• Journal of information and communication convergence engineering
• /
• v.14 no.1
• /
• pp.51-56
• /
• 2016

In this paper, we present a three-dimensional (3-D) automatic target recognition system based on optical integral imaging reconstruction. In integral imaging, elemental images of the reference and target 3-D objects are obtained through a lenslet array or a camera array. Then, reconstructed 3-D images at various reconstruction depths can be optically generated on the output plane by back-projecting these elemental images onto a display panel. 3-D automatic target recognition can be implemented using computational integral imaging reconstruction and digital nonlinear correlation filters. However, these methods require non-trivial computation time for reconstruction and recognition. Instead, we implement 3-D automatic target recognition using optical cross-correlation between the reconstructed 3-D reference and target images at the same reconstruction depth. Our method depends on an all-optical structure to realize a real-time 3-D automatic target recognition system. In addition, we use a nonlinear correlation filter to improve recognition performance. To prove our proposed method, we carry out the optical experiments and report recognition results.

• ETRI Journal
• /
• v.27 no.6
• /
• pp.806-809
• /
• 2005

The main goal of this research is automatic video analysis using a face recognition technique. In this paper, an automatic video management system is introduced with a variety of functions enabled, such as index, edit, summarize, and retrieve multimedia data. The automatic management tool utilizes MPEG-7 visual descriptors to generate a video index for creating a summary. The resulting index generates a preview of a movie, and allows non-linear access with thumbnails. In addition, the index supports the searching of shots similar to a desired one within saved video sequences. Moreover, a face recognition technique is utilized to personalbased video summarization and indexing in stored video data.

• Journal of Sasang Constitutional Medicine
• /
• v.20 no.1
• /
• pp.34-41
• /
• 2008

1. Objectives We had been developing a 3D Automatic Face Recognition Apparatus (3D-AFRA) in order to evaluate the external appearances with more objectivity. This apparatus provides a 3D image and numerical data on facial configuration, and this study aims to evaluate the accuracy of 3D-AFRA recognition. 2. Methods Each scanned pictures were pointed with the 3D Automatic Face Recognition Apparatus(3D-AFRA). And the results were compared with data pointed pictures with manual. And we analysed the difference between Automatic and manual by paired -test. 3. Results and conclusions In frontal face, the P-value was more than 0.05. In conclusion, We assessed that accuracy of recognition of 3D Automatic Face Recognition Apparatus(3D-AFRA) is considerably good. But we should develop methods of measurement for lateral face and indistinct points of frontal face.

• Journal of Sasang Constitutional Medicine
• /
• v.18 no.3
• /
• pp.57-63
• /
• 2006

1. Objectives The Face is an important standard for the classification of Sasang Contitutions. Now We are developing 3D Face Automatic Recognition Apparatus to analyse the facial characteristics. This apparatus show us 3D image of man's face and measure facial figure. We should examine accuracy of position recognition in 3D Face Automatic Recognition Apparatus. 2. Methods We took a photograph of Face status with Land Mark 8 times using Face Automatic Recognition Apparatus. Each taking-photo, We span Face statusby 10 degree. At last time, We took a photograph of Face status's lateral face. And We analysed Error Averige of Distance between seven Land Marks. So We examined the accuracy of position recognition in 3D Face Automatic Recognition Apparatus at indirectly in degree changing of Face status. 3. Results and Conclusions According to degree change of Face status, Error Averige of Distance between Seven Land Marks is 0.1848mm. In conclusion, We assessed that accuracy of position recognition in 3D Face Automatic Recognition Apparatus is considerably good in spite of degree changing of Face status

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.20 no.2
• /
• pp.207-213
• /
• 2011

In this paper, we present a study on the real-time implementation of mobile robot to which the interactive voice recognition technique is applied. The speech command utters the sentential connected word and asserted through the wireless remote control system. We implement an automatic distance speech command recognition system for voice-enabled services interactively. We construct a baseline automatic speech command recognition system, where acoustic models are trained from speech utterances spoken by a microphone. In order to improve the performance of the baseline automatic speech recognition system, the acoustic models are adapted to adjust the spectral characteristics of speech according to different microphones and the environmental mismatches between cross talking and distance speech. We illustrate the performance of the developed speech recognition system by experiments. As a result, it is illustrated that the average rates of proposed speech recognition system shows about 95% above.

