$GTCH GBT Tokenize is Developing Advanced Computational Geometry Methods
"Kirlian imaging of living tissue may exhibit energy levels that can be visualized as auras. We are now developing a set of geometrical computation methods and algorithms to graphically analyze these images further feeding the data into our machine learning programs. The target is to reach consistent and reliable conclusions with respect to the analysis of the image and any anomalies. Computational geometry is a field of computer science that is aimed to solve problems stated in terms of geometry. Here we are implementing these techniques for Kirilian images to find unique patterns. The development of these geometrical algorithms is part of our image processing long term plan and will be further used during next year for AI imaging analytics. For example, we develop a specific version of Scanline method which is an algorithm that typically works on a row-by-row basis rather pixel-by-pixel basis. We created a new flow to identify critical vertexes only and then analyze pixel-by-pixel. This enables light speed image analysis in order to search and classify image's points of interest. The main advantage of our version is that the sorting of crucial vertices only, is done in parallel with the normal scan, significantly shortening the overall scan time and reducing objects-of-interest detection time. We develop plane sweep based algorithms that works via a conceptual sweep line to analyze Kirilian images as an infrastructure for our AI program. The main idea is to scan across the image, stopping at object of interest vertexes (points) to identify patterns and consistencies. These geometric operations will become geometric objects that will intersect with others to form a human organ aura. By looking for full and partial similarities, repetitions, or atypical auras patterns, our goal is to be able to detect dynamic images changes in time. In addition, another set of computational geometry algorithms will search object's boundaries in order to detect specific patterns and finding images correlations. The geometrical computation engine will be the first step to extract the base data from the Kirilian images and later processed by the AI engine."
"Kirlian imaging of living tissue may exhibit energy levels that can be visualized as auras. We are now developing a set of geometrical computation methods and algorithms to graphically analyze these images further feeding the data into our machine learning programs. The target is to reach consistent and reliable conclusions with respect to the analysis of the image and any anomalies. Computational geometry is a field of computer science that is aimed to solve problems stated in terms of geometry. Here we are implementing these techniques for Kirilian images to find unique patterns. The development of these geometrical algorithms is part of our image processing long term plan and will be further used during next year for AI imaging analytics. For example, we develop a specific version of Scanline method which is an algorithm that typically works on a row-by-row basis rather pixel-by-pixel basis. We created a new flow to identify critical vertexes only and then analyze pixel-by-pixel. This enables light speed image analysis in order to search and classify image's points of interest. The main advantage of our version is that the sorting of crucial vertices only, is done in parallel with the normal scan, significantly shortening the overall scan time and reducing objects-of-interest detection time. We develop plane sweep based algorithms that works via a conceptual sweep line to analyze Kirilian images as an infrastructure for our AI program. The main idea is to scan across the image, stopping at object of interest vertexes (points) to identify patterns and consistencies. These geometric operations will become geometric objects that will intersect with others to form a human organ aura. By looking for full and partial similarities, repetitions, or atypical auras patterns, our goal is to be able to detect dynamic images changes in time. In addition, another set of computational geometry algorithms will search object's boundaries in order to detect specific patterns and finding images correlations. The geometrical computation engine will be the first step to extract the base data from the Kirilian images and later processed by the AI engine."
