By Frédéric Cao, José-Luis Lisani, Jean-Michel Morel, Pablo Musé, Frédéric Sur

ISBN-10: 3540684808

ISBN-13: 9783540684800

Recent years have obvious dramatic development match popularity algorithms utilized to ever-growing photograph databases. they've been utilized to picture sewing, stereo imaginative and prescient, photo mosaics, reliable item popularity and video or internet photo retrieval. extra essentially, the power of people and animals to discover and realize shapes is without doubt one of the enigmas of notion.

The e-book describes an entire procedure that starts off from a question snapshot and a picture database and yields a listing of the pictures within the database containing shapes found in the question photograph. A fake alarm quantity is linked to every detection. Many experiments will exhibit that popular basic shapes or pictures can reliably be pointed out with fake alarm numbers starting from 10-5 to lower than 10-300.

Technically conversing, there are major matters. the 1st is extracting invariant form descriptors from electronic photographs. the second one is finding out even if form descriptors are identifiable because the comparable form or now not. A perceptual precept, the Helmholtz precept, is the cornerstone of this selection.

These judgements depend on uncomplicated stochastic geometry and compute a fake alarm quantity. The decrease this quantity, the safer the id. the outline of the methods, the various experiments on electronic pictures and the easy proofs of mathematical correctness are interlaced as a way to make a analyzing obtainable to numerous audiences, comparable to scholars, engineers, and researchers.

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Extra resources for A Theory of Shape Identification

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Such a random model is called a contrario or background model. A curve Ci is said to be ε-meaningful if NFA(Ci ) = #E · H(µi )Li < ε. 4 A Mathematical Justification of Meaningful Contrasted Boundaries 23 Remark 1. In digital images, the independence of the values of |Du| is sound only if the points are far enough from each other. In practice, the minimal distance will be taken equal to 2, since a 2 × 2 finite difference scheme is used to compute the image gradient. The following proposition justifies Def.

The main drawbacks of these methods are the number of thresholds (edge detection needs at least a gradient threshold, and the Hough Transform needs a quantization step for the parameter space discretization and a threshold for the voting procedure) and their computational burden and instability (due to local edges chaining). A fuzzy segment concept was proposed in [45]. In this method the primary detection is still based on a set of points derived from a local edge detector. 4 Bibliographic Notes 57 The method presented in this chapter can be viewed as an adaptation to the level lines of Desolneux et al.

When extracting shape elements by local encoding, all the different configurations near the junctions will be considered. Clearly, meaningful level lines provide a set of curves which is more reliable and directly usable at the expense of a more heavy computational cost (a few seconds for a typical 512 × 512 images, half the time being dedicated to the computation of the level lines tree and half to the selection of meaningful boundaries). 34 2 Extracting Meaningful Curves from Images Fig. 10 Junction and level lines.

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A Theory of Shape Identification by Frédéric Cao, José-Luis Lisani, Jean-Michel Morel, Pablo Musé, Frédéric Sur


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