mixed-use buildings is used for extracting location information. Furthermore, during the process, a geocoding method using character matching is applied to this study enabling prompt location estimation and sublating the fluctuation of accuracy caused by interior structures. Based on the proposed method in this study, an architecture is designed, and three-dimensional viewer program is developed for the implementation of this study. Also, this research is quantitatively analyzed through match rate and processing time of proposed method.

High Speed and High Accuracy Pre-Classification Method for OCR: Margin Added Hashing

Reducing the time complexity of character matching is critical to the development of efficient Japanese Optical Character Recognition (OCR) systems. To shorten the processing time, recognition is usually split into separate pre-classification and precise recognition stages. For high overall recognition performance, the pre-classification stage must both have very high classification accuracy and return only a small number of putative character categories for further processing. Furthermore, for any practical system, the speed of the pre-classification stage is also critical. The associative matching (AM) method has often been used for fast pre-classification because of its use of a hash table and reliance on just logical bit operations to select categories, both of which make it highly efficient. However, a certain level of redundancy exists in the hash table because it is constructed using only the minimum and maximum values of the data on each axis and therefore does not take account of the distribution of the data. We propose a novel method based on the AM method that satisfies the performance criteria described above but in a fraction of the time by modifying the hash table to reduce the range of each category of training characters. Furthermore, we show that our approach outperforms pre-classification by VQ clustering, ANN, LSH and AM in terms of classification accuracy, reducing the number of candidate categories and total processing time across an evaluation test set comprising 116,528 Japanese character images.

Dynamic Time Warping for Chinese calligraphic character matching and recognizing

Historical Chinese calligraphy is now being scanned by ongoing project of Universal Digital Library to enable universal access. 483 Calligraphic page images are segmented into 13,351 individual characters, followed by character feature extraction. When an image query submits, a short list of candidates from the database is selected according to simple features of stroke transect. Then the selected candidates are ranked by shape matching using two-dimensional Dynamic Time Warping (DTW). After that, similar calligraphic characters are retrieved and ranked, then the query is recognized as the label of the most similar retrieved characters.

Spectral Analysis of Projection Histogram for Enhancing Close matching character Recognition in Malayalam

The success rates of Optical Character Recognition (OCR) systems for printed Malayalam documents is quite impressive with the state of the art accuracy levels in the range of 85-95% for various. However for real applications, further enhancement of this accuracy levels are required. One of the bottle necks in further enhancement of the accuracy is identified as close-matching characters. In this paper, we delineate the close matching characters in Malayalam and report the development of a specialised classifier for these close-matching characters. The output of a state of the art of OCR is taken and characters falling into the close-matching character set is further fed into this specialised classifier for enhancing the accuracy. The classifier is based on support vector machine algorithm and uses feature vectors derived out of spectral coefficients of projection histogram signals of close-matching characters.

Research on Image Acquisition System Based on Binocular Stereo Vision

During object point character matching based on binocular stereoscopic vision, the measured object needs to be observed in different directions, from left and right. The article studied the structure configuration of image acquisition system and its optical path. For the object point with absolutely visible details, through extracting its feature matching point from the left and right images, its depth coordinate was obtained by calculation after matching. Finally the example of feature matching achieved with VC++ was given.

Finding witnesses by peeling

In the k -matches problem, we are given a pattern and a text, and for each text location, the desired output consists of all aligned matching characters if there are k or fewer of them, and any k aligned matching characters if there are more than k of them. This problem is one of several string matching problems that seek not only to find where the pattern matches the text under different “match” definitions, but also to provide witnesses to the match. Other such problems include k -aligned ones, k -witnesses, and k -mismatches. In addition, the solutions to several other string matching problems rely on the efficient solutions of the witness finding problems. In this article we provide a general method for solving such witness finding problems efficiently. We do so by casting the problem as a generalization of group testing, which we then solve by a process we call peeling . Using this general framework we obtain improved results for all of the problems mentioned. We also show that our method also solves a couple of problems outside the pattern matching domain.

