A Digital Investigation Manifesting use of Geometric Stencils for the Drawing of Akrotiri Thera Prehistoric Wall Paintings

Author(s):  
Panayiotis Rousospoulos ◽  
Dimitris Arabadjis ◽  
Mihalis Exarhos ◽  
Michail Panagopoulos ◽  
Georgios Galanopoulos ◽  
...  

The present chapter deals with the problem of determining the method used to draw several celebrated and beautiful wall-paintings belonging to the Late Bronze Age (c. 1630 B.C.), that were excavated at Akrotiri, Thera, Hellas (Greece). First, the authors process the wall paintings’ digital images in order to extract the contour of their main thematic entities. Subsequently, a number of fundamental definitions are given and the main hypothesis is stated, namely that geometrical stencils were used for the drawing of the considered wall paintings. A first estimation of the probable one stoke parts of the contour is undertaken, based on curvature considerations and minimization of corresponding error functions. Next, they select families of geometrical curves as potential prototypes of the employed stencils. The selection is based on archaeological and historical criteria. A novel exhaustive curve fitting method is introduced that offers unambiguously optimal matching of two digital curves. Taking into consideration the previous stages, the exact values of the stencils’ parameters are determined. Finally, the hypothesis that stencils were used for the drawing of the considered wall paintings is supported substantially by a visual representation of the one stroke parts together with the corresponding stencil segments that generated them.

Author(s):  
Carey Witkov ◽  
Keith Zengel

The chi-squared method for parameter estimation and model testing is developed for the one-parameter case of a line with a slope but no intercept. Curve fitting is motivated, and several methods for curve fitting are introduced. The chi-squared method is shown to be the optimal curve fitting method whenever Gaussian distributed measurement uncertainties and a model are present. The central limit theorem, which assures Gaussian distributed measurement uncertainties for a wide range of physical experiments, is introduced. End-of-chapter problems are included (with solutions in an appendix).


2012 ◽  
Vol 19 (2) ◽  
pp. 381-394
Author(s):  
José Pereira ◽  
Octavian Postolache ◽  
Pedro Girão

Using A Segmented Voltage Sweep Mode and A Gaussian Curve Fitting Method to Improve Heavy Metal Measurement System PerformanceThis paper presents a voltammetric segmented voltage sweep mode that can be used to identify and measure heavy metals' concentrations. The proposed sweep mode covers a set of voltage ranges that are centered around the redox potentials of the metals that are under analysis. The heavy metal measurement system can take advantage of the historical database of measurements to identify the metals with higher concentrations in a given geographical area, and perform a segmented sweep around predefined voltage ranges or, alternatively, the system can perform a fast linear voltage sweep to identify the voltammetric current peaks and then perform a segmented voltage sweep around the set of voltages that are associated with the voltammetric current peaks. The paper also includes the presentation of two auto-calibration modes that can be used to improve system's reliability and proposes the usage of a Gaussian curve fitting of voltammetric data to identify heavy metals and to evaluate their concentrations. Several simulation and experimental results, that validate the theoretical expectations, are also presented in the paper.


2010 ◽  
Vol 26 (6-8) ◽  
pp. 801-811 ◽  
Author(s):  
Mingxiao Hu ◽  
Jieqing Feng ◽  
Jianmin Zheng

1993 ◽  
Vol 272 (1) ◽  
pp. 125-134 ◽  
Author(s):  
James M. Jordan ◽  
Michael D. Love ◽  
Harry L. Pardue

2007 ◽  
Vol 46 (13) ◽  
pp. 4549-4560 ◽  
Author(s):  
Q. Peter He ◽  
Jin Wang ◽  
Martin Pottmann ◽  
S. Joe Qin

Author(s):  
Bhavish Sushiel Agarwal ◽  
Jyoti R Desai ◽  
Snehanshu Saha

The use of hand gestures opens a wide range of application for human computer interaction. The paper makes use of haar classifiers and camShift algorithm to track the movement of hand. Parallelism is introduced at every step by segmenting the data from camshaft into an NxN grid. Every block of the grid now represents a lead point which is calculated from mean of all the points belonging to the particular grid. Now we have only N2 points to recognize the curve that was performed by the user in his action. Finally the fit that was found is compared to pre-defined curve fit data to find out the curve using Mahalanobis equation. Parallelism used in reducing the number of points to be fitted allows the recognition to be faster.


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