Delaunay triangulation in Alaska

[Auge_Ohr]

btw. your Picture is not "stable". when overload by other Windows all is gone.

And how to do this program?

you have to "save" your work e.g. using GraSaveScreen()
to "restore" Screen when need use oStatic:Paint := {|| GraRestScreen() }
both Function you find under XbpBitmap()

Code: Select all

   // use Bitmap to save work
   //
   ::oBitmap := XbpBitmap():new():create()

   // make the presentation space and assign to Device
   //
   ::oPS := XbpPresSpace():New()
   ::oPS:mode := XBPPS_MODE_HIGH_PRECISION
   ::oPS:Create(::Static1:WinDevice())

   // make empty Bitmap same Size
   //
   ::oBitmap:presSpace( ::oPS )
   ::oBitmap:make( ::aScreenSize[1], ::aScreenSize[2] )

   // hDC need for DLLcall
   //
   ::hDC := ::Static1:winDevice():getHDC()

   // "save" Screen
   //
   ::oSaveScreen := GraSaveScreen(::oPS,{0,0},::aScreenSize)

   // use :paint callback slot to repaint
   //
   ::Static1:paint := {|aRect,uNIL,oSelf| ::StaticPaint(aRect) }

   ...

METHOD DemoDlg:StaticPaint(aRect)
  GraRestScreen(::oPS,aRect,::oSaveScreen)
RETURN Self

[Auge_Ohr]

i have test your new Code and Speed is much better
i did not reach your i7 Speed with my FX8350 and Class Code iVar -> LOCAL (about 11min for ALL)

Question : why are you not using a Retangle which include all Points ?

1st, i think, it looks better
2nd, First Ribs now still to slow ... not sure why you need 3 x FOR / NEXT Loop
with 4 Point on Edge you can easy "calculate" First Ribs -> 0,01 Sec.

your Code seems to start in lower/left and grow to upper/right
when have 4 Point on Edge it seems to start in every Edge ... hm ...

... what about :
1st if a have only 1000/4 -> 250 Points they will be calculate quick even if i have to run it 4 x ( Thread ?).
2nd now delete 3 Edge Points and use it with other Array to calculate "missing" ... hm ...

[Eugene Lutsenko]

Search existing edges, and triangles by using the search in arrays does not always work. While I give it up, even though it is a very accelerated program. But if there are two programs: one is very fast, and the other is working properly, then I vibiraem the one that works correctly. I am trying to integrate the Delaunay triangulation in Eidos system. All rework.

[Eugene Lutsenko]

Solve the problem of proper boundaries of the drawing area at a regular grid and eliminate the intersections of ribs

[Eugene Lutsenko]

24/03/2016 A geo cognitive subsystem 4.8, which provides recovery of values of functions on the basis of the argument. Converts 2D Excel-table named "Inp_map.xls" in the original data file "Inp_data.dbf", having the coordinates X, Y, Z points and their attributes (model map database attribute information). It renders the original data from the database "Inp_data.dbf" or final results from a database of recognition: "Rsp_it.dbf" in chart form (grid and Gradient color) using Delaunay triangulation.

Thank you Roger and Jimmy for your help!

http://lc.kubagro.ru/aidos/_Aidos-X.htm

[Eugene Lutsenko]

I basically completed the subsystem 4.8, for which versed with the Delaunay triangulation. The system here:
ftp://lc.kubagro.ru/Dima/Aidos-X.zip

[Eugene Lutsenko]

Thanks to your help, I was able to do a great job, which has written several articles. And today in the subsystem 4.8. I did delineating the image mode of the brightness and color contrast.

Луценко Е.В. Количественное измерение сходства-различия клонов винограда по контурам листьев с применением АСК-анализа и системы «Эйдос» / Е.В. Луценко, Л.П. Трошин, Д.К. Бандык // Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета (Научный журнал КубГАУ) [Электронный ресурс]. – Краснодар: КубГАУ, 2016. – №02(116). С. 1205 – 1228. – IDA [article ID]: 1161602077. – Режим доступа: http://ej.kubagro.ru/2016/02/pdf/77.pdf, 1,5 у.п.л.

