1. RESTITUTION AUTOMATION FOR CLOSE-RANGE APPLICATIONS Artemis Valanis, Andreas Georgopoulos School of Rural and Surveying Engineering Laboratory of Photogrammetry National Technical University of Athens

2. Development of a semi- or fully- automated method for the restitution process of image products in the case of close-range applications Applicability of the method for the case of Byzantine monuments Trial applications on other kinds of monuments OBJECTIVES

3. Construction complexity Presence of decorative elements Tuffstone is the basic construction material OBJECT DESCRIPTION

4. Extensive research of the properties of the object of interest and trial application of several image processing techniques Development of a method for the detection of the objects of interest Program and interface development Application of the proposed method for Byzantine and other kinds of monuments Evaluation of the results COURSE OF STUDY

5. Noise reduction methods(mean-, order statistics and adaptive filters) Image enhancement in the frequency domain (FFT, Ideal filters, Butterworth filters) Edge detection algorithms (Sobel, Prewitt, Canny, LoG, color edge detection) Morphological processing (erosion, dilation, morphological gradient) Segmentation and thresholding techniques TRIAL APPLICATIONS OF VARIOUS IMAGE PROCESSING TECHNIQUES

6. PROBLEMS ENCOUNTERED IN THE DETECTION OF THE OBJECTS Object complexity Strong resemblance in the appearance of the stones and joints Existence of inclinated planes and shadowed areas Erosion of the construction material Presence of moisture

7. PROPOSED APPROACH Sample selection Calculation of the mean value and standard deviation of the gray values of the pixels of the sample Region Growing

8. ROUTE FOLLOWED BY THE ALGORITHM

9. REGIONS EXAMINED BY THE ALGORITHM ACCORDING TO THE POSITION OF THE CANDIDATE PIXEL

10. Connectivity criterion: At least two pixels of the examined region must belong to the object Homogeneity criterion: The arithmetic (mr) mean of the gray values of the candidate and the identified as object pixels of the currently examined regionmust belong in the confidence interval given by Equation [1] ms - z ssmr ms + z ss [1] CRITERIA EVALUATED BY THE ALGORITHM

11. DETECTION RESULTS FOR A SINGLE OBJECT

12. Definition of the area to be processed Sample selection for the objects that must be detected Application of an adaptive thresholding technique Improvement of the binary image which is yielded by the thresholding process Exploitation of the improved binary image for the automated sample selection Application of the algorithm for each one of the objects detected AUTOMATION OF THE PROCESS

13. EXAMPLE

14. INTERFACE OF THE PROGRAM

15. APPLICATIONS – BYZANTINE MONUMENTS

16. EXPERIMENTS FOR THE CASE OF THE DOME

17. EXPERIMENTS FOR THE DECORATIVE ELEMENTS

18. FINAL RESULTS AND COMPARISON

19. Tolerance: σ Tolerance: 2σ Tolerance: 3σ EVALUATION

20. EVALUATION

21. The proposed method is very flexible and fast The program used for the application of the developed methods offers a wide range of possibilities and is user friendly The accuracy of the restitution is objectively characterized as satisfactory for the case of Byzantine monuments The restitution process is accelerated by a factor of at least 1.7 CONCLUSIONS

22. Examination of more complex properties such as texture Thorough review and further development of the fully automated method Detailed research of the properties of other kinds of monuments SUGGESTIONS

23. Thank you for your attention!

24. VECTORIZATION

25. ARCH OF ADRIANOS

26. NATIONAL THEATRE

27. BYZANTINE WALL(DAPHNI MONASTERY)