• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2509-2528
• /
• 2019

There are various styles of license plates for different countries and use cases that require style-specific methods. In this paper, we propose and illustrate a multi-style license plate recognition system. The proposed system performs a series of processes for license plate candidates detection, structure classification, character segmentation and character recognition, respectively. Specifically, we introduce a license plate structure classification process to identify its style that precedes character segmentation and recognition processes. We use a K-Nearest Neighbors algorithm with pre-training steps to recognize numbers and characters on multi-style license plates. To show feasibility of our multi-style license plate recognition system, we evaluate our system for multi-style license plates covering single line, double line, different backgrounds and character colors on Korean and the U.S. license plates. For the evaluation of Korean license plate recognition, we used a 50 minutes long input video that contains 138 vehicles of 6 different license plate styles, where each frame of the video is processed through a series of license plate recognition processes. From two experiments results, we show that various LP styles can be recognized under 50 ms processing time and with over 99% accuracy, and can be extended through additional learning and training steps.

• Journal of Multimedia Information System
• /
• v.8 no.2
• /
• pp.85-92
• /
• 2021

In a face, there is much information of person's identity. Because of this property, various tasks such as expression recognition, identity recognition and deepfake have been actively conducted. Most of them use the exact frontal view of the given face. However, various directions of the face can be observed rather than the exact frontal image in real situation. The profile (side view) lacks information when comparing with the frontal view image. Therefore, if we can generate the frontal face from other directions, we can obtain more information on the given face. In this paper, we propose a combined style model based the conditional generative adversarial network (cGAN) for generating the frontal face from multi-view images that consist of characteristics that not only includes the style around the face (hair and beard) but also detailed areas (eye, nose, and mouth).

• Journal of the Korean School Mathematics Society
• /
• v.16 no.2
• /
• pp.363-381
• /
• 2013

The constituents of math learning styles are information recognition and information processing in the cognitive domain and attitudes toward math learning and environments of math learning in the affective domain. Each of the constituents has two opposing styles; there are the visual style and verbal style in information recognition; and there are the whole style and analytical style in information processing. And as for attitudes toward math learning, there are two styles which are the authoritative and goal-oriented style and the practical and entertaining style. Also as for attitudes toward environments of math learning, there are two styles which are the interior-oriented style and exterior-oriented style. There can be classified into 16 types of mathematics learning by the combination of a total of 8 styles of mathematics learning. The purpose of this study was to analyze the preference of the students from three middle schools located in Daegu Metropolitan City to the styles and types of mathematics learning.

• Journal of Information Processing Systems
• /
• v.19 no.5
• /
• pp.663-672
• /
• 2023

Most vehicle detection methods have poor vehicle feature extraction performance at night, and their robustness is reduced; hence, this study proposes a night vehicle detection method based on style transfer image enhancement. First, a style transfer model is constructed using cycle generative adversarial networks (cycleGANs). The daytime data in the BDD100K dataset were converted into nighttime data to form a style dataset. The dataset was then divided using its labels. Finally, based on a YOLOv5s network, a nighttime vehicle image is detected for the reliable recognition of vehicle information in a complex environment. The experimental results of the proposed method based on the BDD100K dataset show that the transferred night vehicle images are clear and meet the requirements. The precision, recall, mAP@.5, and mAP@.5:.95 reached 0.696, 0.292, 0.761, and 0.454, respectively.

• Journal of Internet Computing and Services
• /
• v.6 no.3
• /
• pp.147-158
• /
• 2005

Day after day the information of vehicle under the complex traffic environments is greatly required not only for traffic flow but also for vehicle disclosure of traffic violation, Vehicle information can be obtained from a recognition of vehicle license plate, This paper proposes a new vehicle plate recognition mechanism that uses moving style vehicle-loaded camera, The method is a real-time processing system using multi-step image processing and recognition process that recognizes general vehicles and special purpose vehicles, The experimental results of real environmental image and recognition using the proposed method are shown.