Луценко Е.В. Система "Эйдос" как геокогнитивная система (ГКС) для восстановления неизвестных значений пространственно-распределенных функций на основе описательной информации картографических баз данных / Е.В. Луценко, Д.К. Бандык // Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета (Научный журнал КубГАУ) [Электронный ресурс]. – Краснодар: КубГАУ, 2016. – №03(117). С. 1 – 49. – IDA [article ID]: 1171603001. – Режим доступа: http://ej.kubagro.ru/2016/03/pdf/01.pdf, 3,062 у.п.л.

Луценко Е.В. Решение задачи классификации боеприпасов по типам стрелкового нарезного оружия методом АСК-анализа / Е.В. Луценко, С.В. Швец, Д.К. Бандык // Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета (Научный журнал КубГАУ) [Электронный ресурс]. – Краснодар: КубГАУ, 2016. – №03(117). С. 838 – 872. – IDA [article ID]: 1171603055. – Режим доступа: http://ej.kubagro.ru/2016/03/pdf/55.pdf, 2,188 у.п.л.

Луценко Е.В. Идентификация типов и моделей самолетов путем АСК-анализа их силуэтов (контуров) (обобщение, абстрагирование, классификация и идентификация) / Е.В. Луценко, Д.К. Бандык // Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета (Научный журнал КубГАУ) [Электронный ресурс]. – Краснодар: КубГАУ, 2015. – №10(114). С. 1316 – 1367. – IDA [article ID]: 1141510099. – Режим доступа: http://ej.kubagro.ru/2015/10/pdf/99.pdf, 3,25 у.п.л.

Луценко Е.В. Автоматизированный системно-когнитивный анализ изображений по их пикселям (обобщение, абстрагирование, классификация и идентификация) / Е.В. Луценко // Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета (Научный журнал КубГАУ) [Электронный ресурс]. – Краснодар: КубГАУ, 2015. – №07(111). С. 334 – 362. – IDA [article ID]: 1111507019. – Режим доступа: http://ej.kubagro.ru/2015/07/pdf/19.pdf, 1,812 у.п.л.

Луценко Е.В. Решение задач ампелографии с применением АСК-анализа изображений листьев по их внешним контурам (обобщение, абстрагирование, классификация и идентификация) / Е.В. Луценко, Д.К. Бандык, Л.П. Трошин // Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета (Научный журнал КубГАУ) [Электронный ресурс]. – Краснодар: КубГАУ, 2015. – №08(112). С. 862 – 910. – IDA [article ID]: 1121508064. – Режим доступа: http://ej.kubagro.ru/2015/08/pdf/64.pdf, 3,062 у.п.л.
UDC 004.8

Technical sciences
"EIDOS" SYSTEM AS A GEO-COGNITIVE SYSTEM (GCS) FOR RECOVERING UNKNOWN VALUES OF SPATIALLY DISTRIBUTED FUNCTIONS BASED ON DESCRIPTIVE INFORMATION FROM CARTOGRAPHIC DATABASES

Lutsenko Eugeny Veniaminovich
Dr.Sci.Econ., Cand.Tech.Sci., professor
RSCI SPIN-code: 9523-7101
prof.lutsenko@gmail.com

Kuban State Agrarian University, Krasnodar, Russia
Bandyk Dmitry Konstantinovich
Artificial intelligence systems developer
RSCI SPIN-code: 4072-8442
bandyk_dd@mail.ru
Belarus

