• Journal of Information Processing Systems
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• v.19 no.4
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• pp.554-562
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• 2023

This research offers a sports video action recognition approach based on the fish swarm algorithm in light of the low accuracy of existing sports video action recognition methods. A modified fish swarm algorithm is proposed to construct invariant features and decrease the dimension of features. Based on this algorithm, local features and global features can be classified. The experimental findings on the typical sports action data set demonstrate that the key details of sports action can be successfully retained by the dimensionality-reduced fusion invariant characteristics. According to this research, the average recognition time of the proposed method for walking, running, squatting, sitting, and bending is less than 326 seconds, and the average recognition rate is higher than 94%. This proves that this method can significantly improve the performance and efficiency of online sports video motion recognition.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.127-136
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• 2023

Many research results on the traffic enforcement of illegal driving of twowheeled motor vehicles using license plate recognition are introduced. Deep learning convolutional neural networks can be used for character and word recognition of license plates because of better generalization capability compared to traditional Backpropagation neural networks. In the plates of twowheeled motor vehicles, the interdependent government and city words are included. If we implement the mutually independent word recognizers using error correction rules for two word recognition results, efficient license plate recognition results can be derived. The CPU based convolutional neural network without library under real time processing has an advantage of low cost real application compared to GPU based convolutional neural network with library. In this paper twowheeled motor vehicle license plate recognition algorithm is introduced using CPU based deep-learning convolutional neural network. The experimental results show that the proposed plate recognizer has 96.2% success rate for outdoor twowheeled motor vehicle images in real time.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.53-62
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• 2006

Object recognition techniques using principal component analysis are disposed to be decreased recognition rate when lighting change of image happens. The purpose of this thesis is to propose an object recognition technique using new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. And the object recognition algorithm proposed here represents more enhanced recognition rate using improved k-Nearest Neighbor. In this thesis, we proposed an object recognition algorithm which creates object space by pre-processing and being learned image using histogram equalization and median filter. By spreading histogram of test image using histogram equalization, the effect to change of illumination is reduced. This method is stronger to change of illumination than basic PCA method and normalization, and almost removes effect of illumination, therefore almost maintains constant good recognition rate. And, it compares ingredient projected test image into object space with distance of representative value and recognizes after representative value of each object in model image is made. Each model images is used in recognition unit about some continual input image using improved k-Nearest Neighbor in this thesis because existing method have many errors about distance calculation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.59-68
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• 1999

Recently, research and developmental direction of communication system is concurrent adopting voice data and face image in speaking to provide more higher recognition rate then in the case of only voice data. Therefore, we present a method of lipreading in speech image sequence by using the 3-D facial shape model. The method use a feature information of the face image such as the opening-level of lip, the movement of jaw, and the projection height of lip. At first, we adjust the 3-D face model to speeching face image sequence. Then, to get a feature information we compute variance quantity from adjusted 3-D shape model of image sequence and use the variance quality of the adjusted 3-D model as recognition parameters. We use the intensity inclination values which obtaining from the variance in 3-D feature points as the separation of recognition units from the sequential image. After then, we use discrete HMM algorithm at recognition process, depending on multiple observation sequence which considers the variance of 3-D feature point fully. As a result of recognition experiment with the 8 Korean vowels and 2 Korean consonants, we have about 80% of recognition rate for the plosives and vowels. We propose that usability with visual distinguishing factor that using feature vector because as a result of recognition experiment for recognition parameter with the 10 korean vowels, obtaining high recognition rate.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.713-725
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• 2019

Automatic License Plate Recognition (ALPR) is a technology required for many applications such as Intelligent Transportation Systems and Video Surveillance Systems. Most of the studies have studied were about the detection and recognition of license plates on cars, and there is very little about detecting and recognizing license plates on motorbikes. In the case of a car, the license plate is located at the front or rear center of the vehicle and is a straight or slightly sloped license plate. Also, the background of the license plate is mainly monochromatic, and license plate detection and recognition process is less complicated. However since the motorbike is parked by using a kickstand, it is inclined at various angles when parked, so the process of recognizing characters on the motorbike license plate is more complicated. In this paper, we have developed a 2-stage YOLOv2 algorithm to detect the area of a license plate after detection of a motorbike area in order to improve the recognition accuracy of license plate for motorbike data set parked at various angles. In order to increase the detection rate, the size and number of the anchor boxes were adjusted according to the characteristics of the motorbike and license plate. Image warping algorithms were applied after detecting tilted license plates. As a result of simulating the license plate character recognition process, the proposed method had the recognition rate of license plate of 80.23% compared to the recognition rate of the conventional method(YOLOv2 without image warping) of 47.74%. Therefore, the proposed method can increase the recognition of tilted motorbike license plate character by using the adjustment of anchor boxes and the image warping which fit the motorbike license plate.

• Research in Community and Public Health Nursing
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• v.15 no.4
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• pp.587-599
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• 2004

