• Title/Summary/Keyword: 특징 정규화

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The Implementation of Face Recognition System for Intelligent Surveillance (지능형 영상 보안을 위한 얼굴 인식 시스템 구현)

  • Kim, Su-Hyun;Jeong, Chang-sung
    • Proceedings of the Korea Information Processing Society Conference
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    • 2013.11a
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    • pp.1401-1403
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    • 2013
  • 사건 발생 후의 대응이 아닌 영상 분석을 통해 실시간으로 위협 상황에 대응할 수 있는 지능형 영상 보안 기술이 매우 중요한 이슈가 되고 있다. 본 논문에서는 지능형 영상 보안에 사용할 수 있는 실시간 얼굴 인식 및 추적 기법을 제안한다. 사람의 정면 얼굴 영상을 ASM(Active Shape Model) 알고리즘을 이용하여 정규화 시키고 Gabor Wavelet Filter를 이용하여 얼굴 고유 특징 벡터를 추출하여 인식에 사용하였다. 인식이 완료된 얼굴은 Camshift와 Kalman Filter를 이용하여 카메라 감시 영역에서 벗어날 때까지 강건한 추적을 통하여 관리자가 실시간으로 확인 및 대응할 수 있게 하였다.

A spam mail blocking method using URL frequency analysis (URL 빈도분석을 이용한 스팸메일 차단 방법)

  • Baek Ki-young;Lee Chul-soo;Ryou Jae-cheol
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.14 no.6
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    • pp.135-148
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    • 2004
  • Recently, it is difficult to block the spam mail that changes variously with past spam distinction method by words. To solve such problem, This paper propose the method of generating spam distinction rule using URL frequency analysis. It is consist of collecting spam, drawing URL that get into characteristic from collected spam mail. URL noonalizing, generating spam distinction rule by time frequency, and blocking mail. It can effectively block various types of spam mail and various forms of spam mail that change.

Utilizing Mixup Regularization to improve Adversarial Domain Adaptation (Mixup 정규화를 활용하여 적대적 도메인 적응 향상)

  • Kalina Bayarchimeg;Youngbok Cho
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2023.01a
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    • pp.17-18
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    • 2023
  • 비지도형 도메인 적응(UDA)에 대한 최근 연구는 도메인 적응에 대한 설명 및 전이 가능한 특징을 풀어 내기 위해 적대적 학습에 의존한다. 그러나 기존 방법에는 대상 도메인의 클래스 인식(class-aware) 정보를 고려하지 않고는 잠재 공간의 구별 가능성을 완전히 보장할 수 없다는 것과 소스 및 대상 도메인의 샘플만으로는 잠재 공간에서 도메인 불변(domain- invariant) 특성을 추출하기에 부족하다는 두 가지 문제가 있다고 알려져 있다. 본 논문에서는 기존 알려진 UDA의 도메인 적응시 발생되는 문제를 해결하기 위해 Adversarial Discriminative Domain Adaptation(ADDA)에서 mixup을 활용해 신경망의 로버스트네스를 향상시키는 것을 확인하였다.

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Robot Control using Vision based Hand Gesture Recognition (비전기반 손 제스처 인식을 통한 로봇 컨트롤)

  • Kim, Dae-Soo;Kang, Hang-Bong
    • Proceedings of the Korea Information Processing Society Conference
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    • 2007.11a
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    • pp.197-200
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    • 2007
  • 본 논문에서는 로봇 컨트롤 시스템을 위해 입력 받은 영상부터 몇 가지의 손 제스처를 인식하는 비전기반 손 제스처 인식방법을 제안한다. 로봇으로부터 입력 받은 이미지는 로봇의 위치, 주변환경, 조명 등 여러 요인에 따라 다양하게 존재한다. 본 논문은 다양한 환경에서 입력되는 영상으로부터 시스템이 로봇 컨트롤을 위해 미리 지정한 몇 가지 제스처를 인식하도록 한다. 먼저 이미지 조명 변화에 강한 손 제스처 인식을 위하여 레티넥스 이미지 정규화를 적용한 후, YCrCb 공간 상에서 입력된 영상에서 손 영역을 검출 후 위치를 추정한다. 인식된 손 영역에서 특징벡터를 추출함으로서 입력 영상내의 존재할 수 있는 손의 크기나 손의 회전각도 등에 상관없이 필요로 하는 제스처를 인식하도록 한다. 제안된 제스처 인식 결과는 로봇컨트롤을 위한 기존의 제스처인식과 비교하여 성능을 측정하였다.

A Dispersion Mean Algorithm based on Similarity Measure for Evaluation of Port Competitiveness (항만 경쟁력 평가를 위한 유사도 기반의 이산형 평균 알고리즘)

  • Chw, Bong-Sung;Lee, Cheol-Yeong
    • Journal of Navigation and Port Research
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    • v.28 no.3
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    • pp.185-191
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    • 2004
  • The mean and Clustering are important methods of data mining, which is now widely applied to various multi-attributes problem However, feature weighting and feature selection are important in those methods bemuse features may differ in importance and such differences need to be considered in data mining with various multiful-attributes problem. In addition, in the event of arithmetic mean, which is inadequate to figure out the most fitted result for structure of evaluation with attributes that there are weighted and ranked. Moreover, it is hard to catch hold of a specific character for assume the form of user's group. In this paper. we propose a dispersion mean algorithm for evaluation of similarity measure based on the geometrical figure. In addition, it is applied to mean classified by user's group. One of the key issues to be considered in evaluation of the similarity measure is how to achieve objectiveness that it is not change over an item ranking in evaluation process.

