A framework for low level feature extraction

  • Wolfgang Förstner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 801)


The paper presents a framework for extracting low level features. Its main goal is to explicitely exploit the information content of the image as far as possible. This leads to new techniques for deriving image parameters, to either the elimination or the elucidation of ”buttons”, like thresholds, and to interpretable quality measures for the results, which may be used in subsequent steps. Feature extraction is based on local statistics of the image function. Methods are available for blind estimation of a signal dependent noise variance, for feature preserving restoration, for feature detection and classification, and for the location of general edges and points. Their favorable scale space properties are discussed.


low level features keypoints edge detection segments local image statistics noise estimation restoration adaptive thresholds scale space quality evaluation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Wolfgang Förstner
    • 1
  1. 1.Institut für PhotogrammetrieUniversität BonnBonn

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