• Journal of the Korean Society of Radiology
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• v.3 no.1
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• pp.29-39
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• 2009

In this paper has proposed to the recognition of the disease on medical images using neural network. The neural network is constructed as three-layers of the input-layer, the hidden-layer and the output-layer. The training method applied for the recognition of disease region is adaptive error back-propagation. The low-frequency region analyzed by DWT are expressed by matrix. The coefficient-values of the characteristic polynomial applied are n+1. The normalized maximum value +1 and minimum value -1 in the range of tangent-sigmoid transfer function are applied to be use as the input vector of the neural network. To prove the validity of the proposed methods used in the experiment with a simulation experiment, the input medical image recognition rate the evaluation of areas of disease. As a result of the experiment, the characteristic polynomial coefficient of low-frequency area matrix, conversed to 4 level DWT, was proved to be optimum to be applied to the feature parameter. As for the number of training, it was marked fewest in 0.01 of learning coefficient and 0.95 of momentum, when the adaptive error back-propagation was learned by inputting standardized feature parameter into organized neural network. As to the training result when the learning coefficient was 0.01, and momentum was 0.95, it was 100% recognized in fifty-five times of the stomach image, fifty-five times of the chest image, forty-six times of the CT image, fifty-five times of ultrasonogram, and one hundred fifty-seven times of angiogram.

• Journal of KIISE:Software and Applications
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• v.30 no.10
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• pp.938-943
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• 2003

In this paper, we Propose a method using the Tikhonov-Miller process to improve the robustness of watermarking under various attacks. A visually recognizable pattern watermark is embedded in the LH2, HL2 and HH2 subband of wavelet transformed domain using threshold and besides watermark is embeded by utilizing HVS(Human Visual System) feature. The pattern watermark was interlaced after random Permutation for a security and an extraction rate. To demonstrate the improvement of robustness and similarity of the proposed method, we applied some basic algorithm of image processing such as scaling, filtering, cropping, histogram equalizing and lossy compression(JPEG, gif). As a result of experiment, the proposed method was able to embed robust watermark invisibility and extract with an excellent normalized correlation of watermark under various attacks.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.297-302
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• 2012

In this paper, we present a object detection and tracking method based on image context analysis. It is robust from the image variations such as complicated background, dynamic movement of the object. Image context analysis is carried out using the hybrid network of k-means and RBF. The proposed object detection employs context-driven adaptive Bayesian framework to relive the effect due to uneven object images. The proposed method used feature vector generator using 2D Haar wavelet transform and the Bayesian discriminant method in order to enhance the speed of learning. The system took less time to learn, and learning in a wide variety of data showed consistent results. After we developed the proposed method was applied to real-world environment. As a result, in the case of the object to detect pass outside expected area or other changes in the uncertain reaction showed that stable. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.185-189
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• 2009

무선 센서 네트워크는 화산 감시, 전장 감시, 동물 서식지 감시, 건축물의 감시, 농장 관리, 의료분야등 다양한 분야에서 연구되고 있다. 국내에서도 국가 정책 사업으로 교량 및 건축물의 균열 감시, 표적의 침입 탐지 및 식별을 위한 무선 센서 네트워크 연구가 활발히 진행 중이다. 특히, 무선 센서 네트워크의 다양한 분야의 연구 중에서 철조망을 이용한 표적의 침입 탐지 및 식별에 관한 연구는 산업 시설, 보안지역, 교도소, 군사지역, 공항 등 다양한 분야에서 사용된다. 현재 철조망 감시는 대부분 유선 센서 노드를 통한 유선 센서 네트워크 환경에서 이루어지고 있다. 기존의 유선 센서 네트워크는 높은 데이터 전송률을 통해 수신되는 높은 정보의 신호를 이용하여 고속 푸리에 변환에 의한 신호의 주파수 분석 기법을 사용해 왔다. 하지만, 유선 센서 네트워크의 높은 데이터 전송률과 비교하여 무선 센서 네트워크의 센서 노드는 유선 센서 네트워크에 비해 매우 낮은 데이터 전송률을 가진다. 따라서 무선 센서 네트워크에서 수신되는 신호의 정보가 매우 낮고, 유선 센서 네트워크에서 사용된 고속 푸리에 변환에 의한 신호의 주파수 분석에 따른 주파수별 특징 추출을 할 수 없다. 따라서 본 논문에서는 철조망 감시를 위한 높은 데이터 전송률을 보장하는 유선 센서 네트워크에 비해 제한된 통신자원과 센서 노드의 낮은 데이터 전송률로 인해 수신되는 한정적인 신호의 정보를 이용한 무선 센서 네트 워크에서 철조망의 표적 침입 탐지 및 식별을 위한 특징 추출 알고리즘을 제안한다.

