• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1369-1376
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• 2012

In this paper, we propose 2D industrial image registration method for the detection of defects. Proposed method performs preprocessing to smooth the original image with the preservation of the edge for the robust registration against general noise. Then, x-direction gradient magnitude image and corresponding binary image are generated. Density analysis around neighborhood regions per pixel are performed to generate feature image for preventing mis-registration due to moire-like patterns, which frequently happen in industrial images. Finally, 2D image registration based on phase correlation between feature images is performed to calculate translational parameters to align two images rapidly and optimally. Experimental results showed that the registration accuracy of proposed method for the real industrial images was 100% and our method was about twenty times faster than the previous method. Our fast and accurate method could be used for the real industrial applications.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.789-795
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• 2020

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.54-66
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• 1997

Efficient retrieval of image data is an important research issue in multimedia database. This paper proposes a new approach to a content-based image retrieval which allows queries to be composed of the local texture patterns and the slope components of contour points. The texture patterns extracted from the source image using the graylevel co-occurrence matrix and the slope components of contour points extracted from the binary image are converted into a internal feature representation of reduced dimensionality which preserves the perceptual similarity and those features can be used in creating efficient indexing structures for a content-based image retrieval. Experimental results of the image retrievalare presented to illustrate the usefulness of this approach that demonstrates the precision 82%, the recall 87% and the average rang 3.3 in content-based image data retrieval.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.827-834
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• 2003

In this paper, we implemented an optical information security system using computer generated hologram based on the principle of interference and BaTiO$_3$that is photorefractive material. First of all, we would generate binary phase hologram which can reconstruct the original image perfectly, and regard this hologram as the image to be encrypted. And then applying the interference rule to the hologram, encrypted and reference (fkey information) images are generated. In the decrypting process, we can get an interference intensity by interfering the reference image and the encrypted image in the Mach-Zehnder interferometer. and transforming interference intensity information into phase information using LCD(liquid crystal display) and finally recover original image by inverse Fourier transforming the phase information. In this process, the Intensity information generated by interference of two images is very sensitive to external vibrations. So, we get a stable interference using the characteristic of SPPCM(self pumped phase conjugate mirror) of BaTiO$_3$that is photorefractive material. The proposed method has an advantage of double image encryption by encrypting the hologram of the image instead of original image.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.5
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• pp.261-268
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• 2014

Face detection is essential to the full automation of face image processing application system such as face recognition, facial expression recognition, age estimation and gender identification. It is found that local image features which includes Haar-like, LBP, and MCT and the Adaboost algorithm for classifier combination are very effective for real time face detection. In this paper, we present a face detection method using local pixel direction code(PDC) feature and lookup table classifiers. The proposed PDC feature is much more effective to dectect the faces than the existing local binary structural features such as MCT and LBP. We found that our method's classification rate as well as detection rate under equal false positive rate are higher than conventional one.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.183-194
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• 2004

We propose a now watermarking scheme that can be used to embed multiple bits and also resilient to geometrical transforms such as scaling, rotation, and cropping, based on off - axis holographic watermark that allows multiple watermark recovery without original content(cover image). The holographic watermark is that Fourier transformed digital hologram is embedded into cover image in the spatial domain. The proposed method has not only increased robustness with a stronger embedding but also imprescriptibility of the watermark in the evaluation process. To compare with the convention기 scheme, the spread spectrum, we embedded and recovered maximum 1,024 bits that consist of binary number over PSNR(peak signal-to-noise ratio) 39dB. And also, we computed robustness with BER(bit error rate) corresponding the above attack

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.420-427
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• 2017

This paper proposes a new method using Wide Line Detector based statistical shape Feature (WLDF) to identify whether or not a tongue is cracked; a cracked tongue is one of the most frequently used visible features for diagnosis in traditional Chinese Medicine (TCM). We first detected a wide line in the tongue image, and then extracted WLDF, such as the maximum length of each detected region, and the ratio between maximum length and the area of the detected region. We trained a binary support vector machine (SVM) based on the WLDF to build a classifier for cracked tongues. We conducted an experiment based on our proposed scheme, using 196 samples of cracked tongues and 245 samples of non-cracked tongues. The results of the experiment indicate that the recognition accuracy of the proposed method is greater than 95%. In addition, we provide an analysis of the results of this experiment with different parameters, demonstrating the feasibility and effectiveness of the proposed scheme.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.402-410
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• 2017

For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2009.05a
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• pp.32-36
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• 2009

본 논문에서는 DCT영역에서 반복적 이진위상컴퓨터형성홀로그램을 이용한 디지털 영상 워터마킹 기술을 제안하였다. 워터마크로 주로 사용되는 랜덤 시퀸스 또는 로고와 같은 은닉영상 대신 은닉영상을 손실없이 재생할 수 있는 이진위상컴퓨터형성홀로그램을 생성하고, 이를 반복적으로 표현해서 워터마크로 사용한다. 그리고 이 워터마크를 호스트영상의 DCT 계수에 적절한 규칙을 통해 가중치를 부여하여 삽입한 후, IDCT한다. 워터마크의 추출은 워터마킹된 영상과 호스트영상을 DCT하고, 삽입시 적용한 규칙을 통해서 수행한다. 그리고 추출된 워터마크의 역푸리에 변환과 호스트영상에 삽입하기전의 워터마크를 역푸리에 변환하여 재생한 은닉영상과의 상관을 취함으로써 워터마크의 존재여부를 검증한다. 제안한 방법은 워터마크 삽입/추출시 반복되는 홀로그램정보를 활용하고, 이진 값으로 구성되어 있으므로 기존의 어떠한 워터마킹 기술보다 외부 공격에 견실한 특징을 가지고 있으며, 컴퓨터 시뮬레이션을 통해 그 성능을 확인하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.908-911
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• 2004

In this paper, we describe a robust image processing algorithm to recognize the road lane in real-time. For the real-time processing, a detection area is decided by a lane segment of a previous frame and edges are detected on the basis of the lane width. For the robust driving, the global threshold with the Otsu algorithm is used to get a binary image in a frame. Therefore, reliable edges are obtained from the algorithms suggested in this paper in a short time. Lastly, the lane segment is found by hough transform. We made a RC(Radio Control) car equipped with a vision system and verified these algorithms using the RC Car.