• Journal of Internet Computing and Services
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• v.4 no.3
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• pp.39-46
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• 2003

Generally, the existing watermarking techniques for copyright protection of a digital image are fragile in geometric distortion and all watermark extractions need the same time regardless of degree of distortion. In this paper, we propose the incremental watermarking technique that used a Complex Wavelet Transform(CWT) in order to solve these problems. The proposed incremental watermarking technique embeds a watermark in a phase component after a CWT with an original image, and a watermark is extracted from an watermarked image by stages. A watermark owner can insist on copyright of an image after comparing a correlation between the extracted watermark and the original watermark if it is larger than the threshold. Also, the incremental watermark extraction determines the extraction time of a watermark by the level of distortion. The proposed technique through performance evaluation displayed that it was robust in geometric distortion than the existing watermarking technique.

• Mass Spectrometry Letters
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• v.8 no.4
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• pp.109-113
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• 2017

Single analytical procedure including extraction, liquid chromatography, and mass spectrometric analysis was evaluated for the simultaneous measurement of lysophospholipids (LPLs). LPLs, particularly, lysophosphatidic acids (LPA) and sphingosine 1-phosphate (S1P) are lipid messengers ubiquitously found in various biological matrix. The molecular species mediate important physiological roles in association with many diseases (e.g. cancer, inflammation, and neurodegenerative disease), which emphasize the significance of the simple and reliable analytical method for biomarker discovery and molecular mechanistic understanding. Thus, we developed analytical method mainly focusing on, but not limited by those lipid species S1P and LPA using reverse phase liquid chromatography-tandem mass spectrometry (RPLC-ESI-MS-MS). Extraction method was modified based on Folch method with optimally minimal level of ionization additive (ammonium formate 10 mM and formic acid). Reverse-phase liquid-chromatography was applied for chromatographical separation in combination with negative ionization mode electrospray-coupled Orbitrap mass spectrometry. The method validation was performed on human blood plasma in a non-targeted lipid profiling manner with full-scan MS mode and data-dependent MS/MS. The proposed method presented good inter-assay precision for primary targets, S1P and LPA. Subsequent analysis of other types of LPLs identified a broad range of lysophosphatidylcholines (LPCs) and lysophosphatidyl-ethanolamines (LPEs).

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.949-959
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• 2020

In this paper, as a prior study related to the generation of topographic information using the CAS500-1/2 satellite, we propose a method of extraction DSM for each 1:5000 scaled map in North Korea using KOMPSAT-3A strip images. This technique is designed to set the processing area by receiving shape file, only to generate output for every 1:5000 scaled map. In addition, dense point clouds and the DSM were extracted by applying MDR, a robust stereo image matching technique. Considering that the strip images are input in the units of scenes, we attempted to extract a DSM by processing and merging multiple image pairs in one 1:5000 map area. As a result, it was possible to confirm the generation of an integrated DSM with minimal separation at the junction, and as a result of the accuracy analysis, it was confirmed that the accuracy was within 5m compared to GCP.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.121-130
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• 2005

In this paper, we propose a feature extraction method for iris recognition using wavelet transforms. The wavelet transform is fast and has a good localization characteristic. In particular, the low frequency band can be used as an effective feature vector. In iris recognition, the noise caused by eyelid the eyebrow, glint, etc may be included in iris. The iris pattern is distorted by noises by itself, and a feature extraction algorithm based on filter such as Wavelets, Gabor transform spreads noises into whole iris region. Namely, such noises degrade the performance of iris recognition systems a major problem. This kind of noise has adverse effect on performance. In order to solve these problems, we propose to divide the iris image into a number of sub-region and apply the wavelet transform to each sub-region. Experimental results show that the performance of proposed method is comparable to existing methods using Gabor transform and region division noticeably improves recognition performance. However, it is noted that the processing time of the wavelet transform is much faster than that of the existing methods.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.483-492
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• 2013