The article proposes to use the automated system-cognitive analysis (ASC-analysis) and its software tool which is "Eidos" system to solving multiparameter typing, system identification and cartographic visualization of spatially-distributed natural, environmental and socio-economic systems. Imagine, that we have an original point cloud with coordinates (X,Y,Z), each with known values of gradation descriptive scales of nominal, ordinal, or numeric type S(s1,s2,...,sn). Then the "Eidos" system provides: 1) building a model that contains generalized knowledge about the strength and the direction of the influence of descriptive gradations of scales at Z=M(S); 2) estimation of the values of Z for points (X,Y) described in the same descriptive scales S(s1,s2,...,sn), but not a part of the original point cloud; 3) a cartographic visualization of the spatial distribution of values of the function Z=M(S) for points outside the initial cloud, using Delaunay triangulation. Basically, this means that the "Eidos" system ensures recovery of the unknown function values on the grounds of the argument and implements it in a generic setting, independent of subject area. We propose a new scientific concept called "Geo-cognition system", which is defined as a software system that provides conversion of source data into information, and knowledge in visualization and mapping of this knowledge, resulting in the cognitive map becomes graphics. This feature can be used to quantify the degree of suitability of the watersheds for cultivation of certain crops, the evaluation of the ecological situation on particular territories on the structure and intensity of anthropogenic load, visualization of results of forecasting of earthquakes and other unwanted risks or emergencies, as well as for solving many other similar mathematical essence of tasks in a variety of subject areas. We have also shown a simple numerical example

Keywords: ASC-ANALYSIS, AUTOMATED SYSTEM-COGNITIVE ANALYSIS, "EIDOS" GEOINFORMATION INTELLIGENT SYSTEM, MULTIPARAMETER TYPING, SYSTEM IDENTIFICATION MAPPING DATABASE COGNITIVE GRAPHICS

UDC 004.8

Technical sciences
HOW TO SOLVE THE TASK OF CLASSIFICATION OF TYPES OF RIFLE AMMUNITION USING THE METHOD OF ASC-ANALYSIS

Lutsenko Eugeny Veniaminovich
Dr.Sci.Econ., Cand.Tech.Sci., professor
RSCI SPIN-code: 9523-7101
prof.lutsenko@gmail.com

Kuban State Agrarian University, Krasnodar, Russia
Shvets Sergey Vladimirovich
Dr.Sci. in Laws, associate professor
RSCI SPIN-code: 7584-8573
baharat@yandex.ru
Kuban State Agrarian University, Krasnodar, Russia

Bandyk Dmitry Konstantinovich
Artificial intelligence systems developer
RSCI SPIN-code: 4072-8442
bandyk_dd@mail.ru
Belarus

In forensics there is an urgent need to determine the type of rifle (automatic, rifle, large caliber pistol) depending on its used ammunition found at the scene of the use of weapons. We offer a solution to this problem with the use of new innovative method of artificial intelligence: automated system-cognitive analysis (ASC-analysis) and its program toolkitwhich is a universal cognitive analytical system called "Eidos". In the "Eidos" system we have implemented the software interface that allows posting of images and identifying their outer contours. By multivariable typing, the system creates a systemic-cognitive model, the use of which, if the model is sufficiently accurate, may be helpful in solving problems of system identification, prediction, classification, decision support and research of the modeled object by studying its model. For this task the following stages: 1) input images of ammunitions into the "Eidos" system and creation of their mathematical models; 2) the synthesis and verification of the models of generalized images of ammunition for types of weapons based on the contour images of specific munitions (multiparameter typing); 3) improving the quality of the model by separating classes for typical and atypical parts; 4) quantification of the similarities-the differences between specific types of munitions with generic images of different types of ammunition of the weapon (system identification); 5) quantification of the similarity-differences between types of ammunition, i.e. cluster-constructive analysis of generalized images of ammunition. A numerical example is given. We also possess a successful experience of solving similar problems in other subject areas

Keywords: ASC-ANALYSIS, AUTOMATED SYSTEM-COGNITIVE ANALYSIS, INTELLIGENCE SYSTEM "EIDOS", MULTIPARAMETER TYPING, SYSTEM IDENTIFICATION, INTELLIGENT IMAGE ANALYSIS

UDC 004.8

Technical sciences

QUANTITATIVE MEASUREMENT OF THE SIMILARITIES AND DIFFERENCES OF CLONES OF GRAPES USING CONTOURS OF LEAVES WITH THE USE OF ASC-ANALYSIS AND "EIDOS" SYSTEM

