[논문]Accurate M-hausdorff distance similarity combining distance orientation for matching multi-modal sensor images

doi:10.1016/j.patrec.2011.01.014

Accurate M-hausdorff distance similarity combining distance orientation for matching multi-modal sensor images

Pattern recognition letters, v.32 no.7, 2011년, pp.903 - 909

Kwon, Oh-Kyu (R&) , Kim, Dongsoo (D Imaging, Ikonisys Inc., 5 Science Park, New Haven, CT 06511, USA) , Suh, Jung Wook (Department of Electrical engineering, Yale University, 10 Hillhouse Avenue, New Haven, CT 06518, USA)

Abstract ▼ AI-Helper

Research highlights►This paper proposes a robust Hausdorff distance similarity (accurate M-HD: AMHD) between multi-modal sensor data. ►To improve robustness against the outliers when comparing the pairs of multi-modal images, the AMHD utilizes the orientation information of each point in addition to the distance transform (DT) map as a similarity criterion. AbstractAlthough Hausdorff distance (HD) has been widely used in an object identification between same modality images, the object identification between different modality images are challenging because of the poor edge correspondence coming from heterogeneous image characteristics. This paper proposes a robust Hausdorff distance similarity (accurate M-HD: AMHD) between multi-modal sensor data. To improve robustness against the outliers when comparing the pairs of multi-modal images, the AMHD utilizes the orientation information of each point in addition to the distance transform (DT) map as a similarity criterion. In the AMHD scheme, the DT map is generated by applying dead-reckoning signed DT, and the distance orientation (DO) map is constructed by employing the Kirsch compass kernel to the DT map, respectively. Using the additional information on the DO, the proposed similarity can precisely examine the outliers including non-correspondent edges and noises, and discard false correspondent distances efficiently. The computer simulations show that the proposed AMHD yields superior performance at aligning multi-modal sensor data (visible-thermal IR face images) over those achieved by the conventional robust schemes in terms of the position error between the ground truth and the computed position.

주제어

참고문헌 (15)

Computer Vision, Graphics, and Image Processing Borgefors 34 3 344 1986 10.1016/S0734-189X(86)80047-0 Distance transformations in digital images

상세보기
Dubuisson, M.-P., Jain, A.K., 1994. A modified Hausdorff distance for object matching. In: CVIP 1994, Proc. 12th IAPR Internat. Conf., Jerusalem, Israel, pp. 566-568.
IEEE Comput. Frischholz 33 2 64 2000 10.1109/2.820041 BioID: A multimodal biometric identification system

상세보기
Comput. Vision Graphics, Image Process. Grevera 95 3 317 2004 10.1016/j.cviu.2004.05.002 The “Dead reckoning” signed distance transform

상세보기
Haralick 1993 Computer and Robot Vision
Hrkac’, T., Kalafatic’, Z., Krapac, J., 2007. Infrared-visual image registration based on corners and hausdorff distance. In: Proc. 15th Scandinavian Conf. on Image Analysis, LNCS, vol. 4522. Springer-Verlag, Berlin, Germany, pp. 383-392.
IEEE Trans. Pattern Anal. Machine Intell. Huttenlocher 15 9 850 1993 10.1109/34.232073 Comparing images using the Hausdorff distance

상세보기
Comput. Biomed. Res. Kirsch 4 3 315 1971 10.1016/0010-4809(71)90034-6 Computer determination of the constituent structure of biological images

상세보기
Pattern Recognit. Kwon 34 10 2005 2001 10.1016/S0031-3203(00)00132-1 Robust Hausdorff distance matching algorithms using pyramidal structures

상세보기
Sheng, Y., Yang, X., McReynolds, D., Zhanga, Z., Gagnonb, L., Svignyc, L., 1999. Real-world multisensor image alignment using edge focusing and Hausdorff distances. In: Conf. on Sensor Fusion: Architectures, Algorithms Appl., Proc. SPIE, vol. 3791. Orlando, FL, USA, pp. 173-185.
IEEE Trans. Image Process. Sim 8 3 425 1999 10.1109/83.748897 Object matching algorithm using robust Hausdorff distance measures

상세보기
Socolinsky, D.A., Wolff, L.B., Neuheisel, J.D., Eveland, C.K., 2001. Illumination invariant face recognition using thermal infrared imagery. In: CVPR 2001, Proc. 2001 IEEE Comput. Soc. Conf., Baltimore, Baltimore, MD, USA, pp. 527-534.
Computer Vision and Image Understanding Socolinsky 91 1-2 72 2003 10.1016/S1077-3142(03)00075-4 Face recognition with visible and thermal infrared imagery

상세보기
Pattern Recognit. Wang 41 3 920 2008 10.1016/j.patcog.2007.07.012 Minutiae feature analysis for infrared hand vein pattern biometrics

상세보기
Interdisciplinary Sci.: Comput. Life Sci. Yu 1 4 280 2009 10.1007/s12539-009-0046-5 Finger-vein image recognition combining modified Hausdorff distance with minutiae feature matching

상세보기

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증

Accurate M-hausdorff distance similarity combining distance orientation for matching multi-modal sensor images

Abstract ▼ AI-Helper

주제어

참고문헌 (15)

이 논문을 인용한 문헌

관련 콘텐츠

원문 URL 링크

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트