RECOVERING INTRINSIC SCENE CHARACTERISTICS FROM IMAGES

We suggest that an appropriate role of early visual processing is to describe a scene in terms of intrinsic (vertical) characteristics -- such as range, orientation, reflectance, and incident illumination -- of the surface element visible at each point in the image. Support for this idea comes from three sources: the obvious utility of intrinsic characteristics for higher-level scene analysis; the apparent ability of humans to determine these characteristics, regardless of viewing conditions or familiarity with the scene; and a theoretical argument that such a description is obtainable, by a noncognitive and nonpurposive process, at least, for simple scene domains. The central problem in recovering intrinsic scene characteristics is that the information is confounded in the original light-intensity image: a single intensity value encodes all the characteristics of the corresponding scene point. Recovery depends on exploiting constraints, derived from assumptions about the nature of the scene and the physics of the imaging process.

[1]  D. Allen,et al.  Relaxation methods in engineering and science , 1954 .

[2]  Lawrence G. Roberts,et al.  Machine Perception of Three-Dimensional Solids , 1963, Outstanding Dissertations in the Computer Sciences.

[3]  N. S. Barnett,et al.  Private communication , 1969 .

[4]  Iain Donald Graham Macleod,et al.  A study in automatic photo-interpretation , 1970 .

[5]  R. Gregory.  The intelligent eye , 1970 .

[6]  Claude L. Fennema,et al.  Scene Analysis Using Regions , 1970, Artif. Intell..

[7]  B. Julesz.  Foundations of Cyclopean Perception , 1971 .

[8]  S. Coren,et al.  SUBJECTIVE CONTOURS AND APPARENT DEPTH , 2005 .

[9]  Gilbert Falk,et al.  Interpretation of Imperfect Line Data as a Three-Dimensional Scene , 1970, Artif. Intell..

[10]  David L. Waltz,et al.  Generating Semantic Descriptions From Drawings of Scenes With Shadows , 1972 .

[11]  Jerome A. Feldman,et al.  A Semantics-Based Decision Theory Region Analyser , 1973, IJCAI.

[12]  A. Macworth.  Interpreting pictures of polyhedral scenes , 1973 .

[13]  Alan K. Mackworth.  Interpreting Pictures of Polyhedral Scenes , 1973, IJCAI.

[14]  Azriel Rosenfeld,et al.  General-purpose models: expectations about the unexpected , 1975, SGAR.

[15]  Berthold K. P. Horn.  Obtaining shape from shading information , 1989 .

[16]  Harry G. Barrow,et al.  Representation and use of knowledge in vision , 1975, SGAR.

[17]  T. Garvey.  Perceptual strategies for purposive vision , 1975 .

[18]  Thomas O. Binford,et al.  Computer Description of Curved Objects , 1973, IEEE Transactions on Computers.

[19]  Azriel Rosenfeld,et al.  Scene Labeling by Relaxation Operations , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[20]  D Marr,et al.  Cooperative computation of stereo disparity. , 1976, Science.

[21]  D Marr,et al.  Early processing of visual information. , 1976, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[22]  D. Marr,et al.  Analysis of occluding contour , 1977, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[23]  Berthold K. P. Horn.  Understanding Image Intensities , 1977, Artif. Intell..

[24]  Robert J. Woodham,et al.  A Cooperative Algorithm for Determining Surface Orientation from a Single View , 1977, IJCAI.

[25]  E. Land.  The retinex theory of color vision. , 1977, Scientific American.

[26]  A. E. Brain,et al.  The measurement and use of registered reflectance and range data in scene analysis , 1977, Proceedings of the IEEE.

[27]  Harry G. Barrow,et al.  Experiments in Interpretation-Guided Segmentation , 1977, Artificial Intelligence.

[28]  David Marr,et al.  Representing Visual Information , 1977 .

[29]  A. Gilchrist.  Perceived lightness depends on perceived spatial arrangement. , 1977, Science.