• Journal of Digital Convergence
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• v.10 no.7
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• pp.123-128
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• 2012

In this paper, we propose a landmark-detection system of object for more accurate object recognition. The landmark-detection system of object becomes divided into a learning stage and a detection stage. A learning stage is created an interest-region model to set up a search region of each landmark as pre-information necessary for a detection stage and is created a detector by each landmark to detect a landmark in a search region. A detection stage sets up a search region of each landmark in an input image with an interest-region model created in the learning stage. The proposed system uses Fast Fourier Transform to detect landmark, because the landmark-detection is fast. In addition, the system fails to track objects less likely. After we developed the proposed method was applied to environment video. As a result, the system that you want to track objects moving at an irregular rate, even if it was found that stable tracking. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.51-57
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• 2021

In this paper, we propose a method to find the DoF(Depth of field) that is blurred in an image by focusing and out-focusing the camera through a efficient convolutional neural network. Our approach uses the RGB channel-based cross-correlation filter to efficiently classify the DoF region from the image and build data for learning in the convolutional neural network. A data pair of the training data is established between the image and the DoF weighted map. Data used for learning uses DoF weight maps extracted by cross-correlation filters, and uses the result of applying the smoothing process to increase the convergence rate in the network learning stage. The DoF weighted image obtained as the test result stably finds the DoF region in the input image. As a result, the proposed method can be used in various places such as NPR(Non-photorealistic rendering) rendering and object detection by using the DoF area as the user's ROI(Region of interest).

• Journal of Multimedia Information System
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• v.7 no.1
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• pp.41-44
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• 2020

In autonomous driving, object recognition based on machine learning is one of the core software technologies. In particular, the object recognition using deep learning becomes an essential element for autonomous driving software to operate. In this paper, we introduce a drivable area detection method based on Region-based CNN model to support autonomous driving. To effectively detect the drivable area, we used the BDD dataset for model training and demonstrated its effectiveness. As a result, our R-CNN model using BDD datasets showed interesting results in training and testing for detection of drivable areas.

• ETRI Journal
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• v.29 no.5
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• pp.700-702
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• 2007

We present a relevance feedback approach based on multi-class support vector machine (SVM) learning and cluster-merging which can significantly improve the retrieval performance in region-based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low-level features and high-level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re-clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two-class SVM and multi-class relevance feedback methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.94-101
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• 1992

The multilayer perceptron, trained by the error back-propagation learning rule, has been known as a mapping network which can represent arbitrary functions. However depending on the complexity of a function and the initial weights of the multilayer perceptron, the error back-propagation learning may fall into a local minimum or a flat area which may require a long learning time or lead to unsuccessful learning. To solve such difficulties in training the multilayer perceptron by standard error back-propagation learning rule, the paper proposes a learning method which progressively enlarges the learning domain from a small area to the entire region. The proposed method is devised from the investigation on the roles of hidden nodes and connection weights in the multilayer perceptron which approximates a function of one variable. The validity of the proposed method was illustrated through simulations for a function of one variable and a function of two variable with many extremal points.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12C
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• pp.1250-1257
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• 2003

Generally images are classified into two regions: edge and flat region. While low frequency components are popular in the flat region, high frequency components are quite important in the edge region. Therefore, deinterlacing algorithm that considers the characteristic of each region can be more efficient. In this paper, an image is divided into edge region and flat region by the local variance. And then, for each region, frequency modularized neural network is assigned. Using this structure, each modularized neural network can learn only its region intensively and avoid the complexity of learning caused by the data of different region. Using the local AC data for the input of neural network can prevent the degradation of the performance of teaming due to the average intensity values of image that disturbs the effective learning. The proposed method shows the improved performance compared with previous algorithms in the simulation.

• Journal of the Korean association of regional geographers
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• v.11 no.4
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• pp.536-545
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• 2005

The aim of this Study is to suggest regional learning methods using Landsat ETM and IKONOS images in the 7th Social Studies Curriculum in high school. In the program of 10 grade social studies, curriculum is constructed on the focus of conceptual learning, excluded in practice and activities on the regional learning. Regional learning, which is to goal understanding of regional environments and establishment of identities, is an essential part in student's field work and investigation activities, but with difficulties in application of schooling in the present curriculum, intended to propose substitute methods with satellite images. This study suggests learning methods for perception of region with the resolution of satellite images. The results of the study may help to extend learning and interests on the geography with practical application to GIS and RS.