• International Journal of Highway Engineering
• /
• v.15 no.1
• /
• pp.155-161
• /
• 2013

PURPOSES: This study is to develop a road traffic sign recognition and automatic positioning for road facility management. METHODS: In this study, we installed the GPS, IMU, DMI, camera, laser sensor on the van and surveyed the car position, fore-sight image, point cloud of traffic signs. To insert automatic position of traffic sign, the automatic traffic sign recognition S/W developed and it can log the traffic sign type and approximate position, this study suggests a methodology to transform the laser point-cloud to the map coordinate system with the 3D axis rotation algorithm. RESULTS: Result show that on a clear day, traffic sign recognition ratio is 92.98%, and on cloudy day recognition ratio is 80.58%. To insert exact traffic sign position. This study examined the point difference with the road surveying results. The result RMSE is 0.227m and average is 1.51m which is the GPS positioning error. Including these error we can insert the traffic sign position within 1.51m CONCLUSIONS: As a result of this study, we can automatically survey the traffic sign type, position data of the traffic sign position error and analysis the road safety, speed limit consistency, which can be used in traffic sign DB.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
• /
• 1998.04a
• /
• pp.314-320
• /
• 1998

Wear debris morphology is closely related to the wear mode and mechanism occured. Image recognition of wear debris is, therefore, a powerful tool in wear monitoring. But it has usually required expert's experience and the results could be too subjective. Development of automatic tools for wear debris recognition is needed to solve this problem. In this work, an algorithm for automatic wear debris recognition was suggested and implemented by PC base software. The presented method defined a characteristic 3-dimensional feature space where typical types of wear debris were separately located by the knowledge-based system and compared the similarity of object wear debris concerned. The 3-dimensional feature space was obtained from multiple feature vectors by using a multi-dimensional scaling technique. The results showed that the presented automatic wear debris recognition was satisfactory in many cases application.

• Tribology and Lubricants
• /
• v.15 no.2
• /
• pp.206-211
• /
• 1999

Wear debris morphology is closely related to the wear mode and mechanism occured. Image recognition of wear debris is, therefore, a powerful tool in wear monitoring. But it has usually required expert's experience and the results could be too subjective. Development of automatic tools for wear debris recognition is needed to solve this problem. In this work, an algorithm for automatic wear debris recognition was suggested and implemented by PC base software. The presented method defined a characteristic 3-dimensional feature space where typical types of wear debris were separately located by the knowledge-based system and compared the similarity of object wear debris concerned. The 3-dimensional feature space was obtained from multiple feature vectors by using a multi-dimensional scaling technique. The results showed that the presented automatic wear debris recognition was satisfactory in many cases application.

• ETRI Journal
• /
• v.46 no.1
• /
• pp.48-58
• /
• 2024

This paper presents the development of language tutoring systems for nonnative speakers by leveraging advanced end-to-end automatic speech recognition (ASR) and proficiency evaluation. Given the frequent errors in non-native speech, high-performance spontaneous speech recognition must be applied. Our systems accurately evaluate pronunciation and speaking fluency and provide feedback on errors by relying on precise transcriptions. End-to-end ASR is implemented and enhanced by using diverse non-native speaker speech data for model training. For performance enhancement, we combine semisupervised and transfer learning techniques using labeled and unlabeled speech data. Automatic proficiency evaluation is performed by a model trained to maximize the statistical correlation between the fluency score manually determined by a human expert and a calculated fluency score. We developed an English tutoring system for Korean elementary students called EBS AI Peng-Talk and a Korean tutoring system for foreigners called KSI Korean AI Tutor. Both systems were deployed by South Korean government agencies.

• Science of Emotion and Sensibility
• /
• v.14 no.2
• /
• pp.197-206
• /
• 2011

The purpose of the study was to suggest automatic recognition of the subject's level of arousal into high arousal and low arousal with neural network SOM learning. The automatic recognition in the level of arousal is composed of three stages. First, it is a stage of ECG measurement and analysis. It measures the subject playing a shooting game with ECG and extracts characteristics for SOM learning. Second, it is a stage of SOM learning. It learns input vectors extracting characteristics. Finally, it is a stage of arousal recognition which recognize the subject's level of arousal when new vectors are input after SOM learning is completed. The study expresses recognition results in the level of arousal and the level of arousal in numerical value and graph when SOM learning results in the level of arousal and new vectors are input. Finally, SOM evaluation was analyzed average 86% by comparing emotion evaluation results of the existing research with automatic recognition results of SOM in order. The study could experience automatic recognition with other levels of arousal by each subject with SOM.