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Internal architecture of distributed real time system of image processing and pattern recognition

Internal architecture of distributed real time system of image processing and pattern recognition. Gostev I. M. Sevastianiv L. A MIEM-PFUR Moscow 2005. Based supposition(1). Pattern – is some description of object !

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Internal architecture of distributed real time system of image processing and pattern recognition

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  1. Internal architecture of distributed real time system of image processing and pattern recognition Gostev I. M. Sevastianiv L. A MIEM-PFUR Moscow 2005

  2. Basedsupposition(1) • Pattern – is some description of object! • Pattern recognition – separation of input object in predetermined class under its features or characteristics. • We use contour of object at the heart of recognized objects. (It is base of gestalt psychology, and base of human perception of object). • Contour have considerable proportion of information about graphical object.

  3. Basedsupposition (2) Development methodology of graphical pattern recognition to invariance to 2D affine transform (translation, scaling and rotation) with receiving as result object’s coordinates and its angle of rotation relatively sample.

  4. Plan of based steps from methodology of image processing and pattern recognition Preliminary processing Input criteria and samples Receive sample contour Binarisation Pattern recognition process Clusterisation Contour tracing III I II

  5. Delta-segmentation principles • Using fly window in which calculated statistical parameters of signals on based which is assignment value of cutting level. • UsingDelta – modulation with only two resulting value of signal. Input Image Different level of intensity Output image

  6. Delta segmentation results (1) Input image and image after delta - segmentation PS. Intermediate image processing is absent

  7. Compare delta-segmentation to another methods • Input Image. • b) Image after processing SUSAN method. • c)Image after processing Delta segmentation method. • d) Image after processing Canny method. a) b) c) d)

  8. Image contour tracing. Two fragment of image after step of contour tracing Zooming of fragment of image N.B. Characteristic feature is receive closed contours of object always.

  9. Clustering and samples Step conclude is: Clusters construction – building verbal description of isolated closed contour of objects and saved its to a file. Any of objects may use as sample for process of pattern recognition Contour of recognition object (Cluster) Noise on image (Clusters) Noise’s object (Cluster).

  10. Common method of image processing.

  11. Understanding of sample Sample – this is verbal description of aggregate of groups of parameters, which unambiguously described a object. On such ofgroups are: • Processing image’s methods and condition of refinement of object; • Coordinate parts of objects; • Pattern recognition’s methods; • Classification thresholds in pattern recognition’s methods; Example of Samples

  12. Example of implementation subset of sample The set is primary vectors of properties is as set of points in 2d space: The set of secondary properties is Let as named “centre of mass” element of set

  13. Understanding “contour function” - sortedfunction of set on angle in order its increasing. - function of interpolation of points of object - normalization of contour function.

  14. Third step of methodology - recognition process Input clusters for pattern recognition 1 System identification is based on consecutive compare clusters whith preload sample in set of methods. Each next method is more calculation difficult and more precision. 2 3 4 Recognized objects “sieve of the methods”

  15. Methodof consecutive weighing. MCW – this is:

  16. Geometric correlation # 1 (GC1) as function if “difference values” and Let us where Let us “function of deviation” as where The function of recognition on based of geometric correlation#1 is where

  17. Geometric correlation #2 (GC2) as mean deviation Let us function from where The function of recognition on based of geometric correlation#2 is where

  18. Example of pattern recognition (1) Sample of objects (zoomed) Message about result of recognition Image in real size Zooming fragment

  19. Example of pattern recognition (2) Sample of objects. Recognized objects

  20. Example of pattern recognition (3) Sample of objects

  21. Parallel processing of input graphical flow Result of image processing and pattern recognition Input information flow is cutting on part and each part is processing into parallel process.

  22. Conveyer processing image and recognition inSTIPR2000 Input image k k, l, m, n – required numbers of line in a method l 1-stMethod m 2-ndmethod n 3-dMethod Output results N- method Conveyer of methods image

  23. Controller of flow

  24. (x, y ), α 1-st host 2-nd host Work diagram of the system for dynamic identification graphic object (1)

  25. 1-st host (x, y ), α 2-nd host Work diagram of the system for dynamic identification graphic object (2)

  26. The work is carried out by the associated professor Gostev Ivan Michailovich and his post-graduates students. Тел: |(095) 916 -8886 mob 7 916-610-7801 Fax: (095) 952-2823 E-mail: igostev@gmail.com igostev@list.ru Address: Moscow 117419 Ordzhonikidze 3 Thank You!

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