• KIPS Transactions on Software and Data Engineering
• /
• v.1 no.2
• /
• pp.127-136
• /
• 2012

Many research of hand gesture recognition based on vision system have been conducted which enable user operate various electronic devices more easily. 3D position calculation and meaningful gesture classification from similar gestures should be executed to recognize hand gesture accurately. A simple and cost effective method of 3D position calculation and gesture spotting (a task to recognize meaningful gesture from other similar meaningless gestures) is described in this paper. 3D position is achieved by calculation of two cameras relative position through pan/tilt module and a marker regardless with the placed position. Fuzzy garbage model is proposed to provide a variable reference value to decide whether the user gesture is the command gesture or not. The reference is achieved from fuzzy command gesture model and fuzzy garbage model which returns the score that shows the degree of belonging to command gesture and garbage gesture respectively. Two-stage user adaptation is proposed that off-line (batch) adaptation for inter-personal difference and on-line (incremental) adaptation for intra-difference to enhance the performance. Experiment is conducted for 5 different users. The recognition rate of command (discriminate command gesture) is more than 95% when only one command like meaningless gesture exists and more than 85% when the command is mixed with many other similar gestures.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.1-19
• /
• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.11 no.6
• /
• pp.175-183
• /
• 2016

The government set up "national happiness, the hope of a new era of national vision under' job center of the creative economy" to achieve by national goals in the first and figure achieved through the establishment of new growth engines of the youth unemployment problem solve and national level there are a number of business start-up support. September 8, 2015 announced the Government's look at the '2016 Year of the budget, the government for new growth engines greatly promoted the venture entrepreneurship ecosystem revitalization and research and development (R & D) the business for enhanced performance in 2017. According to the direction of this study is to evaluate the current creative economy business incubator at the comparison whether the correct orientation mainly entrepreneurs, entrepreneurs preliminary recognition of student entrepreneurship ecosystem. Entrepreneurs 113 people in that way, 71 people pre-entrepreneurs, students 60, workers were founding agencies conducted an online survey of 47 people, 16 people Investors, 50 public and 11 additional persons including a total of 368 people. This study is in line with the orientation of these entrepreneurs to create economic status and recognition of the Republic of Korea entrepreneurship ecosystem, pre- entrepreneurs, students will examine the comparative analysis around. Analysis, social perception of entrepreneurship is somewhat higher than it was confirmed that the negative response of 32.2% to 36.3% of positive response. Social awareness of entrepreneurs showed a 2-fold higher response rate than the negative of response of 17.1% to 41.7% responding that positive recognition for the current start-up environment is bad, the response is good response to higher response rate than 23.5% to 41.1% It showed. The percentage of responses that better respect the entrepreneurship environment of the future Republic of Korea showed a higher response rate than the rate of 23% in response to deteriorate to 41.2%, with 52.9% awareness is the percentage that responded that the bad part about the ruthless Korea's entrepreneurship environment in China good part as response rate approximately three times greater than the 17.7% showed high response rates. Social awareness of entrepreneurs experience the presence of the founding start-up experience was confirmed that the more negative the number increases, the more the contrary the number of start-up experience increased awareness of the current and future environment of entrepreneurship was identified as a positive entrepreneurship environment. Also recognized was confirmed to change the parent of the more positive changes in the start-up of entrepreneurs doctor also positive about entrepreneurship, start-up entrepreneurs start with a doctor's motivation for founding non-economic reasons than for economic reasons has confirmed Higher. This study showed the overall level overview analysis of the status and recognition of the Republic of Korea entrepreneurship ecosystem. Future studies need to be a proposal for an existing previous studies for more precise direction to go forth to analyze the entrepreneurship ecosystem with a focus on problems and improvement of the Republic of Korea entrepreneurship ecosystem.