Precise Detection of Car License Plates by Locating Main Characters

We propose a novel method to precisely detect car license plates by locating main characters, which are printed with large font size. The regions of the main characters are directly detected without detecting the plate region boundaries, so that license regions can be detected more precisely than by other existing methods. To generate a binary image, multiple thresholds are applied, and segmented regions are selected from multiple binarized images by a criterion of size and compactness. We do not employ any character matching methods, so that many candidates for main character groups are detected; thus, we use a neural network to reject non-main character groups from the candidates. The relation of the character regions and the intensity statistics are used as the input to the neural network for classification. The detection performance has been investigated on real images captured under various illumination conditions for 1000 vehicles. 980 plates were correctly detected, and almost all non-detected plates were so stained that their characters could not be isolated for character recognition. In addition, the processing time is fast enough for a commercial automatic license plate recognition system. Therefore, the proposed method can be used for recognition systems with high performance and fast processing.

ECEB: Enhanced Constraint Repetition Block for Regular Expression Matching on FPGA

Recent Network Intrusion Detection Systems (NIDSs) utilize Perl Compatible Regular Expression to describe malicious patterns existing in the content payload of packets more and more e±ciently. Several techniques are introduced to optimize the performance or complete the system for full support all of PCRE features in hardware platform, but some issues have just been solved partially. Constraint Repetition is among important characteristics of PCRE but its e®ective hardware-based implementation solutions are still limited. This paper describes an Enhanced Constraint rEpetition Block (ECEB) for regular expression matching engine in FPGA. To support more PCRE features, we improve our implementation to handle flag 'm' modifier. We also implement the block memory (BRAM) based character matching which saves a lot of LUTs and effectively improves overall system's throughput compared to related techniques. A software tool-chain for auto-generating PCRE matching system is also introduced. We do experiments on low-cost XC2VP50 Xilinx Virtex II Pro chip with the rule set of SNORT, an open source NIDS, to evaluate our implementation. The results verify that our architecture can achieve throughput up to 1Gbps and save up to 90% hardware resources compared with the conventional architecture.

Privacy Preserving Integration of Health Care Data

For health care related research studies the medical records of patients may need to be retrieved from multiple sites with different regulations on the disclosure of health information. Given the sensitive nature of health care information, privacy is a major concern when patients’ health care data is used for research purposes. In this paper, the authors propose approaches for integration and querying of health care data from multiple sources in a secure and privacy preserving manner. In particular, the first approach ensures secure data integration based on unique identifiers, and the second one considers data integration based on quasi identifiers, for which a rule-based framework is proposed for cross-linking data records, including secure character matching.

Do position-general radicals have a role to play in processing Chinese characters?

Over 90% of Chinese characters are compounds, comprising two or more constituents called radicals. Two experiments employed a character matching task to examine the contribution of radical position in Chinese character processing. The task was to decide whether the target character had appeared in two briefly and sequentially presented preceding source characters. Experiment 1 discovered significantly more false matching when the target (e.g., ‘’) shared radicals with the source stimuli (‘’, ‘’) than when the target and the source shared no radical, indicating that radicals contribute to character processing. Experiment 2 replicated this finding and further demonstrated that sharing a single radical between target and source characters, regardless of its radical position, was sufficient to generate false matching. More importantly, participants took significantly longer time to correctly reject those target characters with two shared radicals (one at the same position and another at the changed position) relative to those with only a single shared radical at the same position. Furthermore, false matching rates were significantly affected by lexical variables such as character frequency. These results suggest that position-general radicals play a significant role in character recognition and processing.

Mining periodic patterns with gap requirement from sequences

We study a problem of mining frequently occurring periodic patterns with a gap requirement from sequences. Given a character sequenceSof lengthLand a patternPof lengthl, we considerPa frequently occurring pattern inSif the probability ofobservingPgiven a randomly picked length-lsubsequence ofSexceeds a certain threshold. In many applications, particularly those related to bioinformatics, interesting patterns areperiodicwith agap requirement. That is to say, the characters inPshould match subsequences ofSin such a way that the matching characters inSare separated by gaps of more or less the same size. We show the complexity of the mining problem and discuss why traditional mining algorithms are computationally infeasible. We propose practical algorithms for solving the problem and study their characteristics. We also present a case study in which we apply our algorithms on some DNA sequences. We discuss some interesting patterns obtained from the case study.

