Applying Image Processing to Implement Optimization Approaches

Real-time plays a pivotal role in diverse scientific application, and optimizing the measurement process is essential to enhance efficiency. This study proposes the implementation of geometric methods to optimize real-time measurements through image processing. By evaluating the available space for a mobile robot to move, indirect measurement of the object as an obstacle in real time is achieved. This technique effectively reduces data transmission speed, a critical parameter for real-time measurements, with the potential to lower costs by necessitating smaller and simpler technical specifications for equipment. In order to enhance measurement accuracy, stereo cameras capturing images from various angles are employed simultaneously. However, this may introduce complexities in calculations, such as parallax errors. To address this issue, filters and restoration methods are applied, such as Gauss, to correct noise and accurately measure space. The study presents real-time results for a static object using the dynamic system, which demonstrated an accuracy exceeding 99%. By utilizing the same image for different calculations, the system complexity is minimized, also measurement time is reduced. While current optimization focuses on 2D, there is potential for extension in 3D. The methodologies presented in this study primarily involve analytical approaches such as Distance-Based Optimization and Multicriteria Decision Analytics. The study's findings and proposed technology pave the way for exciting future research and practical implementations, potentially revolutionizing real-time measurements in numerous scientific and technology domains.