Purpose: The purpose of this study is to compare abdomen-fat rate, life style and social-support between normal weight women and obese women. Method: 304 women objectives from their 30 to 59 years of age were selected living in Je-chon city, Chung-Buck province and their height and weight were measured from April 1st to June 30th, 2003. Data were classified into low-weight group ($18.5kg/m^2$), normal-weight group ($18.5{\sim}22.9kg/m^2$), over weight group ($23{\sim}24.9kg/m^2$), and obese group ($25kg/m^2$) following the Korean Conference of Obesity, 2001. in which 119 people in the normal weight group and 91 people in the obese group, i.e. total 210 people were analyzed in sequence. Using SPSS Win 10.1 Program, frequency and percentile, and by ANOVA, $X^2-test$ and t-test were treated. Results: The average age of obese women was 46.68 distributing 40.7% of forties and 39.6% of fifties while normal-weight women were average 41.73-year old distributing 53.8% of forties and 34.5% of thirties, which revealed aged in obese women. The body fat rate of obese women averaged $37.52{\pm}4.17%$, in which 98.9% of obese women and 21.0% of normal weight women with a more than 30% of body-fat rate resulted in a higher body-fat rate in obese women. The waists of obese women averaged $88.37{\pm}8.22cm$, in which more than 85cm showed in obese women of 68.2% and normal weight women of 7.6% indicating a higher waist-fat rate in obese women. The abdomen-fat rate of more than 0.85 of waist vs hip-fat showed 74.7% in obese women and 58.4% in normal weight women, indicating a higher abdomen-fat rate in obese women. Obese women and normal weight women showed significant differences in education level, number of children, religion, menstrual status, and mother's weight. Especially, obese women ate hotter or saltier food than normal weight women preferring meat. However, no significant differences appeared in marital status, social economic status. occupation. eating habits. smoking. drinking and physical exercise. Social support levels showed a lower rate in obese women than in normal weight women, indicating a statistically significant difference (p<.05). Observing areas of social support, obese women showed lower rates in attachment/intimacy, social integrity, opportunity of foster and confidence in value except help and instruction, which indicated a statistically significant difference (p<.05). Social support for obese women showed significant differences in age, education level, social hierarchy, religion and menstrual status. Obese women were more negative than normal weight women in health recognition, indicating a statistically significant difference (p<.01). Normal weight women showed higher health recognition when provided high social support and significantly low (p<.01) health recognition when provided low social support. However, there was no significant difference in health recognition in obese women whether high or low social support was given. The health recognition of obese women showed significant differences in age, education level, social hierarchy, number of children, menstrual status, physical exercise, eating habits, eating taste and preference of food. Conclusion: Obese women showed elder than normal-weight women, higher body-fat rate and abdomen-fat rate, lower social support, and a tendency to more negative health recognition. Therefore, providing weight-control programs for the treatment of obesity and prevention of recurrence for obese women to prevent progressing to adult disease and promote a healthy life, we suggest that better eating habits and the encouragement of regular physical exercise should be included, as well as total approachment on change of health recognition and social support would be needed.

• Journal of Korea Multimedia Society
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• v.16 no.8
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• pp.905-913
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• 2013

The Parallel Model Compensation Using Frequency-variant: FV-PMC for noise-robust speech recognition is a method to classify the noises, which are expected to be intermixed with input speech when recognized, into several groups of noises by setting average frequency variant as a threshold value; and to recognize the noises depending on the classified groups. This demonstrates the excellent performance considering noisy speech categorized as good using the standard threshold value. However, it also holds a problem to decrease the average speech recognition rate with regard to unclassified noisy speech, for it conducts the process of speech recognition, combined with noiseless model as in the existing PMC. To solve this problem, this paper suggests a enhanced method of recognition to prevent the unclassified through improving the extent of rating scales with use of maximum likelihood so that the noise groups, including input noisy speech, can be classified into more specific groups, which leads to improvement of the recognition rate. The findings from recognition experiments using Aurora 2.0 database showed the improved results compared with those from the method of the previous FV-PMC.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.343-348
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• 2014

Face recognition system has advanctage to automatically recognize a person without causing repulsion at deteciton process. However, the face recognition system has a drawback to show lower perfomance according to illumination variation unlike the other biometric systems using fingerprint and iris. Therefore, this paper proposed a robust face recogntion method against illumination varition by slective fusion technique using both visible and infrared faces based on fuzzy linear disciment analysis(fuzzy-LDA). In the first step, both the visible image and infrared image are divided into four bands using wavelet transform. In the second step, Euclidean distance is calculated at each subband. In the third step, recognition rate is determined at each subband using the Euclidean distance calculated in the second step. And then, weights are determined by considering the recognition rate of each band. Finally, a fusion face recognition is performed and robust recognition results are obtained.

• Journal of Korea Multimedia Society
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• v.8 no.7
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• pp.898-905
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• 2005

Iris recognition Is that identifies a user based on the unique iris muscle patterns which has the functionalities of dilating or contracting pupil region. Because it is reported that iris recognition is more accurate than other biometries such as face, fingerprint, vein and speaker recognition, iris recognition is widely used in the high security application domain. However, if unnecessary information such as eyelid and eyelash is included in iris region, the error for iris recognition is increased, consequently. In detail, if iris region is used to generate iris code including eyelash and eyelid, the iris codes are also changed and the error rate is increased. To overcome such problem, we propose the method of detecting eyelid by using pyramid searching parabolic deformable template. In addition, we detect the eyelash by using the eyelash mask. Experimental results show that EER(Equal Error Rate) for iris recognition using the proposed algorithm is lessened as much as $0.3\%$ compared to that not using it.

• Journal of KIISE:Software and Applications
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• v.37 no.11
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• pp.859-865
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• 2010

In this paper, a new method for finger vein recognition is proposed. Researchers are recently interested in the finger vein recognition since it is a good way to avoid the forgery in finger prints recognition and the inconveniences in obtaining images of the iris for iris recognition. The vein images are processed to obtain the line shaped vein images through the local histogram equalization and a thinning process. This thinned vein images are processed for matching, using a new matching algorithm, named HS(HeeSung) matching algorithm. This algorithm yields an excellent recognition rate when it is applied to the curve-linear images processed through a thinning or an edge detection. In our experiment with the finger vein images, the recognition rate has reached up to 99.20% using this algorithm applied to 650finger vein images(130person ${\times}$ 5images each). It takes only about 60 milliseconds to match one pair of images.