GBT Tokenize Is Developing Real Time Object Detection Algorithms And Techniques For Kirlian Research
GBT Technologies Inc., together with GBT Tokenize Corp (“GBT/Tokenize”) is developing real time algorithms and techniques for Kirlian research. A Kirilian image includes vast amount of graphical data. GBT/Tokenize research is aiming to develop a system and method to analyze this data as a potential of health-related information source. These algorithms and methods are pattern detection and recognition, based on unique principles. An image’s objects are categorized according to their physical characteristics like shape, color, texture, and more. A machine learning based flow is targeted to operate as an object’s classifier and analytics processor. We believe that the object detection and analytics method can be used for processing Kirlian images for their characteristics as another stage of GBT/Tokenize’s Kirlian Electrophotography research. Kirlian photography method introduces a series of techniques that are based on the phenomenon known as electrical coronal discharge. This technique produces an object’s energy related images with a colorful representation called aura. When performed on human organs, although not scientifically proven, some believe that these images can be interpreted to analyze health conditions. GBT/Tokenize is researching the development of imaging related techniques to further investigate the data generated from these images for possible health related conclusions. The research is not medical but technical and targeted to conclude a real time analysis of Kirilian images that may be related to human’s health conditions.
The goal of our research is to enable a machine learning algorithm to decide as to whether an image’s object is of interest or not, pointing a possible health related conclusion. A Kirlian image contains a huge amount of data. The detection and analytics of a Kirlian image requires major computational capabilities in a real time operation. A neural network analysis is done to ensure a reliable object’s classification and to train for the detection of objects of interest within a Kirlian image. The approach performs an image based color-based pre-processing, to reach a conclusion about certain pattern and color presented in the image. The goal is to reach a real-time Kirilian image processing with the use of deep learning algorithms and supporting computational hardware resources, achieving advanced imaging conclusions that may provide health related information.
Since a Kirlian image includes a vast amount of information, a private, custom real-time algorithms is planned to be developed. This approach is planned to utilize the analysis of an image object’s neighboring positions, utilizing this data to increase the overall detection speed. The method is contemplated to process a computation of the original objects and their neighboring information to formulate an efficient flow that can be repeatedly executed, achieving a true real-time processing. Finally, advanced algorithmic and hardware related architectures will exploit parallel processing to accelerate the complex computations operations. Data parallelism can be achieved using a CPU that performs programmatic instruction, which is efficient to accelerate large amount of data processing. The company targets this development to achieve a high speed, real time Kirilian imaging processing that may be of use for health-related advice.
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“As we continue with the Kirlian Imaging research, we encountered the need for a fast processing of a Kirlian image. A Kirlian image contains a huge amount of data to be analyzed, especially if we are interested in a real time processing. An image that is produced by Kirlian technique includes a colorful, object’s energy representation, called Aura. We are aiming to analyze this Aura, using computational geometry algorithms and neural network algorithms. Each image’s Aura is targeted to be analyzed according to its color, size, shape and pattern. This type of processing may take a long time even with advanced computational geometry approaches. Due to the vast amount of image’s data, we are in the need to develop advanced pattern recognition algorithms that will enable real-time results. We are researching a new detection and analysis approach to categorize each image’s Aura characteristics and features. The method will analyze geometrical objects, their relation to their neighbored objects and to the overall image. Each object will be processed through a module, which we call a classifier to catalog it within similar set of objects. It is our goal to have a neural network-based algorithm performing a parallel processing per objects group to achieve rapid, real-time results. In order to further enhance this processing, we will consider parallel processing of the data by software and supporting hardware, i.e. CPUs, GPUs. We are going to invest significant efforts to develop real-time object detection and pattern recognition methods to reach our goal, which is a speed lightning, real time analysis. This stage of the research is aimed to assist with providing the data on-the-fly, possibly advising into health-related information can be identify underline conditions and symptoms” stated Danny Rittman, the Company’s CTO.
There is no guarantee that the Company will be successful in researching, developing or implementing this technology. In order to successfully implement this technology, the Company will need to raise adequate capital to support its research and, if successfully researched, developed and granted regulatory approval, the Company would need to enter into a strategic relationship with a third party that has experience in manufacturing, selling and distributing this product. There is no guarantee that the Company will be successful in any or all of these critical steps.