• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.706-711
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• 2010

In the applications of a human speech interface, reducing the error rate in recognition is the one of the main research issues. Many previous studies attempted to correct errors using post-processing, which is dependent on a manually constructed corpus and correction patterns. We propose an automatically learnable post-processing method that is independent of the characteristics of both the domain and the speech recognizer. We divide the entire post-processing task into two steps: error detection and error correction. We consider the error detection step as a classification problem for which we apply the conditional random fields (CRFs) classifier. Furthermore, we apply transformation-based learning (TBL) to the error correction step. Our experimental results indicate that the proposed method corrects a speech recognizer's insertion, deletion, and substitution errors by 25.85%, 3.57%, and 7.42%, respectively.

• Journal of Biosystems Engineering
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• v.32 no.1 s.120
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• pp.44-49
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• 2007

A stereo vision system was developed for robot milking system (RMS) using two monochromatic cameras. An algorithm for inverse perspective transformation was developed for the 3-D information acquisition of all teats. To verify performance of the algorithm in the stereo vision system, indoor tests were carried out using a test-board and model teats. A real cow and a model cow were used to measure distance errors. The maximum distance errors of test-board, model teats and real teats were 0.5 mm, 4.9 mm and 6 mm, respectively. The average distance errors of model teats and real teats were 2.9 mm and 4.43 mm, respectively. Therefore, it was concluded that this algorithm was sufficient for the RMS to be applied.

• ETRI Journal
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• v.38 no.4
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• pp.703-713
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• 2016

In this paper, we present a segmentation-based method for offline Farsi handwritten word recognition. Although most segmentation-based systems suffer from segmentation errors within the first stages of recognition, using the inherent features of the Farsi writing script, we have segmented the words into sub-words. Instead of using a single complex classifier with many (N) output classes, we have created N simple recurrent neural network classifiers, each having only true/false outputs with the ability to recognize sub-words. Through the extraction of the number of sub-words in each word, and labeling the position of each sub-word (beginning/middle/end), many of the sub-word classifiers can be pruned, and a few remaining sub-word classifiers can be evaluated during the sub-word recognition stage. The candidate sub-words are then joined together and the closest word from the lexicon is chosen. The proposed method was evaluated using the Iranshahr database, which consists of 17,000 samples of Iranian handwritten city names. The results show the high recognition accuracy of the proposed method.

• The Journal of Advanced Prosthodontics
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• v.9 no.6
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• pp.409-415
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• 2017

PURPOSE. Accurate information is essential in dentistry. The image information of missing teeth is used in optically based medical equipment in prosthodontic treatment. To evaluate oral scanners, the standardized model was examined from cases of image recognition errors of linear discriminant analysis (LDA), and a model that combines the variables with reference to ISO 12836:2015 was designed. MATERIALS AND METHODS. The basic model was fabricated by applying 4 factors to the tooth profile (chamfer, groove, curve, and square) and the bottom surface. Photo-type and video-type scanners were used to analyze 3D images after image capture. The scans were performed several times according to the prescribed sequence to distinguish the model from the one that did not form, and the results confirmed it to be the best. RESULTS. In the case of the initial basic model, a 3D shape could not be obtained by scanning even if several shots were taken. Subsequently, the recognition rate of the image was improved with every variable factor, and the difference depends on the tooth profile and the pattern of the floor surface. CONCLUSION. Based on the recognition error of the LDA, the recognition rate decreases when the model has a similar pattern. Therefore, to obtain the accurate 3D data, the difference of each class needs to be provided when developing a standardized model.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.10-18
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• 2010

Due to a large number of classes in Hangul character recognition, it is usual to use the six-type preclassification stage. After the preclassification, the first consonent, vowel, and last consonent can be classified separately. Though each of three components has a few of classes, classification errors occurs often due to shape similarity such as 'ㅔ' and 'ㅖ'. So this paper proposes a hierarchical recognition method which adopts multi-stage tree structures for each of 6-types. In addition, to reduce the interference among three components, the method uses the recognition results of first consonents and vowel as features of vowel classifier. The recognition accuracy for the test set of PHD08 database was 98.96%.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.317-321
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• 2020

This study researched the correlation between myopic refractive errors and intraocular pressure. The study population comprised 39 adults(17 of males, 22 of females). We measured the intraocular pressure using a Non-Contact Tonometer(NCT) and the correlation between myopic refractive errors was analyzed by dividing into three groups: mild, moderate, high myopia. The gender of subjects showed no statistically difference between the intraocular pressure and refractive errors, but as the refractive errors increased, the intraocular pressure incereased, which showed a statistically significant difference. In addition, the higher intraocular pressure in moderate and high myopia than mild myopia can cause glaucoma, that can develop at a young age. it is need to sufficient recognition and understanding correlation between intraocular pressure and myopic refractive errors in the middle-aged high myopia.

• The Journal of the Convergence on Culture Technology
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• v.4 no.3
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• pp.241-246
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• 2018

Because of the mechanical problems of input camera equipment, image restoration process is performed in order to minimize recognition errors due to the noise problem generated in test data image. The image restoration method resolves the noise problem by examining the numbers and positions of the Direct neighbors and the Indirect neighbors for each pixel constituting the test data. As a result, satisfactory recognition result can be obtained by eliminating the noise problem generated in the test data through the image restoration process as much as possible and also by calculating the differences between the learning data and the test data in the area unit, thereby reducing the possibility of recognition error by the noise problem.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1439-1446
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• 2011

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the higher correlation feature information and Principle Component Analysis. Since the suggested method doesn't require a lot of computation than the method using existing geometric information or stereo image, the fact that it is very suitable for building the real-time system has been proved through the experiment. In addition, since the existing point to point method which is a simple distance calculation has many errors, in this paper to improve recognition rate the recognition error could be reduced by using several successive input images as a unit of recognition with K-Nearest Neighbor which is the improved Class to Class method.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.400-409
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• 2010

This paper presents a position error recognition system when the wafer is mounted in cleaning equipment among the wafer manufacturing processes. The proposed system is to enhance the performance in cost and reliability by preventing the wafer cleaning system from damaging by alerting it when it is put in correct position. The key algorithms are the calibration method between image acquired from camera and physical wafer, a infrared lighting and the design of the filter, and the extraction of wafer boundary and the position error recognition resulting from generation of circle based on least square method. The system is to install in-line process using high reliable and high accurate position recognition. The experimental results show that the performance is good in detecting errors within tolerance.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.226-231
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• 2011

This research was done to prove the optimal position to detect a train reader when using a multiple fusion sensor. The experiment was done using four Train installed RFID Readers located on the train. These readers were read by sensors installed at intervals of 50 meters on the up and down sections of the Line 8, from Amsa station to Moran station. We analyze errors in the recognition range according to the Tag's number of recognition due to RFID of train speed, and propose a method of estimation for an accurate estimation of the position of train At this the Least-Squares Method is applied to judge the position of train accurately from the error because of Tag's number of recognition and RFID of train speed. also It is verified through simulation.