An automatic MRI/SPECT registration algorithm using image intensity and anatomical feature as matching characters: application on the evaluation of Parkinson's disease

Single-photon emission computed tomography (SPECT) of dopamine transporters with (99m)Tc-TRODAT-1 has recently been proposed to offer valuable information in assessing the functionality of dopaminergic systems. Magnetic resonance imaging (MRI) and SPECT imaging are important in the noninvasive examination of dopamine concentration in vivo. Therefore, this investigation presents an automated MRI/SPECT image registration algorithm based on a new similarity metric. This similarity metric combines anatomical features that are characterized by specific binding, the mean count per voxel in putamens and caudate nuclei, and the distribution of image intensity that is characterized by normalized mutual information (NMI). A preprocess, a novel two-cluster SPECT normalization algorithm, is also presented for MRI/SPECT registration. Clinical MRI/SPECT data from 18 healthy subjects and 13 Parkinson's disease (PD) patients are involved to validate the performance of the proposed algorithms. An appropriate color map, such as "rainbow," for image display enables the two-cluster SPECT normalization algorithm to provide clinically meaningful visual contrast. The proposed registration scheme reduces target registration error from >7 mm for conventional registration algorithm based on NMI to approximately 4 mm. The error in the specific/nonspecific (99m)Tc-TRODAT-1 binding ratio, which is employed as a quantitative measure of TRODAT receptor binding, is also reduced from 0.45+/-0.22 to 0.08+/-0.06 among healthy subjects and from 0.28+/-0.18 to 0.12+/-0.09 among PD patients.

Conservation of helicity and helical character matching in chiral interactions

An electronic theory for molecular chirality and chiral interactions has recently been published that suggests that underlying chiral interactions is the conservation of molecular electronic helicity and that enantioselectivity in a reaction can be maximized when the interacting chiral components are helically matched. Herein are presented proofs for these principles, and their connections with other theories and implications for the design of chiral catalysts are discussed.

Printed Japanese Character Recognition Using Multiple Commercial OCRs

This paper proposes two algorithms for maintaining matching between lines and characters in text documents output by multiple commercial optical character readers (OCRs). (1) a line matching algorithm using dynamic programming (DP) matching and (2) a character matching algorithm using character string division and standard character strings. The paper proposes a method that introduces majority logic and reject processing in character recognition. To demonstrate the feasibility of the method, we conducted experiments on line matching recognition for 127 document images using five commercial OCRs. Results demonstrated that the method extracted character areas with more accuracy than a single OCR along with appropriate line matching. The proposed method enhanced recognition from 97.61% provided by a single OCR to 98.83% in experiments using the character matching algorithm and character recognition. This method is expected to be highly useful in correcting locations at which unwanted lines or characters occur or required lines or characters disappear.

CHINESE WORD SEARCHING IN IMAGED DOCUMENTS

An approach to searching for user-specified words in imaged Chinese documents, without the requirements of layout analysis and OCR processing of the entire documents, is proposed in this paper. A small number of Chinese characters that cannot be successfully bounded using connected component analysis due to larger gaps between elements within the characters are blacklisted. A suitable character that is not included in the blacklist is chosen from the user-specified word as the initial character to search for a matching candidate in the document. Once a matched candidate is found, the adjacent characters in the horizontal and vertical directions are examined for matching with other corresponding characters in the user-specified word, subject to the constraints of alignment (either horizontal or vertical direction) and size similarity. A weighted Hausdorff distance is proposed for the character matching. Experimental results show that the present method can effectively search the user-specified Chinese words from the document images with the format of either horizontal or vertical text lines, or both appearing on the same image.

Interhemispheric Interaction Affected by Identification of Chinese Characters

One aspect of interhemispheric processing that has recently received attention is whether it increases or decreases the processing power of the brain. The present study investigated whether dividing information between the hemispheres becomes more advantageous to task performance as computational complexity increases. A Chinese character matching task was given to subjects involving three levels of computational complexity (the pairs of characters being divided into three types, visually similar, homonymous, and synonymous). The result showed no difference in performance between within-field and across-field presentation for visually similar characters. Performance was significantly better for across field than within field with homophones and synonyms. However, the error rates of within field for phonetic matching was significantly higher than those for semantic matching. These results suggest that both the computational difficulty of materials and the intrinsic function of each hemisphere play a role in interhemispheric processing.