A Study on Face Recognition using Neural Networks and Characteristics Extraction based on Differential Image and DCT (차영상과 DCT 기반 특징 추출과 신경망을 이용한 얼굴 인식에 관한 연구)

  • 임춘환;고낙용;박종안
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.24 no.8B
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    • pp.1549-1557
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    • 1999
  • In this paper, we propose a face recognition algorithm based on the differential image method-DCT This algorithm uses neural networks which is flexible for noise. Using the same condition (same luminous intensity and same distance from the fixed CCD camera to human face), we have captured two images. One doesn't contain human face. The other contains human face. Differential image method is used to separate the second image into face region and background region. After that, we have extracted square area from the face region, which is based on the edge distribution. This square region is used as the characteristics region of human face. It contains the eye bows, the eyes, the nose, and the mouth. After executing DCT for this square region, we have extracted the feature vectors. The feature vectors were normalized and used as the input vectors of the neural network. Simulation results show 100% recognition rate when face images were learned and 92.25% recognition rate when face images weren't learned for 30 persons.

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Bearing Faults Identification of an Induction Motor using Acoustic Emission Signals and Histogram Modeling (음향 방출 신호와 히스토그램 모델링을 이용한 유도전동기의 베어링 결함 검출)

  • Jang, Won-Chul;Seo, Jun-Sang;Kim, Jong-Myon
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.11
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    • pp.17-24
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    • 2014
  • This paper proposes a fault detection method for low-speed rolling element bearings of an induction motor using acoustic emission signals and histogram modeling. The proposed method performs envelop modeling of the histogram of normalized fault signals. It then extracts and selects significant features of each fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally, using the extracted features as inputs, the support vector regression (SVR) classifies bearing's inner, outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed method with varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and the number of features from 2 to 150. Experimental results show that the proposed fault identification method using 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performance and outperforms conventional fault diagnosis methods as well.

Hardware Implementation of Fog Feature Based on Coefficient of Variation Using Normalization (정규화를 이용한 변동계수 기반 안개 특징의 하드웨어 구현)

  • Kang, Ui-Jin;Kang, Bong-Soon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.6
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    • pp.819-824
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    • 2021
  • As technologies related to image processing such as autonomous driving and CCTV develop, fog removal algorithms using a single image are being studied to improve the problem of image distortion. As a method of predicting fog density, there is a method of estimating the depth of an image by generating a depth map, and various fog features may be used as training data of the depth map. In addition, it is essential to implement a hardware capable of processing high-definition images in real time in order to apply the fog removal algorithm to actual technologies. In this paper, we implement NLCV (Normalize Local Coefficient of Variation), a feature of fog based on coefficient of variation, in hardware. The proposed hardware is an FPGA implementation of Xilinx's xczu7ev-2ffvc1156 as a target device. As a result of synthesis through the Vivado program, it has a maximum operating frequency of 479.616MHz and shows that real-time processing is possible in 4K UHD environment.

The Coding Method with Multi-mode Technique (고효율의 멀티모드 데이터 변조방법)

  • 김진한;심재성;정규해
    • Proceedings of the IEEK Conference
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    • 2003.11a
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    • pp.457-460
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    • 2003
  • DC억압능력이 없거나 부족한 코드에 만족할 만한 DC억압능력을 갖도록 하는 방법은 DC 제어 비트의 사용, Dual Code의 사용, Multimode Code의 사용 등이 있다. 어떤 방법이든 부가 비트가 사용되지만, 그 중에서 멀티모드 변조코드는 우수한 DC억압능력과 높은 코드효율을 갖고 있음에도 불구하고 복잡한 하드웨어와 높은 에러 전파율을 갖는 단점도 있다. 본 논문에서 제시하는 멀티모드 변조코드의 특징은 데이터열의 다중화를 위해 의사 스크램블 기법을, 다중화된 데이터열의 변조를 위해서는 DC-free RLL 변조코드를 사용한다. 의사 스크램블에 의한 데이터열의 다중화는 데이터를 복조할 때 에러전파 확률을 떨어뜨리는 효과가 있고 다중화된 데이터열의 변조를 위한 DC-free RLL 변조코드의 사용은 DC억압능력을 향상시키고 하드웨어가 훨씬 간단해진다.

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The road roughness based Braking Pressure Calculation System(BPCS) for an Autonomous Vehicle Stability (자율차량 안정성을 위한 도로 거칠기 기반 제동압력 계산 시스템)

  • Son, Su-Rak;Lee, Byung-Kwan;Sim, Son-Kweon
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.13 no.5
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    • pp.323-330
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    • 2020
  • This paper proposes the road roughness based Braking Pressure Calculation System(BPCS) for an Autonomous Vehicle Stability. The system consists of an image normalization module that processes the front image of a vehicle to fit the input of the random forest, a Random Forest based Road Roughness Classification Module that distinguish the roughness of the road on which the vehicle is travelling by using the weather information and the front image of a vehicle as an input, and a brake pressure control module that modifies a friction coefficient applied to the vehicle according to the road roughness and determines the braking strength to maintain optimal driving according to a vehicle ahead. To verify the efficiency of the BPCS experiment was conducted with a random forest model. The result of the experiment shows that the accuracy of the random forest model was about 2% higher than that of the SVM, and that 7 features should be bagged to make an accurate random forest model. Therefore, the BPCS satisfies both real-time and accuracy in situations where the vehicle needs to brake.