• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1245-1254
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• 2020

In this study, the limitations of the existing water level detection algorithm using CCTV images were recognized and the water level detection algorithm was improved by applying the Region growing technique. It applied three techniques (Horizontal projection profile, Texture analysis, and Optical flow) to estimate the water area, and the results were analyzed in a comprehensive analysis to select the initial water area. The water level was then continuously detected by the Region growing technique, referring to the initial water area. As a result, it was possible to confirm that the exact level of water was detected without being affected by environmental factors compared to the existing level detection algorithm, which had frequent mis-detection phenomena depending on the surrounding environmental factors. In addition, the water level was detected in the video showing flooded roads in urban areas, not in the video of the river. These results are believed to be able to supplement the difficulty of monitoring at all times with limited manpower by automatically detecting the level of water through numerous CCTV footage installed throughout the country, and to contribute to laying the foundation for preventing disasters caused by torrential rains and typhoons in advance.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.78-85
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• 2012

This paper proposes an efficient algorithm to develop a 3D terrain tools which is essential in the development of 3D computer games. In particular, this paper proposes a cross detection technique to improve picking speed. In other words, this paper proposes a more efficient cross detection technique consisting of a ray and a parallelogram than a cross detection technique consisting of a ray and a triangle. So, we can confirm the faster picking speed. This paper uses a picking example among DirectX SDK samples to test it. In addition, this paper compares the number of function calls for cross detection using the existing techniques and the proposed technique. As a result, in this paper the proposed technique has fallen off to about a 50 percent than the existing techniques. And if it is calculated by times, in this paper the proposed technique was reduced to 1 to 2 seconds than the existing techniques. Additionally, in this paper 3D terrain tools are provide more improved algorithms for features such as texture splatting, height map control, object arrangement and realistic water effect. So, 3D terrain tools is available efficient in the development of real 3d computer games.

• Journal of Korea Multimedia Society
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• v.7 no.4
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• pp.451-458
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• 2004

A leather quality inspection by naked eyes has known as unreliable because of its biological characteristics like accumulated fatigue caused from an optical illusion and biological phenomenon. Therefore it is necessary to automate the leather quality inspection by computer vision technique. In this paper, we present automatic leather qua1ity classification system get information from leather surface. Leather is usually graded by its information such as texture density, types and distribution of defects. The presented algorithm explain how we analyze leather information like texture density and defects from the gray-level images obtained by digital camera. The density data is computed by its ratio of distribution area, width, and height of Fourier spectrum magnitude. And the defect information of leather surface can be obtained by histogram distribution of pixels which is Windowed from preprocessed images. The information for entire leather could be a standard for grading leather quality. The proposed leather inspection system using machine vision can also be applied to another field to substitute human eye inspection.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.6
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• pp.545-550
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• 2013

We present a tactile information transceiver using a friction-tunable slider-pad. While previous tactile information devices were focused on either input or output functions, the present device offers lateral position/vertical direction detection and texture expression. In characterizing the tactile input performance, we measured the capacitance change due to the displacement of the slider-pad. The measured difference for a z-axis click was 0.146 nF/$40{\mu}m$ when the x-y axis navigation showed 0.09 nF/$750{\mu}m$ difference. In characterizing the texture expression, we measured the lateral force due to a normal load. We applied a voltage between parallel electrodes to induce electrostatic attraction in DC and AC voltages. We measured the friction under identical fingertip action conditions, and obtained friction in the range of 32-152 mN and lateral vibration in the force range of 128.1 mN at 60 V, 2 Hz. The proposed device can be applied to integrated tactile interface devices for mobile appliances.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.363-366
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• 2001

The present process of grading leather quality by the rare eyes is not reliable. Because inconsistency of grading due to eyes strain for long time can cause incorrect result of grading. Therefore it is necessary to automate the process of grading quality of leather based on objective standard for it. In this paper, leather automatic classification system consists of the process obtaining the information of leather and the process grading the quality of leather from the information. Leather is graded by its information such as texture density, types and distribution of defects. This paper proposes the algorithm which sorts out leather information like texture density and defects from the gray-level images obtained by digital camera. The density information is sorted out by the distribution value of Fourier spectrum which comes out after original image is converted to the image in frequency domain. And the defect information is obtained by the statistics of pixels which is relevant to Window using searching Window after sort out boundary lines from preprocessed images. The information for entire leather is used as standard of grading leather quality, and the proposed algorithm is practically applied to machine vision system.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.363-372
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• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.131-139
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• 2016

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5580-5593
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• 2019

A malicious Uniform Resource Locator (URL) recognition and detection method based on the combination of Attention mechanism with Convolutional Neural Network and Long Short-Term Memory Network (Attention-Based CNN-LSTM), is proposed. Firstly, the WHOIS check method is used to extract and filter features, including the URL texture information, the URL string statistical information of attributes and the WHOIS information, and the features are subsequently encoded and pre-processed followed by inputting them to the constructed Convolutional Neural Network (CNN) convolution layer to extract local features. Secondly, in accordance with the weights from the Attention mechanism, the generated local features are input into the Long-Short Term Memory (LSTM) model, and subsequently pooled to calculate the global features of the URLs. Finally, the URLs are detected and classified by the SoftMax function using global features. The results demonstrate that compared with the existing methods, the Attention-based CNN-LSTM mechanism has higher accuracy for malicious URL detection.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.58-66
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• 2020

The blur effect, which is generated by various external factors such as out-of-focus and object movement, degrades high-frequency components in the original sharp image. Based on this observation, we propose a novel method for blur detection using textural features. Specifically, the proposed method simultaneously adopts learning-based and watershed-based textural features, which effectively detect the blur on various situations. Moreover, we employ the region-based refinement to improve the processing time while also increasing detection accuracy. Experimental results demonstrate that the proposed method provides the competitive performance compared to previous approaches in literature.

• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.61-65
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• 2016

Facial feature detection is a fundamental function in the field of computer vision such as security, bio-metrics, 3D modeling, and face recognition. There are many algorithms for the function, active shape model is one of the most popular local texture models. This paper addresses issues related to face detection, and implements an efficient extraction algorithm for extracting the facial feature points to use on iOS platform. In this paper, we extend the original ASM algorithm to improve its performance by four modifications. First, to detect a face and to initialize the shape model, we apply a face detection API provided from iOS CoreImage framework. Second, we construct a weighted local structure model for landmarks to utilize the edge points of the face contour. Third, we build a modified model definition and fitting more landmarks than the classical ASM. And last, we extend and build two-dimensional profile model for detecting faces within input images. The proposed algorithm is evaluated on experimental test set containing over 500 face images, and found to successfully extract facial feature points, clearly outperforming the original ASM.