Abstract:

With an emphasis on hierarchical picture and edge detection, this research provides a critical analysis of ontologies within the framework of higher order function support for optimum learning. The formal representation of knowledge known as ontology is crucial in the structuring and organization of data across many fields. Picture edge detection is crucial for many computer vision tasks, including object identification, scene analysis, and picture segmentation. The purpose of this research is to learn how ontologies may be used to improve the efficiency of higher-order functions, with a focus on hierarchical picture and edge recognition methods. Our goal is to improve these algorithms' precision, productivity, and sturdiness by incorporating ontology into their training processes. The potential of ontology to aid in the transfer of information across different learning challenges and domains is also investigated. For these goals, we perform a systematic literature assessment of ontologydriven higher order functions in the context of image and edge detection to discover current techniques.

Description:

Hierarchical Model, Conditional Random Field, Semantic Image Segmentation, EDGE Detection, optimum learning