Contour Metrics for Portable Object Identification
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Solution Overview
Problem
Current image processing technologies fail to transform identified objects into portable, numerical representations that are independent of the original data source, lacking methods to utilize patterns as small as 1 pixel for object identification, group them across different dimensions, and create contour metrics for learning contour identification systems.
Innovation Solution
The system transforms data into contour maps and their individual contours into contour metrics, using self-learning algorithms to identify and manage patterns, storing them as manifold representation codes that can be used for pattern recognition and identification across different systems and dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current image detection algorithms are used to identify objects within data formats, then object identification is achieved, but the objects remain tied to the original source and cannot be ported to other applications for further processing
Solution Approach 1:
The patent extracts identified objects from their original image data and transforms them into standalone numerical representations (contour metrics). This extraction allows objects to be separated from the source data, enabling portability to other applications while maintaining their essential characteristics through mathematical descriptors.
Solution Approach 2:
The patent transforms visual image data into numerical parameters (contour metrics including area, perimeter, circularity, and other geometric descriptors). This parameter transformation converts qualitative visual information into quantitative data that can be independently processed and ported across different systems without dependence on the original image source.
2Productivity
If current technology processes image data to identify objects, then identification is achieved, but computational processes must remain with the original source
Solution Approach 1:
The patent extracts computational processing from the original image data environment by creating independent numerical representations. This allows subsequent analysis to occur in simplified numerical space rather than requiring complex image processing pipelines, improving computational efficiency while reducing system complexity.
Solution Approach 2:
The patent replaces complex image processing mechanical systems with mathematical computations on numerical metrics. By substituting visual pattern recognition with numerical metric analysis, the system achieves higher computational efficiency and lower complexity in subsequent processing stages.
3Measurement precision
If pattern recognition systems use original image data, then accurate identification is achieved, but the systems cannot transform objects into independent numerical representations
Solution Approach 1:
The patent maintains measurement precision by using multiple geometric descriptors (area, perimeter, circularity, aspect ratio, and other contour metrics) to represent objects. These numerical parameters preserve the essential characteristics needed for accurate identification while enabling format independence and portability across different data systems.
Solution Approach 2:
The patent creates a composite numerical representation by combining multiple contour metrics and geometric descriptors into a unified object model. This composite approach maintains the richness of original image information while presenting it in an independent, format-agnostic numerical structure that enables versatile processing.
Data Source
AI summary
A system and method that transforms data formats into contour metrics and further transforms each contour of that mapping into contours pattern metric sets so that each metric created has a representation of one level of contour presentation, at each iteration of the learning contour identification system defined herein. This transformation of data instance to contour metrics permits a user to take relevant data of a data set, as determined by a learning contour identification system, to machines of other types and function, for the purpose of further analysis of the patterns found and labeled by said system. The invention performs with data format representations, not limited to, signals, images, or waveform embodiments so as to identify, track, or detect patterns of, amplitudes, frequencies, phases, and density functions, within the data case and then by way of using combinations of statistical, feedback adaptive, classification, training algorithm metrics stored in hardware, identifies patterns in past data cases that repeat in future, or present data cases, so that high-percentage labeling and identification is a achieved.