• Title/Summary/Keyword: 특징기반 정합

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Area based image matching with MOC-NA imagery (MOC-NA 영상의 영역기준 영상정합)

  • Youn, Jun-Hee;Park, Choung-Hwan
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.28 no.4
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    • pp.463-469
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    • 2010
  • Since MOLA(Mars Orbiter Laser Altimeter) data, which provides altimetry data for Mars, does not cover the whole Mars area, image matching with MOC imagery should be implemented for the generation of DEM. However, automatic image matching is difficult because of insufficient features and low contrast. In this paper, we present the area based semi-automatic image matching algorithm with MOC-NA(Mars Orbiter Camera ? Narrow Angle) imagery. To accomplish this, seed points describing conjugate points are manually added for the stereo imagery, and interesting points are automatically produced by using such seed points. Produced interesting points being used as initial conjugate points, area based image matching is implemented. For the points which fail to match, the locations of initial conjugate points are recalculated by using matched six points and image matching process is re-implemented. The quality assessment by reversing the role of target and search image shows 97.5 % of points were laid within one pixel absolute difference.

Target Object Extraction Based on Clustering (클러스터링 기반의 목표물체 분할)

  • Jang, Seok-Woo;Park, Young-Jae;Kim, Gye-Young;Lee, Suk-Yun
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2013.01a
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    • pp.227-228
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    • 2013
  • 본 논문에서는 연속적으로 입력되는 스테레오 입체 영상으로부터 2차원과 3차원의 특징을 결합하여 군집화함으로써 대상 물체를 보다 강건하게 분할하는 기법을 제안한다. 제안된 방법에서는 촬영된 장면의 좌우 영상으로부터 스테레오 정합 알고리즘을 이용해 영상의 각 화소별로 카메라와 물체 사이의 거리를 나타내는 깊이 특징을 추출한다. 그런 다음, 깊이와 색상 특징을 효과적으로 군집화하여 배경에 해당하는 영역을 제외하고, 전경에 해당하는 대상 물체를 감지한다. 실험에서는 제안된 방법을 여러가지 영상에 적용하여 테스트를 해 보았으며, 제안된 방법이 기존의 2차원 기반의 물체 분리 방법에 비해 보다 강건하게 대상물체를 분할함을 확인하였다.

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Illumination Robust Feature Descriptor Based on Exact Order (조명 변화에 강인한 엄격한 순차 기반의 특징점 기술자)

  • Kim, Bongjoe;Sohn, Kwanghoon
    • Journal of Broadcast Engineering
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    • v.18 no.1
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    • pp.77-87
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    • 2013
  • In this paper, we present a novel method for local image descriptor called exact order based descriptor (EOD) which is robust to illumination changes and Gaussian noise. Exact orders of image patch is induced by changing discrete intensity value into k-dimensional continuous vector to resolve the ambiguity of ordering for same intensity pixel value. EOD is generated from overall distribution of exact orders in the patch. The proposed local descriptor is compared with several state-of-the-art descriptors over a number of images. Experimental results show that the proposed method outperforms many state-of-the-art descriptors in the presence of illumination changes, blur and viewpoint change. Also, the proposed method can be used for many computer vision applications such as face recognition, texture recognition and image analysis.

Registration Method between High Resolution Optical and SAR Images (고해상도 광학영상과 SAR 영상 간 정합 기법)

  • Jeon, Hyeongju;Kim, Yongil
    • Korean Journal of Remote Sensing
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    • v.34 no.5
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    • pp.739-747
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    • 2018
  • Integration analysis of multi-sensor satellite images is becoming increasingly important. The first step in integration analysis is image registration between multi-sensor. SIFT (Scale Invariant Feature Transform) is a representative image registration method. However, optical image and SAR (Synthetic Aperture Radar) images are different from sensor attitude and radiation characteristics during acquisition, making it difficult to apply the conventional method, such as SIFT, because the radiometric characteristics between images are nonlinear. To overcome this limitation, we proposed a modified method that combines the SAR-SIFT method and shape descriptor vector DLSS(Dense Local Self-Similarity). We conducted an experiment using two pairs of Cosmo-SkyMed and KOMPSAT-2 images collected over Daejeon, Korea, an area with a high density of buildings. The proposed method extracted the correct matching points when compared to conventional methods, such as SIFT and SAR-SIFT. The method also gave quantitatively reasonable results for RMSE of 1.66m and 2.45m over the two pairs of images.

(Content-Based Video Copy Detection using Motion Directional Histogram) (모션의 방향성 히스토그램을 이용한 내용 기반 비디오 복사 검출)

  • 현기호;이재철
    • Journal of KIISE:Software and Applications
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    • v.30 no.5_6
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    • pp.497-502
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    • 2003
  • Content-based video copy detection is a complementary approach to watermarking. As opposed to watermarking, which relies on inserting a distinct pattern into the video stream, video copy detection techniques match content-based signatures to detect copies of video. Existing typical content-based copy detection schemes have relied on image matching which is based on key frame detection. This paper proposes a motion directional histogram, which is quantized and accumulated the direction of motion, for video copy detection. The video clip is represented by a motion directional histogram as a 1-dimensional graph. This method is suitable for real time indexing and counting the TV CF verification that is high motion video clips.