SURF is an algorithm which extracts feature points and generates their descriptors from input images, and it is being used for many applications such as object recognition, tracking, and constructing panorama pictures. Although SURF is known to be robust to changes of scale, rotation, and view points, it is hard to implement it in real time due to its complex and repetitive computations. Using 3.3 GHz Pentium, in our experiment, it takes 240ms to extract feature points and create descriptors in a VGA image containing about 1,000 feature points, which means that software implementation cannot meet the real time requirement, especially in embedded systems. In this paper, we present a hardware architecture that can compute the SURF algorithm very fast while consuming minimum hardware resources. Two key concepts of our architecture are parallelism (for repetitive computations) and efficient line memory usage (obtained by analyzing memory access patterns). As a result of FPGA synthesis using Xilinx Virtex5LX330, it occupies 101,348 LUTs and 1,367 KB on-chip memory, giving performance of 30 frames per second at 100 MHz clock.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.1
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• pp.15-21
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• 2007

We propose a 3D modeling method for surface inspection of non-constant velocity moving object. 1'lie laser lines reflect tile surface curvature. We can acquire 3D surface information by analyzing projected laser lines on object. In this paper, we use multi-line laser to improve the single stripe method and high speed of single frame. Binarization and edge extraction of frame image were proposed for robust laser each line extraction. A new labeling method was used for laser line labeling. We acquired some feature points for image matching from the frame data and juxtaposed the frames data to obtain a 3D shape image. We verified the superiority of proposed method by applying it to inspect container's damages.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.159-167
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• 2006

Lipreading has been suggested as one of the methods to improve the performance of speech recognition in noisy environment. However, existing methods are developed and implemented only in software. This paper suggests a hardware design for real-time lipreading. For real-time processing and feasible implementation, we decompose the lipreading system into three parts; image acquisition module, feature vector extraction module, and recognition module. Image acquisition module capture input image by using CMOS image sensor. The feature vector extraction module extracts feature vector from the input image by using parallel block matching algorithm. The parallel block matching algorithm is coded and simulated for FPGA circuit. Recognition module uses HMM based recognition algorithm. The recognition algorithm is coded and simulated by using DSP chip. The simulation results show that a real-time lipreading system can be implemented in hardware.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.3
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• pp.284-291
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• 2005

I propose a framework for detecting, recognizing and classifying facial features based on learned expression patterns. The framework recognizes facial expressions by using PCA and expression HMM(EHMM) which is Hidden Markov Model (HMM) approach to represent the spatial information and the temporal dynamics of the time varying visual expression patterns. Because the low level spatial feature extraction is fused with the temporal analysis, a unified spatio-temporal approach of HMM to common detection, tracking and classification problems is effective. The proposed recognition framework is accomplished by applying posterior probability between current visual observations and previous visual evidences. Consequently, the framework shows accurate and robust results of recognition on as well simple expressions as basic 6 facial feature patterns. The method allows us to perform a set of important tasks such as facial-expression recognition, HCI and key-frame extraction.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.1
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• pp.7-16
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• 2010

Face recognition of face images is an active subject in the area of computer pattern recognition, which has a wide range of potential. Automatic extraction of face image of the feature points is an important step during automatic face recognition. Whether correctly extract the facial feature has a direct influence to the face recognition. In this paper, a new method of facial feature extraction based on Discrete Wavelet Transform is proposed. Firstly, get the face image by using PC Camera. Secondly, decompose the face image using discrete wavelet transform. Finally, we use the horizontal direction, vertical direction projection method to extract the features of human face. According to the results of the features of human face, we can achieve face recognition. The result show that this method could extract feature points of human face quickly and accurately. This system not only can detect the face feature points with great accuracy, but also more robust than the tradition method to locate facial feature image.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.395-402
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• 2005

Object extraction is needed to track objects in color traffic image sequence. To extract objects, we use background differencing method based on MOG(Mixture of Gaussians). In extracted objects, shadows may be included. Due to shadows, we may not find exact location of objects and sometimes we find adjacent objects are glued together. Many methods have been proposed to remove shadows. Conventional methods usually assume that color and texture information are preserved under the shadow. Thus these methods do not work well if these assumptions do not hold. In this paper, we propose a new robust shadow removal method which works well in those situations. First we extract shadow pixel candidates by analysing color information and compute the ratio of shadow pixel candidates over the total number of Pixels. W the ratio is reasonable, we remove shadow candidate Pixels and if not, we use data in history array containing Previous removal records. We applied the method to real color traffic image sequences and obtained good results.