• International Commerce and Information Review
• /
• v.9 no.3
• /
• pp.233-254
• /
• 2007

According to the empirical study, the research model has been verified about the introduction of new ubiquitous technology into the Customs Information System. The research model is meaningful in deducing policy for accepting the new ubiquitous technology into the Customs Information System. The organization style is not yet positively accomplished, because of insufficient recognition about the new ubiquitous instrument. The other side the individual style, environmental style and working characteristics are contribute to accept the new ubiquitous environment, because of sufficient of education, usage and recognizing the usefulness of the portal information. It is established the Customs Information System with new ubiquitous technology such as RFID, DM, UCR etc.. for the usefulness and convenience.

• Communications of Mathematical Education
• /
• v.29 no.1
• /
• pp.111-130
• /
• 2015

The purpose of the study was to investigate the differences between two groups of students according to information recognition styles such as visual learners and linguistic learners. Two instructional methods, algeblocks and factorization formula, were utilized to introduce the factorization. Four students were participated for the study, and two of them were visual learners and the other two were linguistic learners based on learning style test. Interviews and the diagnostic tests were implemented before the instructions which were lasted for 6 sessions. After the instructions all the participants were interviewed and the researchers also interviewed them 5 days later. The results of the study were the followings: 1. All the participants regardless of their learning style revealed that algeblocks were helpful in understanding the factorization. 2. Visual learners were more likely using algeblocks, while the linguistic learners were more enthusiastic and proficient in using formula to solve the problems. 3. Five days later, two types of learning style students revealed different tendencies. Visual learners mainly used algeblocks, and linguistic learners were not enthusiastic about using algeblocks and one of them did not use them at all. 4. Five days later, two visual learners could not remember the formula, but linguistic learners could remember the formula in somewhat different level.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.3
• /
• pp.83-89
• /
• 2016

Internet of Things (IoT) is widely used for biomechanics in sports activities and AHRS(Attitude and Heading Reference System) is a more cost effective solution than conventional high-grade IMUs (Inertial Measurement Units) that only integrate gyroscopes. In this paper, we attach the AHRS to the snowboard to measure the motion data like Air To Fakie, Caballerial and Free Style. In order to reduce the measurement error, we have adopted the sensors equipped with Kalman filtering and also used Euler angle to quaternion conversion to reduce the Gimbal-lock effect. We have tested and evaluated the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it on the basic motions of Snowboarding from the 9-axis trajectory information which is gathered from AHRS sensor. With the result, PCA, ICA have low accuracy, but SVM have good accuracy to use for recognition of basic motions of Snowboarding.

• The KIPS Transactions:PartB
• /
• v.18B no.3
• /
• pp.109-116
• /
• 2011

In this paper, we introduce non-touch style interface system technology without any touch style controlling mechanism, which is called as "Air-interface". To develop this system, we used the full reflection principle of infrared (IR) light and then user's hand is separated from the background with the obtained image at every frame. The segmented hand region at every frame is used as input data for an hand-motion recognition module, and the hand-motion recognition module performs a suitable control event that has been mapped into the specified hand-motion through verifying the hand-motion. In this paper, we introduce some developed and suggested methods for image processing and hand-motion recognition. The developed air-touch technology will be very useful for advertizement panel, entertainment presentation system, kiosk system and so many applications.

• Journal of Korea Society of Industrial Information Systems
• /
• v.17 no.3
• /
• pp.35-42
• /
• 2012

The database for driving patterns can be utilized in various system such as automatic driving system, driver safety system, and it can be helpful to monitor driving style. Therefore, we propose a driving pattern recognition system in which the sensor streams from a smartphone are recorded and used for recognizing driving events. In this paper we focus on the driving pattern recognition that is an essential and preliminary step of driving style recognition. We divide input sensor streams into 7 driving patterns such as, Left-turn(L), U-turn(U), Right-turn(R), Rapid-Braking(RB), Quick-Start(QS), Rapid-Acceleration (RA), Speed-Bump(SB). To classify driving patterns, first, a preprocessing step for data smoothing is followed by an event detection step. Last the detected events are classified by DTW(Dynamic Time Warping) algorithm. For assisting drivers we provide the classified pattern with the corresponding video stream which is recorded with its sensor stream. The proposed system will play an essential role in the safety driving system or driving monitoring system.