• Journal of Information Technology Applications and Management
• /
• v.10 no.3
• /
• pp.45-54
• /
• 2003

Security system with web camera remarkably has been developed at an Internet era. Using transmitted images from remote camera, the system can recognize current situation and take a proper action through web. Existing motion detection methods use simply difference image, background image techniques or block matching algorithm which establish initial block by set search area and find similar block. But these methods are difficult to detect exact motion because of useless noise. In this paper, the proposed method is updating changed background image as much as $N{\times}M$pixel mask as time goes on after get a difference between imput image and first background image. And checking image pixel can efficiently detect motion by computing fixed distance pixel instead of operate all pixel.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.1
• /
• pp.111-115
• /
• 2020

In this paper, I proposed an effective technique for accurately predicting pixel values using edge components. Adjacent pixel values are similar to each other. That is, generally, similarity exists between adjacent pixels in an image. In the proposed algorithm, edge components are detected using the surrounding pixels in the first step, and pixel values are estimated using the edge components in the second step. Therefore, the prediction accuracy of the pixel value is improved and the prediction error is reduced. Pixel value prediction is a necessary technique for various applications such as image magnification and confidential data concealment. Experimental results show that the proposed method has higher prediction accuracy and fewer prediction error. Therefore, the proposed technique can be effectively used for applications such as image magnification and confidential data concealment.

• Korean Journal of Human Ecology
• /
• v.24 no.3
• /
• pp.407-421
• /
• 2015

This study examined the pixel's expression trend and nature featured in contemporary fashion, which works as a basic unit symbolizing the digital image, paying attention to its formativeness. The work through this process aimed at suggesting an opportunity for recognition about pixel image utilized as a formative component beyond its simple meaning of unit and providing the fundamental materials for usage in creative fashion design reflecting the digital emotion in the future, In research method, literature review was followed on pixel and the empirical study about its image was also performed that was found in the modern fashion. As a result, the trend in pixel has these characteristics. Its first nature lies in its plane expression. It was printed as mosaic or graphic grid image or expressed through patchwork technique. Also, rather than a certain form or figure, its unique image was emphasized according to the applied color, size, and position. Second, a stepwise pattern in pixel was applied to external format for part of clothing, eye glass and necktie, indicating some interest and wit. Third, in application to plane and external shape, the graphically modernized effect was realized, not to mention the illusive image with cubic expression. As shown, the characteristics in contemporary fashion via pixel expression were given in fantastic image, optical humor, and reflection of digital value.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.727-731
• /
• 2003

ASTER image has some advantages for classification such as 15 spectral bands and 15m ${\sim}$ 90m spatial resolution. However, in the classification using general remote sensing image, shadow areas are often classified into water area. It is very difficult to divide shadow and water. Because reflectance characteristics of water is similar to characteristics of shadow. Many land cover items are consisted in one pixel which is 15m spatial resolution. Nowadays, very high resolution satellite image (IKONOS, Quick Bird) and Digital Surface Model (DSM) by air borne laser scanner can also be used. In this study, mixed pixel analysis of ASTER image has carried out using IKONOS image and DSM. For mixed pixel analysis, high accurated geometric correction was required. Image matching method was applied for generating GCP datasets. IKONOS image was rectified by affine transform. After that, one pixel in ASTER image should be compared with corresponded 15×15 pixel in IKONOS image. Then, training dataset were generated for mixed pixel analysis using visual interpretation of IKONOS image. Finally, classification will be carried out based on Linear Mixture Model. Shadow extraction might be succeeded by the classification. The extracted shadow area was validated using shadow image which generated from 1m${\sim}$2m spatial resolution DSM. The result showed 17.2% error was occurred in mixed pixel. It might be limitation of ASTER image for shadow extraction because of 8bit quantization data.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.25 no.11
• /
• pp.857-861
• /
• 2012

