• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.47-55
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• 2000

In this paper, a vision-based roadway detection algorithm for an automated vehicle control system, based on roadway sign information on roads, is proposed. First, in order to detect roadway signs, the color scene image is enhanced under hue-invariance. Fuzzy logic is employed to simplify the enhanced color image into a binary image and the binary image is morphologically filtered. Then, an effective algorithm of locating signs based on binary rank order transform (BROT) is utilized to extract signs from the image. This algorithm performs better than those previously presented. Finally, the inner shapes of roadway signs with curving roadway direction information are recognized by neural networks. Experimental results show that the new detection algorithm is simple and robust, and performs well on real sign detection. The results also show that the neural networks used can exactly recognize the inner shapes of signs even for very noisy shapes.

• ETRI Journal
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• v.36 no.5
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• pp.696-703
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• 2014

In this paper, we present a walking guidance system for the visually impaired for use at subway stations. This system, which is based on environmental knowledge, automatically detects and recognizes both exit numbers and arrow signs from natural outdoor scenes. The visually impaired can, therefore, utilize the system to find their own way (for example, using exit numbers and the directions provided) through a subway station. The proposed walking guidance system consists mainly of three stages: (a) sign detection using the MCT-based AdaBoost technique, (b) sign recognition using support vector machines and hidden Markov models, and (c) three verification techniques to discriminate between signs and non-signs. The experimental results indicate that our sign recognition system has a high performance with a detection rate of 98%, a recognition rate of 99.5%, and a false-positive error rate of 0.152.

• Journal of Korea Multimedia Society
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• v.15 no.3
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• pp.312-322
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• 2012

The paper presents an approach for recognizing the broken area of the traffic signs. The method is based on the Recognition System for Traffic Signs (RSTS). This paper describes an approach to using the location histogram matching for the broken traffic signs recognition, after the general process of the image detection and image categorization. The recognition proceeds by using the SIFT matching to adjust the acquired image to a standard position, then the histogram bin will be compared preprocessed image with reference image, and finally output the location and percents value of the broken area. And between the processing, some preprocessing like the blurring is added in the paper to improve the performance. And after the reorganization, the program can operate with the GPS for traffic signs maintenance. Experimental results verified that our scheme have a relatively high recognition rate and a good performance in general situation.

• Korean Journal of Veterinary Research
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• v.36 no.3
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• pp.719-729
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• 1996

To investigate changes of estrus signs and genital organs in the bitch by hormonal induction of estrus, fourteen bitches of nulliparous and multiparous(2nd-5th) were grouped into diestrus and anestrus according to their estrus cycle. The hormonal treatments were divided into four groups: group A($PGF_2{\alpha}+PMSG+hCG$) and group B(PMSG+hCG) in diestrus bitches and group C(GnRH+FSH+hCG) and group D(PMSG+hCG) in anestrus bitches. The external signs of proestrus and estrus as well as the vaginal smear findings and natural breeding as estrus detection were investigated in all the experimental groups. Also, genital organs were examined at two months after the hormone treatment. The bitches in anestrus showed 100% of male attraction, vaginal bleeding and vulvar swelling as proestrus signs after the hormonal treatment for estrus induction and they showed higher numerical value of signs than the bitches in diestrus. The group A showed the lowest value in proestrous signs of all the groups. The bitches in anestrus treated with GnRH+FSH showed 100% of positive estrus by vaginal smear findings and 75% of natural breeding as estrus detection index and these values were the highest of all the groups. Pregnancy was recognized in only group C and the conception rate was 7.14% in al the experimental animals. Of the side effects after the hormone treatment, external findings of continous male attraction, continous external swelling and purulent exudate were recognized in all the experimental groups and the bitches in diestrus showed higher value of the findings than the bitches in anestrus. Of the changes of genital organs after the hormone treatment, hypertrophy of uterine horn, sanguineous exudate and purulent exudate as uterine findings were recognized in all the groups and these findings were shown more in the bitches in diestrus than in those in anestrus. These results indicated that group C showed the highest value of all the experimental groups in external signs of estrus and estrus detection and also pregnancy was recognized only in that group, consequently, that the hormonal treatment of group C would be the most effective for estrus induction, and also indicated that bitches in anestrus were more suitable than bitches in diestrus for the induction of estrus. In addition, side effects in external genital organs and uteri after hormone treatment were shown more in the bitches in diestrus than in those in anestrus, indicating that bitches in anestrus would be of choice for estrus induction.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.12
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• pp.1259-1266
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• 2014

Road signs provide important safety information about road and traffic conditions to drivers. Road signs include not only common traffic signs but also warning information regarding unexpected obstacles and road constructions. Therefore, accurate detection and identification of road signs is one of the most important research topics related to safe driving. In this paper, we propose a 3-D vision technique to automatically detect and track road signs in a video sequence which is acquired from a stereo vision camera mounted on a vehicle. First, color information is used to initially detect the sign candidates. Second, the SVM (Support Vector Machine) is employed to determine true signs from the candidates. Once a road sign is detected in a video frame, it is continuously tracked from the next frame until it is disappeared. The 2-D position of a detected sign in the next frame is predicted by the 3-D motion of the vehicle. Here, the 3-D vehicle motion is acquired by using the 3-D pose information of the detected sign. Finally, the predicted 2-D position is corrected by template-matching of the scaled template of the detected sign within a window area around the predicted position. Experimental results show that the proposed method can detect and track many types of road signs successfully. Tracking comparisons with two different methods are shown.

