Download 2D and 3D Image Analysis by Moments by Jan Flusser, Tomas Suk, Barbara Zitova PDF

By Jan Flusser, Tomas Suk, Barbara Zitova

ISBN-10: 1119039355

ISBN-13: 9781119039358

Presents fresh major and fast improvement within the box of 2nd and 3D image analysis

2D and 3D snapshot research by way of Moments, is a special compendium of moment-based photograph research such as conventional equipment and in addition displays the newest improvement of the field.

The e-book provides a survey of 2nd and 3D second invariants with admire to similarity and affine spatial differences and to snapshot blurring and smoothing through numerous filters. The booklet comprehensively describes the mathematical heritage and theorems in regards to the invariants yet a wide half is usually dedicated to functional utilization of moments. functions from quite a few fields of desktop imaginative and prescient, distant sensing, clinical imaging, photograph retrieval, watermarking, and forensic research are established. consciousness is usually paid to effective algorithms of second computation.

Key features:

  • Presents a scientific review of moment-based gains utilized in 2nd and 3D snapshot analysis.
  • Demonstrates invariant houses of moments with appreciate to varied spatial and depth transformations.
  • Reviews and compares numerous orthogonal polynomials and respective moments.
  • Describes effective numerical algorithms for second computation.
  • It is a "classroom prepared" textbook with a self-contained creation to classifier design.
  • The accompanying site includes round three hundred lecture slides, Matlab codes, entire lists of the invariants, try pictures, and different supplementary material.

2D and 3D snapshot research through Moments, is perfect for mathematicians, machine scientists,   engineers, software program builders, and Ph.D scholars considering snapshot research and popularity. as a result addition of 2 introductory chapters on classifier layout, the ebook can also function a self-contained textbook for graduate college classes on item recognition.

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Example text

From the above considerations, we can clearly see one of the fundamental requirements of object recognition. The operator of the intraclass variability  must be closed under a composition in each equivalence class. If  is applied twice (possibly with different parameters), then the composition c = 1 2 must be of the same type. Without this closure property the task would be ill-posed because the “classes” would not be equivalence classes and would not define a partition, so an object could be an element of multiple classes.

A slightly different approach, but still belonging to semi-differential invariants, is the Curvature scale space (CSS) proposed in [53]. 9. Under smoothing, neighboring inflection points converge to each other and then vanish. 9 Semi-differential invariants. The object is divided by inflection points. Both convex and concave cuts can be used for a description by global invariants the number of smoothings that each inflection point survives are encoded into a curve which characterizes the object (although not completely).

So, it is a kind of generalization or extrapolation from a small sample set, where the result is always uncertain. , the recognition of unknown objects) is a routine task. Theoretically, if all the assumptions were perfectly fulfilled, the training should be very simple. Assuming that I1 , · · · , In are invariant and discriminatory, then all members of each class are mapped into a single point in the feature space irrespective of the intra-class variations and the points representing different classes are distinct.

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