• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.252-259
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• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.525-538
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• 2020

A practical animal face classification system that classifies animals in image and video data is considered as a pivotal topic in machine learning. In this research, we are proposing a novel method of fully connected dual Deep Convolutional Neural Network (DCNN), which extracts and analyzes image features on a large scale. With the inclusion of the state of the art Batch Normalization layer and Exponential Linear Unit (ELU) layer, our proposed DCNN has gained the capability of analyzing a large amount of dataset as well as extracting more features than before. For this research, we have built our dataset containing ten thousand animal faces of ten animal classes and a dual DCNN. The significance of our network is that it has four sets of convolutional functions that work laterally with each other. We used a relatively small amount of batch size and a large number of iteration to mitigate overfitting during the training session. We have also used image augmentation to vary the shapes of the training images for the better learning process. The results demonstrate that, with an accuracy rate of 92.0%, the proposed DCNN outruns its counterparts while causing less computing costs.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.323-332
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• 2019

The purpose of this study was to examine the validity and reliability of the Revised Multidimensional Coping Scale(MCS-R) in university student who attended smoking cessation group therapy. Data were collected from 198 university students. Construct validity using exploratory factor analysis were conducted and convergent validity using Resilience scale, Patient Health Questionare-9(PHQ-9) were conducted. A Principal components analysis with varimax rotation and Kaiser normalization identified a thirteen-factor accounting for 69.7% of the variance in scores. Also, the internal consistency(0.66-0.94) and test-retest reliability was adequate(0.44-0.85) in all subscales of the MCS-R. The MCS-R has adequate psychometric characteristics so it can be used to verify.

• Archives of Craniofacial Surgery
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• v.25 no.1
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• pp.17-21
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• 2024

Background: The pain caused by local anesthetic injection can lead to patient anxiety prior to surgery, potentially necessitating sedation or general anesthesia during the excision procedure. In this study, we aim to compare the pain relief efficacy and safety of using a digital automatic anesthetic injector for local anesthesia. Methods: Thirty-three patients undergoing excision of a benign soft tissue tumor under local anesthesia were prospectively enrolled from September 2021 to February 2022. A single-blind, randomized controlled study was conducted. Patients were divided into two groups by randomization: the experimental group with digital automatic anesthetic injector method (I-JECT group) and the control group with conventional injection method. Before surgery, the Amsterdam preoperative anxiety information scale was used to measure the patients' anxiety. After local anesthetic was administered, the Numeric Pain Rating Scale was used to measure the pain. The amount of anesthetic used was divided by the surface area of the lesion was recorded. Results: Seventeen were assigned to the conventional group and 16 to the I-JECT group. The mean Numeric Pain Rating Scale was 1.75 in the I-JECT group and 3.82 in conventional group. The injection pain was lower in the I-JECT group (p< 0.01). The mean Amsterdam preoperative anxiety information scale was 11.00 in the I-JECT group and 9.65 in conventional group. Patient's anxiety did not correlate to injection pain regardless of the method of injection (p= 0.47). The amount of local anesthetic used per 1 cm² of tumor surface area was 0.74 mL/cm² in the I-JECT group and 2.31 mL/cm² in the conventional group. The normalization amount of local anesthetic was less in the I-JECT group (p< 0.01). There was no difference in the incidence of complications. Conclusion: The use of a digital automatic anesthetic injector has shown to reduce pain and the amount of local anesthetics without complication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.763-774
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• 2015

Hand posture recognition has been a wide region of applications in Human Computer Interaction and Computer Vision for many years. The problem arises mainly due to the high dexterity of hand and self-occlusions created in the limited view of the camera or illumination variations. To remedy these problems, a hand posture recognition method using 3-D point cloud is proposed to explicitly utilize 3-D information from depth maps in this paper. Firstly, hand region is segmented by a set of depth threshold. Next, hand image normalization will be performed to ensure that the extracted feature descriptors are scale and rotation invariant. By robustly coding and pooling 3-D facets, the proposed descriptor can effectively represent the various hand postures. After that, SVM with Gaussian kernel function is used to address the issue of posture recognition. Experimental results based on posture dataset captured by Kinect sensor (from 1 to 10) demonstrate the effectiveness of the proposed approach and the average recognition rate of our method is over 96%.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.7
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• pp.852-861
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• 2003