FPN (fixed-pattern-noise) mainly comes from the device or pattern mismatches in pixel and color filter, pixel photodiode leakage in CMOS image sensor. In this paper, optical stack module related pixel FPN was investigated and the classification of pixel FPN contribution with the individual optical module process was presented. The methodology and procedure would be helpful in reducing the greater pixel FPN and distinguishing the complex FPN sources with respect to various noise factors.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.4C
• /
• pp.380-387
• /
• 2009

In this paper, optical image encryption using integral imaging and pixel-scrambling technologies is proposed. In the encryption process, we use pixel scrambling to change the order of subsections into which the cover image is divided, and the utilize the integral imaging scheme to obtain the elemental image from the scrambled image. In order to achieve higher security, we reuse pixel scrambling to the elemental image. In the decryption process, we employ optical integral imaging reconstruction technique and inverse pixel scrambling methode. Computer simulation results prove the feasibility of the proposed method and robustness against data loss and noise.

• Journal of Advanced Marine Engineering and Technology
• /
• v.28 no.4
• /
• pp.587-592
• /
• 2004

Pixel classification is one of basic issues of image processing. The general characteristics of the pixels belonging to various classes are discussed and the radical principles of pixel classification are given. At the same time, a pixel classification scheme based on image information scales is proposed. The proposed method is overcome that computation amount become greater and contents easily get turned. And image directional scales has excellent anti-noise performance. In the result of experiment. good efficiency is showed compare with other methods.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.8 no.2
• /
• pp.25-31
• /
• 2009

The image processing methods are widely used in many industrial fields to detect defections in inspection devices. In this study an image processing method was conducted for the detection of abnormal pixels in a OLED(Organic Light Emitting Diode) type panel which is used for small size displays. The display quality of an OLED device is dependent on the pixel formation quality. So, among the so many pixels, to find out the faulty pixels is very important task in manufacturing processing or inspection division. We used a line scanning type BW(Black & White) camera which has very high resolution characteristics to acquire an image of display pixel patterns. And the various faulty cases in pixel abnormal patterns are considered to detect abnormal pixels. From the results of the research, the normal BW pixel image could be restored to its original color pixel.

• Journal of the Korea Society for Simulation
• /
• v.10 no.3
• /
• pp.59-70
• /
• 2001

Typical low-level image processing tasks require thousands of operations per pixel for each input image. Traditional general-purpose computers are not capable of performing such tasks in real time. Yet important features of traditional computers are not exploited by low-level image processing tasks. Since storage requirements are limited to a small number of low-precision integer values per pixel, large hierarchical memory systems are not necessary. The mismatch between the demands of low-level image processing tasks and the characteristics of conventional computers motivates investigation of alternative architectures. The structure of the tasks suggests employing an array of processing elements, one per pixel, sharing instructions issued by a single controller. In this paper we implemented various image processing filtering using the format converter. Also, we realized from conventional gray image process to color image process. This design method is based on realized the large processor-per-pixel array by integrated circuit technology This format converter design has control path implementation efficiently, and can be utilize the high technology without complicated controller hardware.

• Journal of Korea Multimedia Society
• /
• v.21 no.12
• /
• pp.1425-1430
• /
• 2018

In this paper, we propose a CNN based deep learning algorithm for semantic segmentation of images. In order to improve the accuracy of semantic segmentation, we combined pixel level object classification and image level object classification. The image level object classification is used to accurately detect the characteristics of an image, and the pixel level object classification is used to indicate which object area is included in each pixel. The proposed network structure consists of three parts in total. A part for extracting the features of the image, a part for outputting the final result in the resolution size of the original image, and a part for performing the image level object classification. Loss functions exist for image level and pixel level classification, respectively. Image-level object classification uses KL-Divergence and pixel level object classification uses cross-entropy. In addition, it combines the layer of the resolution of the network extracting the features and the network of the resolution to secure the position information of the lost feature and the information of the boundary of the object due to the pooling operation.