• Asian Oncology Nursing
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• v.12 no.1
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• pp.52-60
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• 2012

Purpose: This study is to determine knowledge about early detection and risk perception of cancer according to taking cancer screening tests in the general population. Methods: The participants were 151 people aged 40 years or older. A questionnaire consisted of knowledge about early detection (warning signs, cancer screening methods, general knowledge for early detection), cancer risk perception and history of cancer screening during past 2 years. Results: The percentages of correct answers were 64.7% in knowledge about warning signs, 73.7% in knowledge of cancer screening tests and 80.1% in general knowledge for early detection. Participants had the highest knowledge about screening methods for stomach cancer and the lowest for liver and colon cancer. The level of risk perception was medium. The participants who participated in cancer screening showed lower risk perception than those who did not. There was no significant relationship between knowledge and performance of cancer screening. The primary reason for not participating in cancer screening was patient's perception of their own health. Conclusion: These results suggest that cancer risk perception can affect the performance of cancer screening and we need to study how to handle this problem. Additionally screening programs should focus on liver cancer and colon cancer.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.41-51
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• 2010

In the research for driver assistance systems, traffic sign information to the driver must be a very important information. Therefore, the detection system of traffic signs located on the road should be able to handel real-time. To detect the traffic signs, color and shape of traffic signs is to use the information after images obtained using the CCD camera. In the road environment, however, using color information to detect traffic sings will cause many problems due to changes of weather and environmental factors. In this paper, to solve it, the candidate traffic sign regions are detected from road images obtained in a variety of the illumination changes using the HSI eign-color model. And then, using the invariant moment-based SVM classifier to detect traffic signs are proposed. Experimental results show that, traffic sign detection rate is 91%, and the processing time per frame is 0.38sec. Proposed method is useful for real-time intelligent traffic guidance systems can be applied.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1630-1636
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• 2022

The results of current method of traffic sign detection gets hindered by environmental conditions and the traffic sign's condition as well. Therefore, in this paper, we propose a method of improving detection performance of damaged traffic signs by utilizing Hopfield Network and Fuzzy Max-Min Neural Network. In this proposed method, the characteristics of damaged traffic signs are analyzed and those characteristics are configured as the training pattern to be used by Fuzzy Max-Min Neural Network to initially classify the characteristics of the traffic signs. The images with initial characteristics that has been classified are restored by using Hopfield Network. The images restored with Hopfield Network are classified by the Fuzzy Max-Min Neural Network onces again to finally classify and detect the damaged traffic signs. 8 traffic signs with varying degrees of damage are used to evaluate the performance of the proposed method which resulted with an average of 38.76% improvement on classification performance than the Fuzzy Max-Min Neural Network.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.5
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• pp.29-37
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• 2009

This paper describes a vision-based and real-time system for detecting road signs from within a moving vehicle. The proposed system has the standard architecture with adaboost algorithm to detect road signs in real time. And it uses the value of vortical plane in the process of extracting candidate areas in view of fact that there are vertically most of signs on roads. Although being useful for detecting objects in real time, the conventional adaboost algorithm deteriorates the performance of detection rate in complex circumstance by reason of using only integral images as features. To overcome this problem, this paper proposes the method that improves the reliability of candidates as using the value of vertical plane for extracting candidate area and improves the performance of the detection rate as using integral images to which we add the kind of feature prototype. The experiments of this paper show that the detection rate of the proposed method has higher than that of the conventional adaboost algorithm under the real complex circumstance of roads.

• Journal of KIISE:Software and Applications
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• v.36 no.1
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• pp.82-91
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• 2009

Sign language spotting is the task of detecting and recognizing the signs in a signed utterance. The difficulty of sign language spotting is that the occurrences of signs vary in both motion and shape. Moreover, the signs appear within a continuous gesture stream, interspersed with transitional movements between signs in a vocabulary and non-sign patterns(which include out-of-vocabulary signs, epentheses, and other movements that do not correspond to signs). In this paper, a novel method for designing a threshold model in a conditional random field(CRF) model is proposed. The proposed model performs an adaptive threshold for distinguishing between signs in the vocabulary and non-sign patterns. A hand appearance-based sign verification method, a short-sign detector, and a subsign reasoning method are included to further improve sign language spotting accuracy. Experimental results show that the proposed method can detect signs from continuous data with an 88% spotting rate and can recognize signs from isolated data with a 94% recognition rate, versus 74% and 90% respectively for CRFs without a threshold model, short-sign detector, subsign reasoning, and hand appearance-based sign verification.