• Journal of Multimedia Information System
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• v.6 no.2
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• pp.81-86
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• 2019

Deep learning is emerging as one of the best tool in processing data related to medical imaging. In our research work, we have proposed a deep learning based framework CNN (Convolutional Neural Network) for the classification of dysplastic tissue images. The CNN has classified the given images into 4 different classes namely normal tissue, mild dysplastic tissue, moderate dysplastic tissue and severe dysplastic tissue. The dataset under taken for the study consists of 672 tissue images of epithelial squamous layer of oral cavity captured out of the biopsy samples of 52 patients. After applying the data pre-processing and augmentation on the given dataset, 2688 images were created. Further, these 2688 images were classified into 4 categories with the help of expert Oral Pathologist. The classified data was supplied to the convolutional neural network for training and testing of the proposed framework. It has been observed that training data shows 91.65% accuracy whereas the testing data achieves 89.3% accuracy. The results produced by our proposed framework are also tested and validated by comparing the manual results produced by the medical experts working in this area.

• Transactions on Electrical and Electronic Materials
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• v.18 no.6
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• pp.320-322
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• 2017

In this paper, a high-performance, low-area gradient-magnitude calculator architecture is proposed, based on approximate image processing. To reduce the computational complexity of the gradient-magnitude calculation, vector properties, the symmetry axis, and common terms were applied in a hardware-resource-shared architec-ture. The proposed gradient-magnitude calculator was implemented using an Altera Cyclone IV FPGA (EP4CE115F29) and the Quartus II v.16 device software. It satisfied the output-data quality while reducing the logic elements by 23% and the embedded multipliers by 76%, compared with previous work.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.954-956
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• 2009

디지털 카메라는 단순히 사진을 찍는 장치가 아니며 사진에 연관된 다양한 메타 데이터를 제공하기 위한 다양한 시스템이 사진을 찍는 CCD와 유기적으로 연계되어 있다. 사진에 연관된 메타 데이터들은 디지털 카메라로 찍은 사진을 분류하는 기능을 지원한다. 하지만 사진의 메타 데이터들은 사진에 대한 검색을 가능하게 하지만, 대부분 사람의 수작업으로 이루어지기 때문에 새로운 메타 데이터의 입력이 어렵다는 문제점이 있다. 사진의 메타 데이터를 쉽게 추가하기 위해 본 논문에서는 GPS 시스템과 Wi-Fi, 데이터베이스를 이용해서 사진의 메타 데이터를 Exif(Exchangeable image file format)에 추가하고자 한다. GPS 시스템은 사진을 찍는 사람들이 어디에 있는지를 제시하고, Wi-Fi와 데이터 베이스를 이용해서 사용자에게 사용자가 사진을 찍은 위치와 관련된 메타 데이터를 제공한다. 이를 기반으로 본 논문에서는 이러한 PreTag라는 사진 메타 데이터 추가 아키텍처를 제시한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.472-475
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• 2020

실생활의 사례를 바탕으로 생성된 여러 분야의 데이터셋을 기계학습 (Machine Learning) 문제에 적용하고 있다. 정보보안 분야에서도 사이버 공간에서의 공격 트래픽 데이터를 기계학습으로 분석하는 많은 연구들이 진행 되어 왔다. 본 논문에서는 공격 데이터를 유형별로 정확히 분류할 때, 실생활 데이터에서 흔하게 발생하는 데이터 불균형 문제로 인한 분류 성능 저하에 대한 해결방안을 연구했다. 희소 클래스 관점에서 데이터를 재구성하고 기계학습에 악영향을 끼치는 특징들을 제거하고 DNN(Deep Neural Network) 모델을 사용해 분류 성능을 평가했다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.634-636
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• 2023

Surveillance systems play a pivotal role in ensuring the safety and security of various environments, including public spaces, critical infrastructure, and private properties. However, detecting abnormal human behavior in lowlight conditions is a critical yet challenging task due to the inherent limitations of visual data acquisition in such scenarios. This paper introduces a spatiotemporal framework designed to address the unique challenges posed by low-light environments, enhancing the accuracy and efficiency of human abnormality detection in surveillance camera systems. We proposed the pre-processing using lightweight exposure correction, patched frames pose estimation, and optical flow to extract the human behavior flow through t-seconds of frames. After that, we train the estimated-action-flow into autoencoder for abnormal behavior classification to get normal loss as metrics decision for normal/abnormal behavior.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.540-553
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• 2023

