A Literature Review: Geometric Methods and Their Applications in Human-Related Analysis

Abstract

Geometric features, such as the topological and manifold properties, are utilized to extract geometric properties. Geometric methods that exploit the applications of geometrics, e.g., geometric features, are widely used in computer graphics and computer vision problems. This review presents a literature review on geometric concepts, geometric methods, and their applications in human-related analysis, e.g., human shape analysis, human pose analysis, and human action analysis. This review proposes to categorize geometric methods based on the scope of the geometric properties that are extracted: object-oriented geometric methods, feature-oriented geometric methods, and routine-based geometric methods. Considering the broad applications of deep learning methods, this review also studies geometric deep learning, which has recently become a popular topic of research. Validation datasets are collected, and method performances are collected and compared. Finally, research trends and possible research topics are discussed.

Using applicable theorems for solving circle geometry problems: A correlational study of senior high school students’ academic performance in circle geometry

“…Data Collection Procedure: Before starting the data collection activities, it was considered crucial and essential to obtain the permission and consent of the school administration and the participants because it was important to follow the professional guidelines and codes of conduct that govern interactions with participants, as stated by (Denzin & Lincoln, 2005). As stated in the opinion of (Lubbe, 2003), the study was able to acquire the participants' trust since they were aware that their responses would not be utilized for personal bene t or gain.…”