• Journal of KIISE
• /
• v.44 no.9
• /
• pp.932-945
• /
• 2017

In this paper, a new hardware and software architecture for a stereo vision processing system including rectification, disparity estimation, and visualization was developed. The developed method, named LArge scale stereo matching method using Support Point Interpolation (LASPI), shows excellence in real-time processing for obtaining dense disparity maps from high quality image regions that contain high density support points. In the real-time processing of high definition (HD) images, LASPI does not degrade the quality level of disparity maps compared to existing stereo-matching methods such as Efficient LArge-scale Stereo matching (ELAS). LASPI has been designed to meet a high frame-rate, accurate distance resolution performance, and a low resource usage even in a limited resource environment. These characteristics enable LASPI to be deployed to safety-critical applications such as an obstacle recognition system and distance detection system for autonomous vehicles. A Field Programmable Gate Array (FPGA) for the LASPI algorithm has been implemented in order to support parallel processing and 4-stage pipelining. From various experiments, it was verified that the developed FPGA system (Xilinx Virtex-7 FPGA, 148.5MHz Clock) is capable of processing 30 HD ($1280{\times}720pixels$) frames per second in real-time while it generates disparity maps that are applicable to real vehicles.

• Journal of Bio-Environment Control
• /
• v.24 no.3
• /
• pp.210-216
• /
• 2015

Purpose of this study was a development of an image processing algorithm to recognize paprika and acquire it's 3D coordinates from stereo images to precisely control an end-effector of a paprika auto harvester. First, H and S threshold was set using HSI histogram analyze for extracting ROI(region of interest) from raw paprika cultivation images. Next, fundamental matrix of a stereo camera system was calculated to process matching between extracted ROI of corresponding images. Epipolar lines were acquired using F matrix, and $11{\times}11$ mask was used to compare pixels on the line. Distance between extracted corresponding points were calibrated using 3D coordinates of a calibration board. Non linear regression analyze was used to prove relation between each pixel disparity of corresponding points and depth(Z). Finally, the program could calculate horizontal(X), vertical(Y) directional coordinates using stereo camera's geometry. Horizontal directional coordinate's average error was 5.3mm, vertical was 18.8mm, depth was 5.4mm. Most of the error was occurred at 400~450mm of depth and distorted regions of image.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.1
• /
• pp.35-45
• /
• 2013

A autonomous vehicle requires improved and robust perception systems than conventional perception systems of intelligent vehicles. In particular, single sensor based perception systems have been widely studied by using cameras and laser radar sensors which are the most representative sensors for perception by providing object information such as distance information and object features. The distance information of the laser radar sensor is used for road environment perception of road structures, vehicles, and pedestrians. The image information of the camera is used for visual recognition such as lanes, crosswalks, and traffic signs. However, single sensor based perception systems suffer from false positives and true negatives which are caused by sensor limitations and road environments. Accordingly, information fusion systems are essentially required to ensure the robustness and stability of perception systems in harsh environments. This paper describes a perception system for autonomous vehicles, which performs information fusion to recognize road environments. Particularly, vision and laser radar sensors are fused together to detect lanes, crosswalks, and obstacles. The proposed perception system was validated on various roads and environmental conditions with an autonomous vehicle.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.45 no.3
• /
• pp.12-19
• /
• 2008

Detection of a number plate consists of three stages; division of a number plate, extraction of each character from the plate, recognition of the characters. Among of these three states, division stage of a number plate is the most important part and also the most time-consuming state. This paper suggests an effective region extraction method of a number plate for various images obtained from unmanned inspection systems of illegal parking violation, especially when we have to consider the diverse surrounding environments of roads. Our approaching method detects each region by investigating the characteristics in changes of brightness and intensity between the background part and character part, and the characteristics on character parts such as the sizes, heights, widths, and distance in between two characters. The method also divides a number plate into different types of the plate. This research can solve the number plate region detection failure problems caused by plate edge damages not only for Korean domestic number plates but also for new European style number plates. The method also reduces the time consumption by processing the detection in real-time, therefore, it can be used as a practical solution.

