Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

Abstract

GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on Tensor for shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, n-D Tensor Calculus can be considered a Language to reliably compute optimized n-Dimensional Tensor Invariants as specific object invariant parameter and attribute words for automated shape/texture optimal synthetic object description by incremental model generation. The class of those invariant parameter and attribute words can be thought as a specific Vocabulary learned from a Generalized Formal Dictionary of the Computational Tensor Invariants language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous, similar approaches are: 1) Progressive Automated Invariant Model Generation, 2) Invariant Minimal Complete Description Set for computational efficiency, 3) Arbitrary Model Precision for robust object description and identification.