Copyright 2001 INIST-CNRS CC BY 4.0 Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Telecommunications and information theory
Accession Number:
edscal.1016040
Database:
PASCAL Archive
Weitere Informationen
According to the Marr paradigm [10], visual processing is performed by low-level feature detection followed by higher level task dependent processing. In this case, any two images exhibiting identical features will yield the same result of the visual processing. The set of images exhibiting identical features form an equivalence class: a metameric class [7]. We choose from this class the (in some precise sense) simplest image as a representative. The complexity of this simplest image may in turn be used for analyzing the information content of features. We show examples of images reconstructed from various scale-space features, and show that a low number of simple differential features carries sufficient information for reconstructing images close to identical to the human observer. The paper presents direct methods for reconstruction of minimal variance representatives, and variational methods for computation of maximum entropy and maximum a posteriori representatives based on priors for natural images. Finally, conclusions on the information content in blobs and edges are indicated.
