• Convergence Security Journal
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• v.22 no.4
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• pp.141-146
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• 2022

The iris, a biometric information, is safe, unique, and reliable, such as fingerprints, and is personal information that can significantly lower the misrecognition rate than other biometric authentication. However, due to the nature of biometric authentication, it is impossible to replace it if it is stolen. There is a case in which an actual iris photo is taken and 3d printed so that the eyes work as if they were in front of the camera. As such, there is a possibility of iris leakage through high-definition images and photos. In this paper, we propose to improve iris masking performance by supplementing iris region masking research based on existing blurring techniques. Based on the results derived in this study, it is expected that it can be used for the security of video conference programs and electronic devices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.84-90
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• 2020

As the uploading and downloading of data through the Internet is becoming more common, data including personal information are easily exposed to unauthorized users. In this study, we detect a target area in images that contain personal information, except for the background, and we protect the detected target area by using a blocking method suitable for the surrounding situation. In this method, only the target area from color image input containing personal information is segmented based on skin color. Subsequently, blurring of the corresponding area is performed in multiple stages based on the surrounding situation to effectively block the detected area, thereby protecting the personal information from being exposed. Experimental results show that the proposed method blocks the object region containing personal information 2.3% more accurately than an existing method. The proposed method is expected to be utilized in fields related to image processing, such as video security, target surveillance, and object covering.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.249-255
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• 2013

In this paper, we propose an algorithm to detect scale invariant object from IR image obtained in the sea environment. We create horizontal edge (HL), vertical edge (LH), diagonal edge (HH) of images through 2-D discrete Haar wavelet transform (DHWT) technique after noise reduction using morphology operations. Considering the sea environment, Gaussian blurring to the horizontal and vertical edge images at each level of wavelet is performed and then saliency map is generated by multiplying the blurred horizontal and vertical edges and combining into one image. Then we extract object candidate region by performing a binarization to saliency map. A small area in the object candidate region are removed to produce final result. Experiment results show the feasibility of the proposed algorithm.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.1-8
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• 2010

Recently, the character image processing technology using portable device camera image at home and abroad are actively conducted, but Practical use are lower rate because of accuracy and time-consuming process problems. In this paper, we propose the license plate recognition method based on geometric information from portable device camera image. In the extracted license plate region we recognize characters using the chain code and the Thickness information through the cumulative projected edge after performing the pre-processing work considering the angle difference, the contrast enhancement and the low resolution from portable device camera image. The proposed algorithm is effective and accurate recognition by light and reducing the processing time. And, the results from the character recognition success rate was 95%. In the future, we will research about license plate recognition algorithm using long distance image or added motion blur image.

• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.555-564
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• 2014

The boundary noise in image syntheses using DIBR consists of noisy pixels that are separated from foreground objects into background region. It is generated mainly by edge misalignment between the reference image and depth map or blurred edge in the reference image. Since hole areas are generally filled with neighboring pixels, boundary noise adjacent to the hole is the main cause of quality degradation in synthesized images. To solve this problem, a new boundary noise removal algorithm using a preprocessing of the depth map is proposed in this paper. The most common way to eliminate boundary noise caused by boundary misalignment is to modify depth map so that the boundary of the depth map can be matched to that of the reference image. Most conventional methods, however, show poor performances of boundary detection especially in blurred edge, because they are based on a simple boundary search algorithm which exploits signal gradient. In the proposed method, a two-step hierarchical approach for boundary detection is adopted which enables effective boundary detection between the transition and background regions. Experimental results show that the proposed method outperforms conventional ones subjectively and objectively.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1392-1400
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• 2011

In most digital imaging applications, high-resolution images or videos are usually desired for later image processing and analysis. The image signal obtained from general imaging system occurs image degradation during the process of image acquirement caused by the optics, physical constraints and the atmosphere effects. Super-resolution reconstruction, one of the solution to address this problem, is image reconstruction technique that produces a high-resolution image from several low-resolution frames in video sequences. In this paper, we propose an improved super-resolution method using Projection onto Convex Sets (POCS) method based on Shift & Add (S&A). The image using conventional algorithms is sensitive to noise. To solve this problem, we propose a fusion algorithm of S&A and POCS. Also we solve the problem using BLPF (Butterworth Low-pass Filter) in frequency domain as optical blur. Our method is robust to noise and has sharpness enhancement ability. Experimental results show that the proposed super-resolution method has better resolution enhancement performance than other super-resolution methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.8
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• pp.453-458
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• 2014

In this paper, we propose a stereo image composition method based on Poisson image editing. If we synthesize images without considering their depth values, it may lead to unwanted consequences. When we segment an image into its background and foreground regions using Grabcut, we take into account their geometric positions to mix color tones; thus, the image is composited more naturally. After synthesizing images, we apply a blurring operation around object boundaries; then, the foreground object and background are composited more seamlessly. In addition, we can adjust the distance of the object by setting arbitrary depth values and generating right color and depth images automatically. Experimental results show that the proposed stereo image composition method provides naturally synthesized stereo images. Improved portions were subjectively confirmed as well.

• Journal of KIISE
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• v.44 no.12
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• pp.1319-1332
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• 2017

In recent years, the number of video cameras that are used to record and broadcast live sporting events has increased, and selecting the shots with the best view from multiple cameras has been an actively researched topic. Existing approaches have assumed that the background in video is fixed. However, this paper proposes a best view selection method for cases in which the background is not fixed. In our study, an athlete of interest was recorded in video during motion with multiple cameras. Then, each frame from all cameras is analyzed for establishing rules to select the best view. The frames were selected using our system and are compared with what human viewers have indicated as being the most desirable. For the evaluation, we asked each of 20 non-specialists to pick the best and worst views. The set of the best views that were selected the most coincided with 54.5% of the frame selection using our proposed method. On the other hand, the set of views most selected as worst through human selection coincided with 9% of best view shots selected using our method, demonstrating the efficacy of our proposed method.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.167-174
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• 2013

Various smart phone applications have been widely developed and an intelligent service to take a picture for the visually handicapped is needed. By using an auditory service and/or other's help, the blind capture an image with their cameras for keeping a record or exchanging the information with another people. However, they are difficult to take the wanted picture because they are unable to grasp the visual contents in the viewfinder. This paper presents a novel guiding system for the blind people to take a picture with the smart phone. The proposed method identifies a person in the picture and determines the presence of eye blinks and blurring. The system provides a guiding information such as camera directions, which are transferred into the blind user with auditory service.

• Journal of the Korea Computer Graphics Society
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• v.19 no.2
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• pp.9-18
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• 2013

We propose a novel method to extract keyframes from home videos based on the 5W and 1H information. Keyframe extraction is a kind of video summarization which selects only specific frames containing important information of a video. As a home video may have content with a variety of topics, we cannot make specific assumptions for information extraction. In addition, to summarize a home video we must analyze human behaviors, because people are important subjects in home videos. In this paper, we extract 5W and 1H information by analyzing human faces, human behaviors, and the global information of background. Experimental results demonstrate that our technique extract more similar keyframes to human selections than previous methods.