• Imaging Science in Dentistry
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• v.54 no.1
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• pp.33-41
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• 2024

Purpose: The aims of this study were to create a deep learning model to distinguish between nasopalatine duct cysts (NDCs), radicular cysts, and no-lesions (normal) in the midline region of the anterior maxilla on panoramic radiographs and to compare its performance with that of dental residents. Materials and Methods: One hundred patients with a confirmed diagnosis of NDC (53 men, 47 women; average age, 44.6±16.5 years), 100 with radicular cysts (49 men, 51 women; average age, 47.5±16.4 years), and 100 with normal groups (56 men, 44 women; average age, 34.4±14.6 years) were enrolled in this study. Cases were randomly assigned to the training datasets (80%) and the test dataset (20%). Then, 20% of the training data were randomly assigned as validation data. A learning model was created using a customized DetectNet built in Digits version 5.0 (NVIDIA, Santa Clara, USA). The performance of the deep learning system was assessed and compared with that of two dental residents. Results: The performance of the deep learning system was superior to that of the dental residents except for the recall of radicular cysts. The areas under the curve (AUCs) for NDCs and radicular cysts in the deep learning system were significantly higher than those of the dental residents. The results for the dental residents revealed a significant difference in AUC between NDCs and normal groups. Conclusion: This study showed superior performance in detecting NDCs and radicular cysts and in distinguishing between these lesions and normal groups.

• Journal of the Korean association of regional geographers
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• v.10 no.2
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• pp.466-478
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• 2004

Regional learning in social studies has an important educational meaning in that it is a tabloid edition of social studies and it also satisfies the regionalization of national curriculum. Social studies in Korea have mainly been led by the social study curriculum of elementary school. But the local textbook which was currently used in elementary school is structured in a negative meaning of regionalization rather than positive one. It is suggested in this study that the regional learning of social studies in middle school should be pursued by the co-work of teachers and students. For this purpose, the theoretical and practical processes to develop the local textbook are compared between two distinctive localities such as Sancheong and Seosan. At first, the relative ratio among the six strands is decided to develop several themes for regional learning, considering the landscape, region-related discourses and ecological environments in each region. Secondly, several themes are extracted to organize the contents of local textbook in each region. Lastly, examples of content-organization are suggested in each region. The processes above are just an example of content-organization, not a fixed one. The process, themes extracted, and the content-organization for each region may be changed according to the school location, local situation, and the quality of classroom.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.125-133
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• 2022

This study attempted to find out whether learning time has a mediating effect according to the gender, region, and grade of middle school students in the relationship between academic engagement and self-esteem. To this end, a survey of 1,045 middle school students in Gyeongsangnam-do was conducted on academic engagement, learning time, and self-esteem. Difference verification was conducted to determine the difference in academic engagement, learning time, and self-esteem according to the general characteristics of the study subjects, correlation analysis was conducted to determine the correlation between major variables, and regression analysis was conducted to verify the mediating effect of learning time. As a result of the analysis, first, there was no difference in the academic engagement of middle school students by group. In the learning time, middle school students in the city area were significantly higher than those in the township area, male students had higher self-esteem than female students, and students in the city area had significantly higher self-esteem as the grade went up. Second, as a result of correlation analysis, learning time, academic engagement, and self-esteem showed a positive correlation. Third, in the entire group not divided by group, both the direct path through which academic engagement reaches self-esteem and the partial mediating model from learning time to self-esteem showed significant effects. In the analysis by gender, only female students excluding male students showed a partial mediating effect, and the analysis results by region showed a partial mediating effect only on students in the city. The analysis results by grade showed a partial mediating effect only for second-year middle school students. In order to improve the self-esteem of middle school students, education and counseling should be conducted in consideration of not only individual differences by gender and grade, but also the region in which they live.