Preattentive processing in vision
Computer Vision, Graphics, and Image Processing
On describing complex surface shapes
Image and Vision Computing
Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Solid shape
Elements of information theory
Elements of information theory
Fast raster scan distance propagation on the discrete rectangular lattice
CVGIP: Image Understanding
Error bounds on the estimation of fractal dimension
SIAM Journal on Numerical Analysis
Dreams of Reason: The Computer and the Rise of the Sciences of Complexity
Dreams of Reason: The Computer and the Rise of the Sciences of Complexity
A Comparison of Measures for Detecting Natural Shapes in Cluttered Backgrounds
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Extracting Salient Curves from Images: An Analysis of the Saliency Network
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Indexing visual representations through the complexity map
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Complexity, Confusion, and Perceptual Grouping. Part I: The Curve-like Representation
International Journal of Computer Vision - Joint special issue on image analysis
On the Distribution of Saliency
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Intermediate-level vision is central to form perception, and we outline an approach to intermediate-level segmentation based on complexity analysis. In this second of a pair of papers, we continue the focus on edge-element grouping, and the motivating example of an edge element inferred from an unknown image. Is this local edge part of a long curve, or part of a texture? If the former, which is the next element along the curve? If the latter, is the texture like a well-combed hair pattern, in which nearby elements are oriented similarly, or more chaotic, as in a spaghetti pattern? In the previous paper we showed how these questions raise issues of complexity and dimensionality, and how context in both position and orientation are important. We now propose a measure based on tangential and normal complexities, and illustrate its computation. Tangential complexity is related to extension; normal complexity to density. Taken together they define four canonical classes of tangent distributions: those arising from curves, from texture flows, from turbulent textures, and from isolated “dust”. Examples are included.