• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.67-70
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• 2009

This paper proposes an algorithm for creating stereoscopic video from a monoscopic video. A viewer uses depth perception clues called a vanishing point which is the farthest from a viewer's viewpoint in order to perceive depth information from objects and surroundings thereof to the viewer. The viewer estimates the vanishing point with geometrical features in monoscopic images, and can perceive the depth information with the relationship between the position of the vanishing point and the viewer's viewpoint. In this paper, we propose a method to estimate a vanishing point with edge direction histogram in a general monoscopic image and to create a depth map depending on the position of the vanishing point. With the conversion method proposed through the experiment results, it is seen that stable stereoscopic conversion of a given monoscopic video is achieved.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.288-291
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• 2011

Video stabilization is an important image enhancement widely used in surveillance system in order to improve recognition performance. Most previous methods calculate inter-frame homography to estimate global motion. These methods are relatively slow and suffer from significant depth variations or multiple moving object. In this paper, we propose a fast and practical approach for video stabilization that selects the most reliable key frame as a reference frame to a current frame. We use optical flow to estimate global motion within an adaptively selected region of interest in static camera environment. Optimal global motion is found by probabilistic voting in the space of optical flow. Experiments show that our method can perform real-time video stabilization validated by stabilized images and remarkable reduction of mean color difference between stabilized frames.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.253-259
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• 2023

The increasing incorporation of AI in video storyboard creation has been observed recently. Traditionally, the production of storyboards requires significant time, cost, and specialized expertise. However, the integration of AI can amplify the efficiency of storyboard creation and enhance storytelling. In Korea, AiSAC stands at the forefront of AI-driven storyboard platforms, boasting the capability to generate realistic images built on open datasets foundations. Yet, a notable limitation is the difficulty in intricately conveying a director's vision within the storyboard. To address this challenge, we proposed the application of image generation features from ChatGPT and Midjourney to AiSAC. Through this research, we aimed to enhance the efficiency of storyboard production and refined the intricacy of expression, thereby facilitating advancements in the video production process.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.6
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• pp.637-643
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• 2010

In this study, we developed a Portable Multi-sensor System, which consists of a video camera, a GPS/MEMS IMU and a UMPC to acquire video images and position/attitude data. We performed image georeferencing based on the bundle adjustment without ground control points using the acquired data and then evaluated the effectiveness of our system through the accuracy verification. The experimental results showed that the RMSE of relative coordinates on the ground point coordinates obtained from our system was several centimeters. Our system can be efficiently utilized to obtain the 3D model of object and their relative coordinates. In future, we plan to improve the accuracy of absolute coordinates through the rigorous calibration of the system and camera.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1595-1613
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• 2017

The IR camera and laser-based IR projector provide an effective solution for real-time collection of moving targets in RGB-D videos. Different from the traditional RGB videos, the captured depth videos are not affected by the illumination variation. In this paper, we propose a novel feature extraction framework to describe human activities based on the above optical video capturing method, namely spatial-temporal texture features for 3D human activity recognition. Spatial-temporal texture feature with depth information is insensitive to illumination and occlusions, and efficient for fine-motion description. The framework of our proposed algorithm begins with video acquisition based on laser projection, video preprocessing with visual background extraction and obtains spatial-temporal key images. Then, the texture features encoded from key images are used to generate discriminative features for human activity information. The experimental results based on the different databases and practical scenarios demonstrate the effectiveness of our proposed algorithm for the large-scale data sets.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.677-687
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• 2022

We propose a detection algorithm based on tree-structured convolutional neural networks (TsCNNs) that finds pornography, propaganda, or other inappropriate content on a social media network. The algorithm sequentially applies the typical convolutional neural network (CNN) algorithm in a tree-like structure to minimize classification errors in similar classes, and thus improves accuracy. We implemented the detection system and conducted experiments on a data set comprised of 6 ordinary classes and 11 inappropriate classes collected from the Korean military social network. Each model of the proposed algorithm was trained, and the performance was then evaluated according to the images and videos identified. Experimental results with 20,005 new images showed that the overall accuracy in image identification achieved a high-performance level of 99.51%, and the effectiveness of the algorithm reduced identification errors by the typical CNN algorithm by 64.87 %. By reducing false alarms in video identification from the domain, the TsCNNs achieved optimal performance of 98.11% when using 10 minutes frame-sampling intervals. This indicates that classification through proper sampling contributes to the reduction of computational burden and false alarms.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.163-168
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• 2018

With the advent of the fourth industrial revolution era, the use of drone has been progressing rapidly in various fields. Now the drones will be used extensively in the area of investigation. Until now the criminal photographs stayed in 2D digital images, it would be possible to reproduce not only 3D images but also make a crime scene with 3D printer. Firstly, the video images taken by the investigation agency using the drones are digital image evidence, and the requirements for securing the evidence capability are not different from the conditions for obtaining the proof of digital evidence. However, when the drones become a new area of scientific investigation, it is essential to systematize the authenticity of the images taken by the drones so that they can be used as evidence. In this paper, I propose a method to secure the evidence capability of digital images taken by drone.

• Journal of Industrial Technology
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• v.29 no.A
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• pp.47-54
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• 2009

In this paper, we proposed hand detection and hand gesture recognition from USB camera video. Firstly, we extract hand region extraction using skin color information from a difference images. Background image is initially stored and extracted from the input images in order to reduce problems from complex backgrounds. After that, 16-directional chain code sequence is computed from the tracking of hand motion. These chain code sequences are compared with pre-trained models using DP matching. Our hand gesture recognition system can be used to control PowerPoint slides or applied to multimedia education systems. We got 92% hand region extraction accuracy and 82.5% gesture recognition accuracy, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.12
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• pp.1031-1043
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• 1990

A sequential recursive Kalman filtering algorithm, using causal image model, which is designed to operate in real time in the scanning mode is developed to enhance quality of 64Kbps videocodec images via function of suppression of various noises and optimum restoration. In order to improve its performance, adapted an averaging of pixel values between processing lines and adaptive filtering strategy based on the local spatial variance. Effecttiveness of the Kalman filtering algorithm proposed has been proved in the processed test kalman filtering algorithm proposed has been proved in the processed test images and the NMSE, LOGMSE measured, therefore, it may proposes possibility of the usage in videocodec for pre- and post- processing.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.835-838
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• 2000

Popular methods for extracting a text region in video images are in general based on analysis of a whole image such as merge and split method, and comparison of two frames. Thus, they take long computing time due to the use of a whole image. Therefore, this paper suggests the faster method of extracting a text region without processing a whole image. The proposed method uses line sampling methods, FFT and neural networks in order to extract texts in real time. In general, text areas are found in the higher frequency domain, thus, can be characterized using FFT The candidate text areas can be thus found by applying the higher frequency characteristics to neural network. Therefore, the final text area is extracted by verifying the candidate areas. Experimental results show a perfect candidate extraction rate and about 92% text extraction rate. The strength of the proposed algorithm is its simplicity, real-time processing by not processing the entire image, and fast skipping of the images that do not contain a text.