In this paper, in order to improve the disadvantages of the fixed design-parameter fuzzy PID controller. a new fuzzy PID controller named a variable design-parameter fuzzy PID controller is suggested. The main characteristic of the suggested controller is to adjust design-parameters of the controller by comparing magnitudes between fuzzy controller inputs at each sampling time when controller inputs are measured. As a result. all fuzzy input partitioned spaces converge within a time-varying normalization scale. and the resultant PID control action can always be applied precisely regardless of operating input magnitudes. In order to verify the effectiveness of the suggested controller. several a computer simulations for a nonlinear system are executed and the control parameters of the variable design-parameter fuzzy PID controller are throughly analyzed.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.869-872
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• 2000

Although many content-based image retrieval systems using shape feature have tried to cover rotation-, position- and scale-invariance between images, there have been problems to cover three kinds of variance at the same time. In this paper, we introduce new approach to extract shape feature from image using MBR(Minimum Bounding Rectangle). The proposed method scans image for extracting MBR information and, based on MBR information, compute contour information that consists of 16 points. The extracted information is converted to specific values by normalization and rotation. The proposed method can cover three kinds of invariance at the same time. We implemented our method and carried out experiments. We constructed R*_tree indexing structure, perform k-nearest neighbor search from query image, and demonstrate the capability and usefulness of our method.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2022.10a
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• pp.107-108
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• 2022

본 연구에서는 Arc GIS의 Network Analysis로 119안전센터의 재난 대응 권역을 설정해 재난 취약 면적을 계산하고 지역자율방재단의 재난피해 완충효과를 분석했다. 모든 값은 Min-Max Normalization 되어 동일한 Scale로 계산되었다. 지역자율방재단은 재난피해 완충 대책으로써 유의미한 효과가 있음을 확인했다. 지속적인 지역자율방재단의 활성화는 주민 참여, 지역특화적 재난 방재 대책 수립에 효과적이다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.25-36
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• 2008

Eye localization means localization of the center of the pupils, and is necessary for face recognition and related applications. Most of eye localization methods reported so far still need to be improved about robustness as well as precision for successful applications. In this paper, we propose a robust eye localization method using multi-scale Gabor feature vectors without big computational burden. The eye localization method using Gabor feature vectors is already employed in fuck as EBGM, but the method employed in EBGM is known not to be robust with respect to initial values, illumination, and pose, and may need extensive search range for achieving the required performance, which may cause big computational burden. The proposed method utilizes multi-scale approach. The proposed method first tries to localize eyes in the lower resolution face image by utilizing Gabor Jet similarity between Gabor feature vector at an estimated initial eye coordinates and the Gabor feature vectors in the eye model of the corresponding scale. Then the method localizes eyes in the next scale resolution face image in the same way but with initial eye points estimated from the eye coordinates localized in the lower resolution images. After repeating this process in the same way recursively, the proposed method funally localizes eyes in the original resolution face image. Also, the proposed method provides an effective illumination normalization to make the proposed multi-scale approach more robust to illumination, and additionally applies the illumination normalization technique in the preprocessing stage of the multi-scale approach so that the proposed method enhances the eye detection success rate. Experiment results verify that the proposed eye localization method improves the precision rate without causing big computational overhead compared to other eye localization methods reported in the previous researches and is robust to the variation of post: and illumination.

• ETRI Journal
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• v.37 no.2
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• pp.395-405
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• 2015

We address the problem of uncooperative person recognition through continuous monitoring. Multiple modalities, such as face, height, clothes color, and voice, can be used when attempting to recognize a person. In general, not all modalities are available for a given frame; furthermore, only some modalities will be useful as some frames in a video sequence are of a quality that is too low to be able to recognize a person. We propose a method that makes use of stochastic information updates of temporal modalities and environment estimators to improve person recognition performance. The environment estimators provide information on whether a given modality is reliable enough to be used in a particular instance; such indicators mean that we can easily identify and eliminate meaningless data, thus increasing the overall efficiency of the method. Our proposed method was tested using movie clips acquired under an unconstrained environment that included a wide variation of scale and rotation; illumination changes; uncontrolled distances from a camera to users (varying from 0.5 m to 5 m); and natural views of the human body with various types of noise. In this real and challenging scenario, our proposed method resulted in an outstanding performance.