• Title/Summary/Keyword: 영상 특징추출

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Contour and Feature Parameter Extraction for Moving Object Tracking in Traffic Scenes (도로영상에서 움직이는 물체 추적을 위한 윤곽선 및 특징 파라미터 추출)

  • Lee, Chul-Hun;Seol Sung-Wook;Joo Jae-Heum;Nam Ki-Gon
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.37 no.1
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    • pp.11-20
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    • 2000
  • This paper presents the method of extracting the contour and shape parameters for moving object tracking in traffic scenes. The contour is extracted by applying difference image method in reduction image and the features are extracted from original image to grow the accuracy of tracking. We used features such as circle distribution, center moment, and maximum and minimum ratio. Data association problem is solved by these features. Kalman filters are used for moving object tracking on real time. The simulation results indicate that the proposed algorithm appears to generate feature vectors good enough for multiple vehicle tracking.

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Panorama image generation using SURF and cylindrical projection (SURF와 실린더 투영을 이용한 파노라마 영상 생성 기법)

  • Kim, Jongho;Park, Siyoung;Yoo, Jisang
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2014.11a
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    • pp.242-244
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    • 2014
  • 파노라마 영상은 하나의 영상이 가지는 제한된 시점의 한계를 극복하고 폭넓은 시야를 가질 수 있다는 점에서 최근 여러 분야에서 활용되고 있는 기술이다. 본 논문에서는 자연스러운 파노라마 영상 생성을 위해 SURF(speed up robust feature)를 이용한 특징점 기반의 파노라마 영상 생성 기법을 제안한다. SURF 알고리즘을 사용하면 정합할 두 영상에서 특징점들을 추출할 수 있다. 추출된 특징점들을 RANSAC(random sample consensus) 알고리즘을 통해 특징점 간 정합시 오차율을 최소화한다. 또한, 이미지 왜곡을 최소화하기 위해 실린더 투영을 이용하여 영상을 보정한다. 최종적으로, 서로 다른 두 영상을 합성할 때 발생하는 경계 주변의 이질감을 보완하기 위해 블렌딩 기법을 사용함으로써 자연스러운 파노라마 영상을 생성한다.

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Region of Interest Extraction Method and Hardware Implementation of Matrix Pattern Image (매트릭스 패턴 영상의 관심 영역 추출 방법 및 하드웨어 구현)

  • Cho, Hosang;Kim, Geun-Jun;Kang, Bongsoon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.4
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    • pp.940-947
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    • 2015
  • This paper presents the region of interest pattern image extraction method on a display printed matrix pattern. Proposed method can not use conventional method such as laser, ultrasonic waves and touch sensor. It searches feature point and rotation angle using luminance and pattern reliable feature points of input image, and then it extracts region of interest. In order to extract region of interest, we simulate proposed method using pattern image written various angles on display panel. The proposed method makes progress using the OpenCV and the window program, and was designed using Verilog-HDL and was verified through the FPGA Board(xc6vlx760) of Xilinx.

Content-based Retrieval System using Image Shape Features (영상 형태 특징을 이용한 내용 기반 검색 시스템)

  • 황병곤;정성호;이상열
    • Journal of Korea Society of Industrial Information Systems
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    • v.6 no.1
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    • pp.33-38
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    • 2001
  • In this paper, we present an image retrieval system using shape features. The preprocessing to gain shape feature includes edge extraction using chain code. The shape features consist of center of mass, standard deviation, ratio of major axis and minor axis length. The similarity is estimated as comparing the features of query image with the features of images in database. Thus, the candidates of images are retrieved according to the order of similarity. The result of an experimentation is dullness for scale, rotation and translation. We evaluate the performance of shape features for image retrieval on a database with over 170 images. The Recall and the Precision is each 0.72 and 0.83 in the result of average experiment. So the proposed method is presented useful method.

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An Improved Feature Extraction Technique of Asterias Amurensis using 6-Directional Scanning and Centers of Region (6-방향 스캐닝과 영역 중심점을 이용한 아무르불가사리의 개선된 특징 추출 기법)

  • Shin, Hyun-Deok;Chu, Ran-Heui
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.13 no.2
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    • pp.67-75
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    • 2013
  • Korea has developed coastal farming industry due to the environmental characteristics that its three sides are surrounded by sea. The damage of coastal farming industry caused by Asterias Amurensis with very strong reproductive rate and predaciousness has increased sharply every year. Moreover, Asterias Amurensis preys on living fish and shellfish and so the damage of fishermen is vern greater. In this paper, a method is proposed to extract effectively the features from the image of Asterias Amurensis acquired in the water. Because the proposed method extracts convex features using 6-directional scanning, it selects a fewer number of feature candidates than the conventional one. In addition, after selecting candidate concave points using the extracted convex features and centers of region, the final concave features are extracted. Due to the features of the starfish which lives in groups, individuals of the starfish in the input image are concentrated. Thus, it is significant to minimize the number of feature candidates extracted from the input image. The experimental results indicate an improvement of the proposed feature extraction method over the conventional one as evidenced by the fact that the feature extract was 88 % of the feature candidates.