• Fashion & Textile Research Journal
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• v.15 no.6
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• pp.1010-1017
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• 2013

Current global textiles and fashion industries have gradually shifted focus to high value-added, high sensibility, and multi-functional products based on new human-friendliness and sustainable growth technologies. Textile design CAD systems have been developed in conjunction with computer hardware and software sector advances. This study compares the patterns or images of actual woven fabrics and virtual fabrics prepared with a textile design CAD system. In this study, several weave structures (such as fancy yarn weave and patterns) were prepared with a shuttle loom. The woven textile images were taken using a CCD camera. The same weave structure data and yarn data were fed into a textile design CAD system in order to simulate fabric images as similarly as possible. Similarity Index analysis methods allowed for an analysis of the index between the actual fabric specimen and the simulated image of the corresponding fabric. The results showed that repeated small pattern weaves provide superior similarity index values than those of a fancy yarn weave that indicate some irregularities due to fancy yarn attributes. A Complex Wavelet Structural Similarity(CW-SSIM) index resulted in a better index than other methods such as Multi-Scale(MS) SSIM, and Feature Similarity(FS) SSIM, across fabric specimen images. A correlation analysis of the similarity index based on an image analysis and a similarity evaluation by panel members was also implemented.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.163-170
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• 2017

Internet of things (IoT) is a new paradigm for collecting, processing and analyzing various contents in order to detect anomalies and to monitor particular patterns in a specific environment. The collected data can be used to discover new patterns and to offer new insights. IoT-enabled data mashup is a new technology to combine various types of information from multiple sources into a single web service. Mashup services create a new horizon for different applications. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations may utilize IoT - enabled data mashup service to merge different types of datasets from different IoT sensor networks in order to leverage their data analytics performance and the accuracy of the predictions. This paper presents an IoT - enabled data mashup service, where the multimedia data is collected from the various IoT platforms, then fed into an environmental cognition service which executes different image processing techniques such as noise removal, segmentation, and feature extraction, in order to detect interesting patterns in hazardous areas. The noise present in the captured images is eliminated with the help of a noise removal and background subtraction processes. Markov based approach was utilized to segment the possible regions of interest. The viable features within each region were extracted using a multiresolution wavelet transform, then fed into a discriminative classifier to extract various patterns. Experimental results have shown an accurate detection performance and adequate processing time for the proposed approach. We also provide a data mashup scenario for an IoT-enabled environmental hazard detection service and experimentation results.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.71-80
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• 2012

This paper proposes vehicle detection and tracking algorithm using a CCD camera. The proposed algorithm uses Haar-like wavelet edge detector to detect features of vehicle and estimates vehicle's location using calibration information of an image. After that, extract accumulated vehicle information in continuous k images to improve reliability. Finally, obtained vehicle region becomes a template image to find same object in the next continuous image using SURF(Speeded Up Robust Features). The template image is updated in the every frame. In order to reduce SURF processing time, ROI(Region of Interesting) region is limited on expended area of detected vehicle location in the previous frame image. This algorithm repeats detection and tracking progress until no corresponding points are found. The experimental result shows efficiency of proposed algorithm using images obtained on the road.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.160-167
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• 2014

This paper presents a spliced color image detection method using Benford' Law and color difference. For a suspicious image, after color conversion, the discrete wavelet transform and the discrete cosine transform are performed. We extract the difference between the ideal Benford distribution and the empirical Benford distribution of the suspicious image as features. The difference between Benford distributions for each color component were also used as features. Our method shows superior splicing detection performance using only 13 features. After training the extracted feature vector using SVM classifier, we determine whether the presence of the image splicing forgery. Experimental results show that the proposed method outperforms the existing methods with smaller number of features in terms of splicing detection accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.431-437
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• 2004

This paper propose a facial recognition method based on an ubiquitous computing that is one of next generation intelligence technology fields. The facial images are acquired by a mobile device so-called cellular phone camera. We consider a mobile security using facial feature extraction and recognition process. Facial recognition is performed by the PCA and fuzzy LDA algorithm. Applying the discrete wavelet based on multi-resolution analysis, we compress the image data for mobile system environment. Euclidean metric is applied to measure the similarity among acquired features and then obtain the recognition rate. Finally we use the mobile equipment to show the efficiency of method. From various experiments, we find that our proposed method shows better results, even though the resolution of mobile camera is lower than conventional camera.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.154-161
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• 2008

In this paper, a novel method is proposed using color and pattern information for recognizing some emotions included in a fertile. Here we use 10 Kobayashi emotion to represent emotions. - { romantic, clear, natural, casual, elegant chic, dynamic, classic, dandy, modem } The proposed system is composed of feature extraction and classification. To transform the subjective emotions as physical visual features, we extract representative colors and Patterns from textile. Here, the representative color prototypes are extracted by color quantization method, and patterns exacted by wavelet transform followed by statistical analysis. These exacted features are given as input to the neural network (NN)-based classifiers, which decides whether or not a textile had the corresponding emotion. When assessing the effectiveness of the proposed system with 389 textiles collected from various application domains such as interior, fashion, and artificial ones. The results showed that the proposed method has the precision of 100% and the recall of 99%, thereby it can be used in various textile industries.