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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Machine vision and applications 3 (1990), S. 45-56 
    ISSN: 1432-1769
    Keywords: computer vision ; model-based vision ; edge detection ; energy minimization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Standard edge detectors fail to find most relevant edges, finding either too many or too few, because they lack a geometric model to guide their search. We present a technique that integrates both photometric and geometric models with an initial estimate of the boundary. The strength of this approach lies in the ability of the geometric model to overcome various photometric anomalies, thereby finding boundaries that could not otherwise be found. Furthermore, edges can be scored based on their goodness of fit to the model, thus allowing one to use semantic model information to accept or reject the edges.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    International journal of computer vision 9 (1992), S. 113-136 
    ISSN: 1573-1405
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Line drawings provide an effective means of communication about the geometry of 3D objects. An understanding of how to duplicate the way humans interpret line drawings is extremely important in enabling man-machine communication with respect to images, diagrams, and spatial constructs. In particular, such an understanding could be used to provide the human with the capability to create a line-drawing sketch of a polyhedral object that the machine can automatically convert into the intended 3D model. A recently published paper (Marill 1991) presented a simple optimization procedure supposedly able to duplicate human judgment in recovering the 3D “wire frame” geometry of objects depicted in line drawings. Marill provided some impressive examples, but no theoretical justification for his approach. Here, we introduce our own work by first critically examining Marill's algorithm. We provide an explanation for why Marill's algorithm was able to perform as well as it did on the examples he presented, discuss its weaknesses, and show very simple examples where it fails. We then provide an algorithm that improves on Marill's results. In particular, we show that an effective objective function must favor both symmetry and planarity-Marill deals only with the symmetry issue. By modifying Marill's objective function to explicitly favor planar-faced solutions, and by using a more competent optimization technique, we were able to demonstrate significantly improved performance in all of the examples Marill provided and those additional ones we constructed ourselves. Finally, we examine some questions relevant to the implications of this work for understanding the human ability to interpret line drawings.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    International journal of computer vision 3 (1989), S. 73-102 
    ISSN: 1573-1405
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A new formulation of the image partitioning problem is presented: construct a complete and stable description of an image-in terms of a specified descriptive language-that is simplest in the sense of being shortest. We show that a descriptive language limited to a low-order polynomial description of the intensity variation within each region and a chain-code-like description of the region boundaries yields intuitively satisfying partitions for a wide class of images. The advantage of this formulation is that it can be extended to deal with subsequent steps of the image understanding problem (or to deal with other attributes, such as texture) in a natural way by augmenting the descriptive language. Experiments performed on a variety of both real and synthetic images demonstrate the superior performance of this approach over partitioning techniques based on clustering vectors of local image attributes and standard edge-detection techniques.
    Type of Medium: Electronic Resource
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  • 4
    Publication Date: 1990-12-01
    Print ISSN: 0932-8092
    Electronic ISSN: 1432-1769
    Topics: Computer Science
    Published by Springer
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