1982 the-visual-inference-of-shape-computation-from-local-feature

PDF · 17 Oct 2026

This dissertation develops a rigorous computational framework for recovering three‑dimensional surface shape from a single image using local image cues such as shading, contours, and illumination direction. Central to the theory are analytic derivations that (1) identify the image direction of maximal second‑derivative intensity as the surface tilt, (2) relate the normalized image Laplacian (∇²I/I) to the principal curvatures along the tilt and its orthogonal direction, and (3) propose a maximum‑likelihood estimator for surface slant under a uniform curvature prior. The work also examines how these estimators behave on three imaging modalities—standard positive images, scanning‑electron‑microscope (EM) images, and photographic negatives—showing that simple intensity normalization can mitigate some modality‑specific errors. Detailed mathematical proofs cover coordinate‑rotation transformations of second‑order image derivatives, the convexity/concavity ambiguity inherent in tilt estimation, and the derivation of the slant estimator formula zN = σκ² (|∇²I/I| – σ_κ²)⁻². Empirical validation uses synthetic sphere intensity profiles to illustrate the estimator’s cup‑shaped response for positive images, its inverse‑square slant scaling for EM images, and the blow‑up behavior for negatives. The dissertation situates these contributions within a broader literature on shape‑from‑shading, edge detection, and visual neuroscience, providing a foundation for both psychophysical modeling and practical computer‑vision algorithms.

Topics

Tilt Direction as the Axis of Maximum Second‑Derivative

Derivation showing that the image direction where the second‑derivative of intensity is maximal coincides with surface tilt, expressed τ = ½ arctan(2I_xy/(I_xx‑I_yy)). Includes the coordinate‑rotation transformation of second‑order derivatives (I_xx, I_yy, I_xy) under a rotation by τ and discusses the inherent ±π convexity/concavity ambiguity that prevents distinguishing convex from concave patches from a single shading cue.

More from Alex

PDF

Building the New Economy _ Data As Capital -- Alex P Pentland; Alexander Lipton; Thomas Hardjono -- MIT Connection Science & Engineering, 1, 2021 -- isbn13 9780262367400 -- 472bf261c1b3a3d47f1755022cb27e7e -- Anna’s Archive.pdf

today
PDF

Social Physics _ How Social Networks Can Make Us Smarter -- Alex Pentland -- Published with a new preface, New York, NY, New York, NY, -- Penguin -- isbn13 9780143126331 -- 2aadbd20c23e807c0e10488dd3feb927 -- Anna’s Archive.pdf

today
PDF

1982_local-computation-of-shape.pdf

today
PDF

1983_a-more-rational-view-of-logic-or-up-against-the-wall-logic-i.pdf

today
PDF

1983_fractal-based-description.pdf

today
PDF

1986_parts-structured-descriptions-of-shape.pdf

today
PDF

1986_shading-into-texture.pdf

today
PDF

1988_on-the-extraction-of-shape-information-from-shading.pdf

today
PDF

1989_perception-of-non-rigid-motion-inference-of-shape-material-a.pdf

today
PDF

1994_photobook-tools-for-content-based-manipulation-of-image-data.pdf

today
PDF

1994_video-and-image-semantics-advanced-tools-for-telecommunicati.pdf

today
See all content