Lutsenko Eugeny Veniaminovich
Dr.Sci.Econ., Cand.Tech.Sci., professor
RSCI SPIN-code: 9523-7101
prof.lutsenko@gmail.com


Troshin Leonid Petrovich
Dr.Sci.Biol., professor
RSCI SPIN-код: 3386-2768
lptroshin@mail.ru
Kuban State Agrarian University, Krasnodar, Russia

Bandyk Dmitry Konstantinovich
Artificial intelligence systems developer
RSCI SPIN-code: 4072-8442
bandyk_dd@mail.ru
Belarus

The article discusses the application of automated system-cognitive analysis (ASC-analysis), its mathematical model is a system of information theory and implements, its software tools – intellectual system called "Eidos" for solving one of the important tasks of ampelography: to quantify the similarities and differences of different clones of grapes using contours of the leaves. To solve this task we perform the following steps: 1) digitization of scanned images of the leaves and creation their mathematical models; 2) formation mathematical models of specific leaves with the application of information theory; 3) modeling the generalized images of leaves of different clones on the basis of specific leaves (multiparameter typing); 4) verification of the model by identifying specific leaf images with generic clones, i.e., classes (system identification); 5) quantification of the similarities and differences of the clones, i.e. cluster-constructive analysis of generalized images of leaves of various clones. The specific shape of the contour of the leaf is regarded as noise information on the clone to which it relates, including information about the true shape of a leaf of this clone (clean signal) and noise, which distort the real shape, due to the random influence of the environment. Software tools of ASA-analysis which is intellectual "Eidos" system provides the noise suppression and the detection of a signal about the true shape of a leaf of each clone on the basis of a number of noisy concrete examples of the leaves of this clone. This creates a single image of the shape of the leaf of each clone, independent of their specific implementations, i.e. "Eidos" of these images (in the sense of Plato) - the prototype or archetype (in the Jungian sense) of the images

Keywords: MULTI-TYPING, SYSTEM IDENTIFICATION, IMAGE, LEAVES, GRAPES, CLONE, SIMILARITIES AND DIFFERENCE, GENERALIZED IMAGES, ASC-ANALYSIS, AUTOMATED SYSTEM-COGNITIVE ANALYSIS, "EIDOS" SYSTEM

UDC 303.732.4

Physical-Mathematical sciences
IDENTIFICATION OF TYPES AND MODELS OF AIRCRAFT USING ASC-ANALYSIS OF THEIR SILHOUETTES (CONTOURS) (GENERALIZATION, ABSTRACTION, CLASSIFICATION AND IDENTIFICATION)

Lutsenko Eugeny Veniaminovich
Dr.Sci.Econ., Cand.Tech.Sci., professor
RSCI SPIN-code: 9523-7101
prof.lutsenko@gmail.com

Kuban State Agrarian University, Krasnodar, Russia
Bandyk Dmitry Konstantinovich
Artificial intelligence systems developer
RSCI SPIN-code: 4072-8442
bandyk_dd@mail.ru
Belarus

The article discusses the application of automated system-cognitive analysis (ASC-analysis), its mathematical model which is system theory of information and its software tool, which is intellectual system called "Eidos" for solving problems related to identification of types and models of aircraft by their silhouettes on the ground, to be more precise, their external contours: 1) digitization of scanned images of aircraft and creation of their mathematical models; 2) formation of mathematical models of specific aircraft with the use of the information theory; 3) modeling of the generalized images of various aircraft types and models and their graphic visualization; 4) comparing an image of a particular plane with generalized images of various aircraft types and models, and quantifying the degree of similarities and differences between them, i.e., the identification of the type and model of airplane by its silhouette (contour) on the ground; 5) quantification of the similarities and differences of the generalized images of the planes with each other, i.e., cluster-constructive analysis of generalized images of various aircraft types and models. The article gives a new approach to digitizing images of aircraft, based on the use of the polar coordinate system, the center of gravity of the image and its external contour. Before digitizing images, we may use their transformation, standardizing the position of the images, their sizes (resolution, distance) and the angle of rotation (angle) in three dimensions. Therefore, the results of digitization and ASC-analysis of the images can be invariant (independent) relative to their position, dimensions and turns. The shape of the contour of a particular aircraft is considered as a noise information on the type and model of aircraft, including information about the true shape of the aircraft type and its model (clean signal) and noise, which distort the real shape, due to noise influences, both of the means of countering detection and identification, and environment. Software tool of ASC-analysis, i.e. Eidos intellectual system, provides identification of the type and the model of airplane by its silhouette, as it was shown in a simplified numerical example