• Journal of Korean society of Dental Hygiene
• /
• v.9 no.2
• /
• pp.179-191
• /
• 2009

The purpose of this study was to examine the needs of dental hygiene students at a lifelong education center of a three-year-course college for a credit bank system. The subjects in this study were 200 dental hygiene students at a college located in J, which offered courses of a credit bank system. A survey was conducted from May 19 to 23, 2008, to gather data on the acquisition of a bachelor's degree and the credit bank system, and the answer sheets from 184 respondents(92%) were collected. After the collected data were analyzed with SPSS/WIN 12.0 program, the following findings were given: 1. In regard to an intention of taking a bachelor's degree, the largest number of the students investigated(74.5%) intended to do that if they had any chance. As for the reason, 55.6% wanted to get the degree in pursuit of their own personal development. 2. Concerning how to win a bachelor's degree, the largest group that accounted for 63.0% preferred the credit bank systems of college lifelong education centers. 41.8% got interested in the credit bank system mainly because their acquaintances informed them of it. It shows that colleges should reinforce publicity activities if they want to offer the credit bank system. 3. The quality of educational programs and cost had an impact on the choice of an educational institution when they planned to get a bachelor's degree from the credit bank system. Therefore excellent educational programs should be provided, and the government should provide learners with economic help and fund educational institutions. 4. As to comparison of a regular college and the credit bank system as a way to get a bachelor's degree, that was considered to be helpful for finding a job(a mean of 3.39) and for the development of sociability(3.22). That was also deemed to be of use for the improvement of practical job performance, to win public recognition and to be helpful for being well-cultivated, though not many students had those opinions. They took a different view according to academic year(p<.05). 5. Regarding the expected effects of getting a degree from the credit bank system, the greatest group expected it to step up their personal development(3.85). The second largest group expected it to boost job efficacy(3.30), and the students whose academic year was higher had a better opinion. 6. As for future directions for the credit bank system, the largest group put emphasis on the improvement of social perception through intensive P.R. and the enhancement of the qualifications of professors and lecturers(4.02). These opinions were more stressed by the juniors than by the sophomores and seniors, and academic year made a significant difference to their views(p<.05).

• Journal of Biosystems Engineering
• /
• v.29 no.4
• /
• pp.341-346
• /
• 2004

Recent livestock people concern not only increase of production, but also superior quality of animal-breeding environment. So far, the optimization of the breeding and air environment has been focused on the production increase. In the very near future, the optimization will be emphasized on the environment for the animal welfare and health. Especially, cattle farming demands the precision livestock farming and special attention has to be given to the management of feeding, animal health and fertility. The management of individual animal is the first step for precision livestock farming and animal welfare, and recognizing each individual is important for that. Though electronic identification of a cattle such as RFID(Radio Frequency Identification) has many advantages, RFID implementations practically involve several problems such as the reading speed and distance. In that sense, computer vision might be more effective than RFID for the identification of an individual animal. The researches on the identification of cattle via image processing were mostly performed with the cows having black-white patterns of the Holstein. But, the native Korean and Japanese cattle do not have any definite pattern on the body. The purpose of this research is to identify the Japanese black cattle that does not have a body pattern using computer vision technology and neural network algorithm. Twelve heads of Japanese black cattle have been tested to verify the proposed scheme. The values of input parameters were specified and then computed using the face images of cattle. The images of cattle faces were trained using associate neural network algorithm, and the algorithm was verified by the face images that were transformed using brightness, distortion, and noise factors. As a result, there was difference due to transform ratio of the brightness, distortion, and noise. And, the proposed algorithm could identify 100% in the range from -3 to +3 degrees of the brightness, from -2 to +4 degrees of the distortion, and from 0% to 60% of the noise transformed images. It is concluded that our system can not be applied in real time recognition of the moving cows, but can be used for the cattle being at a standstill.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.1
• /
• pp.124-132
• /
• 2002

In this paper, we present a vision algorithm and method for input image improvement and preprocessing of dented and raised characters on the sidewall of tires. we define optical condition between reflect coefficient and reflectance by the physical vector calculate. On the contrary this work will recognize the engraved characters using the computer vision technique. Tire input images have all most same grey levels between the characters and backgrounds. The reflectance is little from a tire surface. therefore, it's very difficult segment the characters from the background. Moreover, one side of the character string is raised and the other is dented. So, the captured images are varied with the angle of camera and illumination. For optimum Input images, the angle between camera and illumination was found out to be with in 90$^{\circ}$. In addition, We used complex filtering with low-pass and high-pass band filters to improve input images, for clear input images. Finally we define equation reflect coefficient and reflectance. By doing this, we obtained good images of tires for pattern recognition.