Logical/linear operators for image curves

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 17; no. 10; pp. 982 - 996
• Main Authors: Iverson, L.A., Zucker, S.W.
• Format: Journal Article
• Language: English
• Published: IEEE 01.10.1995
• Subjects: Biological information theory; Boolean functions; Computer vision; Detectors; Ear; Feature extraction; Gaussian noise; Image edge detection; Integrated circuit noise; Machine vision
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/34.464562

We propose a language for designing image measurement operators suitable for early vision. We refer to them as logical/linear (L/L) operators, since they unify aspects of linear operator theory and Boolean logic. A family of these operators appropriate for measuring the low-order differential structure of image curves is developed. These L/L operators are derived by decomposing a linear model into logical components to ensure that certain structural preconditions for the existence of an image curve are upheld. Tangential conditions guarantee continuity, while normal conditions select and categorize contrast profiles. The resulting operators allow for coarse measurement of curvilinear differential structure (orientation and curvature) while successfully segregating edge-and line-like features. By thus reducing the incidence of false-positive responses, these operators are a substantial improvement over (thresholded) linear operators which attempt to resolve the same class of features.< >