Feature-based Disparity Correction for the Visual Discomfort Minimization of Stereoscopic Video Camera (입체영상의 시각 피로 최소화를 위한 특징기반 시차 보정)

  • Jung, Eun-Kyung;Kim, Chang-Il;Baek, Seung-Hae;Park, Soon-Yong
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.6
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    • pp.77-87
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    • 2011
  • In this paper, we propose a disparity correction technique to reduce the inherent visual discomfort while watching stereoscopic videos. The visual discomfort must be solved for commercial 3D display systems to provide natural stereoscopic videos to human eyes. The proposed disparity correction technique consists of horizontal and vertical disparity corrections. The horizontal disparity correction is implemented by controlling the depth budget of stereoscopic video using the geometric relations of a stereoscopic camera system. In addition, the vertical disparity correction is implemented by using a feature-based stereo matching algorithm. Conventional vertical disparity corrections have been done by only using camera calibration parameters, which still cause systematic errors in vertical disparities. In this paper, we minimize the vertical disparity as small as possible by using a feature-based correction algorithm. Through the comparisons of conventional feature-based correction algorithms, we analyze the performance of the proposed technique.

Object Recognition using Entropy Measure on Line Features (특징 선분과 엔트로피 측도를 이용한 물체 인식)

  • Ko, San;Lee, Kyong-Mu;Zhang, Byoung-Tak
    • Proceedings of the Korean Society for Cognitive Science Conference
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    • 2005.05a
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    • pp.135-140
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    • 2005
  • 본 논문에서는 크기, 회전, 위치의 변환과 물체의 가리워짐, 복잡한 이미지에 대해서도 강인하게 동작하는 새로운 물체 인식 기법을 제안한다. 제안 기법은 기하학적 해싱 알고리즘에서 착안한 정합 방식과, 새롭게 정의된 엔트로피 정합 측도를 도입함으로써, 노드 간의 대응 과정 없이도 물체의 전체 구조정보를 한꺼번에 정합에 이용 할 수 있게 하여, 기존의 노드 대응에 기반한 그래프 정합 기법이 가지고 있는 조합적 계산 복잡도를 개선하고, 동시에 노드 정보의 손실과 경험적으로 정해주어야 하는 변수들을 최소화 하였다. 속성 관계 그래프에 기반한 정합 기법과 제안 기법과의 성능 비교 실험 결과 정확도와 인식 속도 측면에서 제안 기법이 보다 좋은 성능을 보임을 확인 하였다.

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Point Cloud Registration using Feature Point (특징점을 사용한 포인트 클라우드 정합)

  • Kim, Kyung Jin;Park, Byung Seo;Kim, Dong Wook;Seo, Young Ho
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2019.11a
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    • pp.219-220
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    • 2019
  • 본 논문에서는 특징점 기반의 포인트 클라우드 정합 알고리즘을 제안한다. 컴퓨터 비전 분야에서 각각 다른 카메라에서 획득한 데이터를 하나의 통합된 데이터로 정합하는 문제에 많은 관심을 두고 있다. 기존의 방법들은 큰 오차를 가지고 있거나 많은 카메라 대수나 고가의 RGB-D 카메라를 필요로 한다. 본 논문에서는 깊이 카메라에서 얻은 깊이 영상과 색상 영상을 이용하고 함수 최적화 알고리즘을 적용해 저가의 RGB-D 카메라 8대를 이용하여 오차가 적은 포인트 클라우드 정합 방법을 제안한다.

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Classification of Feature Points Required for Multi-Frame Based Building Recognition (멀티 프레임 기반 건물 인식에 필요한 특징점 분류)

  • Park, Si-young;An, Ha-eun;Lee, Gyu-cheol;Yoo, Ji-sang
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.41 no.3
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    • pp.317-327
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    • 2016
  • The extraction of significant feature points from a video is directly associated with the suggested method's function. In particular, the occlusion regions in trees or people, or feature points extracted from the background and not from objects such as the sky or mountains are insignificant and can become the cause of undermined matching or recognition function. This paper classifies the feature points required for building recognition by using multi-frames in order to improve the recognition function(algorithm). First, through SIFT(scale invariant feature transform), the primary feature points are extracted and the mismatching feature points are removed. To categorize the feature points in occlusion regions, RANSAC(random sample consensus) is applied. Since the classified feature points were acquired through the matching method, for one feature point there are multiple descriptors and therefore a process that compiles all of them is also suggested. Experiments have verified that the suggested method is competent in its algorithm.

Face Region Detection using Face Template based on Eigenfaces (고유얼굴 기반의 얼굴형판을 이용한 얼굴영역 추출)

  • Go, Jae-Pil;Byeon, Hye-Ran
    • Journal of KIISE:Software and Applications
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    • v.27 no.11
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    • pp.1123-1132
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    • 2000
  • 얼굴영역을 추출하기 위한 방법은 크게 얼굴의 지형적 특징추출에 기반한 방법과 얼굴형판 정합에 기반한 방법으로 분류할 수 있다. 일반적으로 복잡한 배경의 영상에서는 형판정합 방법이 우수하나, 형판의 대표성을 부여하기가 어렵다는 점이 문제시되어 왔다. 본 논문에서는 얼굴영역을 추출하기 위하여 복잡한 얼굴패턴을 몇 개의 주성분 값으로 표현할 수 있는 Hotelling변환 과정을 이용하여 얼굴형판을 생성하고 이를 적용하여 얼굴의 크기, 영상의 명암, 얼굴의 위치에 무관하게 얼굴영역을 추출한다. 또한 휴리스틱한 임계치를 이용하여 두 사람 이상의 얼굴영역을 추출하고 기울어진 얼굴영역을 추출하기 위한 방법도 제시한다. 실험을 통하여 다양한 입력영상에 대한 추출 결과와 고유얼굴에 기반한 방법의 특징을 살펴 보았다.

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