The hollow letter CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is an optimized version of solid CAPTCHA, specifically designed to weaken characteristic information and increase the difficulty of machine recognition. Although convolutional neural networks can solve CAPTCHA in a single step, a good attack result heavily relies on sufficient training data. To address this challenge, we propose a seed filling algorithm that converts hollow characters to solid ones after contour line restoration and applies three rounds of detection to remove noise background by eliminating noise blocks. Subsequently, we utilize a support vector machine to construct a feature vector for recognition. Security analysis and experiments show the effectiveness of this algorithm during the pre-processing stage, providing favorable conditions for subsequent recognition tasks and enhancing the accuracy of recognition for hollow CAPTCHA.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.40
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• pp.2.1-2.7
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• 2018

With the development of computer-aided design/computer-aided manufacturing (CAD/CAM) technology, it has been possible to reconstruct the cranio-maxillofacial defect with more accurate preoperative planning, precise patient-specific implants (PSIs), and shorter operation times. The manufacturing processes include subtractive manufacturing and additive manufacturing and should be selected in consideration of the material type, available technology, post-processing, accuracy, lead time, properties, and surface quality. Materials such as titanium, polyethylene, polyetheretherketone (PEEK), hydroxyapatite (HA), poly-DL-lactic acid (PDLLA), polylactide-co-glycolide acid (PLGA), and calcium phosphate are used. Design methods for the reconstruction of cranio-maxillofacial defects include the use of a pre-operative model printed with pre-operative data, printing a cutting guide or template after virtual surgery, a model after virtual surgery printed with reconstructed data using a mirror image, and manufacturing PSIs by directly obtaining PSI data after reconstruction using a mirror image. By selecting the appropriate design method, manufacturing process, and implant material according to the case, it is possible to obtain a more accurate surgical procedure, reduced operation time, the prevention of various complications that can occur using the traditional method, and predictive results compared to the traditional method.

• Journal of the Korea Computer Industry Society
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• v.6 no.5
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• pp.767-774
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• 2005

In this paper, we propose an efficient coding method of digital hologram using standard compression tools for video images. At first, we convert fringe patterns into video data using a principle of CGH(Computer Generated Hologram), and then encode it. In this research, we propose a compression algorithm is made up of various method such as pre-processing for transform, local segmentation with global information of object image, frequency transform for coding, scanning to make fringe to video stream, classification of coefficients, and hybrid video coding. The proposed algorithm illustrated that it have better properties for reconstruction and compression rate than the previous methods.

• Journal of the Korea Computer Graphics Society
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• v.8 no.1
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• pp.1-11
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• 2002

In order to widely use volume rendering technology in practical fields, a user should be able to control the classification parameter interactively and extract a meaningful information easily from the 3D data as fast as it can be. Previous work on an accelerating volume rendering reconstructs an isotropic volume from an anisotropic one and classifies in pre-processing time and then renders the classified volume rapidly in run time. But, this traditional step may result in long pre-processing time and no real-time feedback. In this paper, we present an efficient classification and rendering method that allows a user to set the opacity transfer function interactively at rendering time on a personal computer without special-purpose hardware.

• Journal of the Korea Computer Industry Society
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• v.5 no.5
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• pp.737-746
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• 2004

Two-channel multiwavelet system is investigated for image compression application in this paper. Generally, multiwavelets are known for their superb capability of compressing non-stationary signals like voice. However, multivavelet system have a critical problem in processing and compressing image data due to mesh-grid visual artifacts. In our two-channel multiwavelet system we have investigated incorporation of pre and post filtering to the multiwavelet transform and compression system for alleviating those ingerent visual artifacts due to multiwavelet effect. In addition, to quantify the image data compression performance of proposed multiwavelet system, computer simulations have been performed using various image data. For bit allocation and quantization, the Lagrange multiplier technique considering data rate vs. distortion rate along with a nonlinear companding method are applied equallly to all systems considered, here. The simulation results have yielded 1 ~ 2 dB compression enhancement over the scalar savelet systems. If the more advanced compression methods like SPIHT and run-length channel coding were adopted for the proposed multiwavelet system, a much higher compression gain could be obtained.