Face Feature Extraction for Automatic Character Creation (캐릭터의 자동 생성을 위한 얼굴에서의 특징 추출)

  • 정종률;정승도;조정원;최병욱
    • Proceedings of the IEEK Conference
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    • 2001.09a
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    • pp.161-164
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    • 2001
  • 캐릭터의 자동 생성이란 영상처리 기법을 이용하여 사람의 얼굴에서 특징을 추출하고, 이 특징들을 기반으로 독특한 캐릭터를 자동으로 얻어내는 방법을 의미한다. 본 논문에서는 사람마다의 얼굴의 특성에 기반한 캐릭터를 자동으로 생성하기 위하여 얼굴의 각 구성요소들의 특징을 효과적으로 추출하기 위한 방법을 제시한다. 얼굴을 구성하는 각각의 요소들의 특징을 추출하고, 추출된 특징을 바탕으로 각 구성요소에 해당하는 데이터베이스를 검색하여 특징을 잘 표현할 수 있는 그림을 선택한다. 최종적으로 선택된 그림들은 원 이미지의 비율에 맞도록 재구성하여 얼굴 캐릭터를 생성한다.

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Efficient Markov Feature Extraction Method for Image Splicing Detection (접합영상 검출을 위한 효율적인 마코프 특징 추출 방법)

  • Han, Jong-Goo;Park, Tae-Hee;Eom, Il-Kyu
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.9
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    • pp.111-118
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    • 2014
  • This paper presents an efficient Markov feature extraction method for detecting splicing forged images. The Markov states used in our method are composed of the difference between DCT coefficients in the adjacent blocks. Various first-order Markov state transition probabilities are extracted as features for splicing detection. In addition, we propose a feature reduction algorithm by analysing the distribution of the Markov probability. After training the extracted feature vectors using the SVM classifier, we determine whether the presence of the image splicing forgery. Experimental results verify that the proposed method shows good detection performance with a smaller number of features compared to existing methods.

An Embedded FAST Hardware Accelerator for Image Feature Detection (영상 특징 추출을 위한 내장형 FAST 하드웨어 가속기)

  • Kim, Taek-Kyu
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.49 no.2
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    • pp.28-34
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    • 2012
  • Various feature extraction algorithms are widely applied to real-time image processing applications for extracting significant features from images. Feature extraction algorithms are mostly combined with image processing algorithms mostly for image tracking and recognition. Feature extraction function is used to supply feature information to the other image processing algorithms and it is mainly implemented in a preprocessing stage. Nowadays, image processing applications are faced with embedded system implementation for a real-time processing. In order to satisfy this requirement, it is necessary to reduce execution time so as to improve the performance. Reducing the time for executing a feature extraction function dose not only extend the execution time for the other image processing algorithms, but it also helps satisfy a real-time requirement. This paper explains FAST (Feature from Accelerated Segment Test algorithm) of E. Rosten and presents FPGA-based embedded hardware accelerator architecture. The proposed acceleration scheme can be implemented by using approximately 2,217 Flip Flops, 5,034 LUTs, 2,833 Slices, and 18 Block RAMs in the Xilinx Vertex IV FPGA. In the Modelsim - based simulation result, the proposed hardware accelerator takes 3.06 ms to extract 954 features from a image with $640{\times}480$ pixels and this result shows the cost effectiveness of the propose scheme.

A Texture-Dependent Color Feature for CBIR (질감의존 색 특징을 이용한 내용기반 영상검색)

  • 정재웅;권태완;박섭형
    • Proceedings of the IEEK Conference
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    • 2003.07e
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    • pp.1819-1822
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    • 2003
  • 내용 기반 영상검색에서 다중 특징을 사용하여 영상을 검색하는 기존의 방법들은 영상에서 특징간의 상관관계를 고려하지 않고 각 특징을 개별적으로 추출하여 검색에 사용한다. 따라서 특징간의 최적의 가중치를 찾아야 하는 문제가 있다. 이 논문에서는 내용기반 영상검색을 위해 색과 질감 특징을 효과적으로 표현할 수 있는 새로운 특징 벡터인 CCE (channel color energy)를 제안한다. 실험을 통하여 제안하는 방법이 정규 가중거리 비교 방법에 비해 우수한 성능을 보이는 것을 확인하였다.

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Classification of Phornographic Video with using the Features of Multiple Audio (다중 오디오 특징을 이용한 유해 동영상의 판별)

  • Kim, Jung-Soo;Chung, Myung-Bum;Sung, Bo-Kyung;Kwon, Jin-Man;Koo, Kwang-Hyo;Ko, Il-Ju
    • 한국HCI학회:학술대회논문집
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    • 2009.02a
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    • pp.522-525
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    • 2009
  • This paper proposed the content-based method of classifying filthy Phornographic video, which causes a big problem of modern society as the reverse function of internet. Audio data was used to extract the features from Phornographic video. There are frequency spectrum, autocorrelation, and MFCC as the feature of audio used in this paper. The sound that could be filthy contents was extracted, and the Phornographic was classified by measuring how much percentage of relevant sound was corresponding with the whole audio of video. For the experiment on the proposed method, The efficiency of classifying Phornographic was measured on each feature, and the measured result and comparison with using multi features were performed. I can obtain the better result than when only one feature of audio was extracted, and used.

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