Marc Luxen, "Performance Evaluation in Natural and Controlled Environments applied to Feature Extraction Procedures", In Proc. 20th ISPRS Congress, Istanbul, Turkey. M. Orhan ALTAN (Eds.) Istanbul, Turkey Vol. XXXV, Part B3(B3), pp. 1061-1067. 2004.

• Abstract
• BibTeX
• URL
• Download

The paper highlights approaches to reference data acquisition in real environments for the purpose of performance evaluation of image analysis procedures. Reference data for the input and for the output of an algorithm is obtained by a) exploiting the noise characteristics of Gaussian image pyramids and b) exploiting multiple views. The approaches are employed exemplarily in the context of evaluating low level feature extraction algorithms.

@inproceedings{Luxen2004Performance,
  author = {Luxen, Marc},
  editor = {M. Orhan ALTAN},
  title = {Performance Evaluation in Natural and Controlled Environments applied to Feature Extraction Procedures},
  booktitle = {Proc. 20th ISPRS Congress, Istanbul, Turkey},
  year = {2004},
  volume = {XXXV, Part B3},
  number = {B3},
  pages = {1061--1067},
  url = {http://www.ipb.uni-bonn.de/papers/2004/luxen04.performance.html}
}

Marc Luxen and Ansgar Brunn, "Parameterschätzung aus unvollständigen Beobachtungsdaten mittels des EM-Algorithmus", Zeitschrift für Geodäsie, Geoinformation und Landmanagement (ZfV).(02), pp. 71-79. 2003.

• Abstract
• BibTeX
• Download

The paper gives an introduction into the problem of parameter estimation from incomplete data and presents the Expectation Maximization Algorithm as a method for solving such problems. The algorithm is put in relation to geodetic estimation problems. Its practicability is shown by an example of line extraction from digital images.

@article{Luxen2003Parameterschatzung,
  author = {Luxen, Marc and Brunn, Ansgar},
  title = {Parametersch\"atzung aus unvollst\"andigen Beobachtungsdaten mittels des EM-Algorithmus},
  journal = {Zeitschrift f\"ur Geod\"asie, Geoinformation und Landmanagement (ZfV)},
  year = {2003},
  number = {02},
  pages = {71--79}
}

Marc Luxen, "Variance Component Estimation in Performance Characteristics Applied to Feature Extraction Procedures", In Pattern Recognition, 25th DAGM Symposium. Bernd Michaelis and Gerald Krell (Eds.) Magdeburg, Germany,, September, 2003. Vol. 2781, pp. 498-506. Springer. 2003.

• Abstract
• BibTeX
• DOI
• Download

The paper proposes variance component estimation (VCE) for empirical quality evaluation in computer vision. An outline is given for the scope of variance component estimation in the context of quality evaluation. The principle of variance component estimation is explained and the approach is applied to results of low level feature extraction. Ground truth is only partly needed for estimating the precision, accuracy and bias of extracted points and straight line segments. The results of diverse feature extraction modules are compared.

@inproceedings{Luxen2003Variance,
  author = {Luxen, Marc},
  editor = {Bernd Michaelis and Gerald Krell},
  title = {Variance Component Estimation in Performance Characteristics Applied to Feature Extraction Procedures},
  booktitle = {Pattern Recognition, 25th DAGM Symposium},
  publisher = {Springer},
  year = {2003},
  volume = {2781},
  pages = {498--506},
  doi = {10.1007/978-3-540-45243-0_64}
}

Marc Luxen and Wolfgang Förstner, "Characterizing Image Quality: Blind Estimation of the Point Spread Function from a Single Image", In Proceedings of the PCV'02 Symposium., pp. A: 205. 2002.

• Abstract
• BibTeX
• Download

The paper describes a method for blind estimation of sharpness and resolving power from a single image. These measures can be used to characterize images in the context of the performance of image analysis procedures. The method assumes the point spread function (PSF) can be approximated by an anisotropic Gaussian. The width Sigma of the PSF is determined by the ratio Sigma_g/Sigma_g' of the standard deviations of the intensity and of its derivative at edges. The contrast sensitivity function (CSF) is based on an optimal model for detecting straight edges between homogeneous regions in noisy images. It depends on the signal to noise ratio and is linear in the frequency. The method is applied to artificial and real images proving that it gives valuable results.

@inproceedings{Luxen2002Characterizing,
  author = {Luxen, Marc and F\"orstner, Wolfgang},
  title = {Characterizing Image Quality: Blind Estimation of the Point Spread Function from a Single Image},
  booktitle = {Proceedings of the PCV'02 Symposium},
  year = {2002},
  pages = {A: 205}
}

Marc Luxen and Wolfgang Förstner, "Optimal Camera Orientation from Points and Straight Lines", In Proceedings of the DAGM 2001. Radig, Bernd and Florczyk, Stefan (Eds.) München, pp. 84-91. 2001.

• Abstract
• BibTeX
• DOI
• Download

The paper presents an optimal estimate for the projection matrix for points of a camera from an arbitrary mixture of six or more observed points and straight lines in object space. It gives expressions for determining the corresponding projection matrix for straight lines together with its covariance matrix. Examples on synthetic and real images demonstrate the feasibility of the approach.

@inproceedings{Luxen2001Optimal,
  author = {Luxen, Marc and F\"orstner, Wolfgang},
  editor = {Radig, Bernd and Florczyk, Stefan},
  title = {Optimal Camera Orientation from Points and Straight Lines},
  booktitle = {Proceedings of the DAGM 2001},
  year = {2001},
  pages = {84--91},
  doi = {10.1007/3-540-45404-7_12}
}

Marc Luxen, "Der EM-Algorithmus bei Schätz- und Klassifikationsverfahren". Thesis at: Institute of Photogrammetry, University of Bonn. 2000.

• Abstract
• BibTeX

[none]

@mastersthesis{Luxen2000Der,
  author = {Luxen, Marc},
  title = {Der EM-Algorithmus bei Sch\"atz- und Klassifikationsverfahren},
  school = {Institute of Photogrammetry, University of Bonn},
  year = {2000},
  note = {Betreuung: Prof. Dr.-Ing. Wolfgang F\"orstner, Dipl.-Ing. Ansgar Brunn}
}