Keywords: ASC-ANALYSIS, AUTOMATED SYSTEM-COGNITIVE ANALYSIS, "EIDOS" INTELLIGENT SYSTEM, INPUT, DIGITIZATION OF IMAGES, GENERALIZED IMAGE SYNTHESIS, ABSTRACTION, CLASSIFICATION, COMPARISON OF SPECIFIC IMAGES WITH GENERIC (IDENTIFICATION), TYPE AND MODEL OF AIRPLANES

UDC 303.732.4
Physical-Mathematical sciences
THE SOLUTION OF PROBLEMS OF AMPELOGRAPHY BY USING ASC-ANALYSIS OF IMAGES OF LEAVES IN THEIR EXTERNAL CONTOURS (GENERALIZATION, ABSTRACTION, CLASSIFICATION AND IDENTIFICATION)

Lutsenko Eugeny Veniaminovich
Dr.Sci.Econ., Cand.Tech.Sci., professor
RSCI SPIN-code: 9523-7101
prof.lutsenko@gmail.com

Kuban State Agrarian University, Krasnodar, Russia
Bandyk Dmitry Konstantinovich
RSCI SPIN-code: 4072-8442
bandyk_dd@mail.ru
artificial intelligence developer, Belarus

Troshin Leonid Petrovich
Dr.Sci.Biol., professor
RSCI SPIN-код: 3386-2768
lptroshin@mail.ru
Kuban State Agrarian University, Krasnodar, Russia

The article discusses the use of automatic systemic-cognitive analysis (ASC-analysis), its mathematical model is a system of information theory and software tools – an intellectual system called "Eidos" for the solution of some problems of ampelography: 1) digitization of scanned images of the leaves and creation of their mathematical models; 2) the formation of mathematical models of specific leaves using the spreading of information theory; 3) the formation of models of generalized images of leaves of various sorts; 4) comparing an image of a specific leaf with a generalized image of the leaf of different varieties and finding a quantitative degree of similarity and differences between them, i.e. the identification of the varieties on the leaf; 5) quantification of the similarities and differences of the varieties, i.e. cluster-constructive analysis of generalized images of the leaves of different varieties. We propose a new approach to digitizing images of leaves, based on using the polar coordinate system, the center of gravity of the image and its external contour. Before scanning images we may use transformation to standardize the position of the still images, their sizes and rotation angle. Therefore, the results of digitization and ASC-analysis of the images might be invariant (independent) relatively to their position, size and rotation. The specific shape of the contour of the leaf is regarded as noise information on the variety, including information about the true shape of the leaf of the class (clean signal) and noise, which distort this true form, originating in a random environment. Software tools of ASC-analysis – intellectual "Eidos" system ensures noise reduction and the selection of the signal about the true shape of the leaf of each variety on the basis of a number of noisy concrete examples of the leaves of this variety. This creates a one way form of a leaf of each class, free from their concrete implementations, i.e., the "Eidos" of these images (in the sense of Plato) is a prototype or archetype (in the Jungian sense) of the images

Keywords: ASC-ANALYSIS, AUTOMATED SYSTEM-COGNITIVE ANALYSIS, "EIDOS" INTELLIGENT SYSTEM, INPUT, DIGITIZATION OF IMAGES, SYNTHESIS OF GENERALIZED IMAGES, ABSTRACTION, CLASSIFICATION, COMPARISON SPECIFIC IMAGES WITH GENERIC (IDENTIFICATION), AMPELOGRAPHY, LEAVES, VARIETY