Understanding how particle size evolves over time in aging materials is critical across industries ranging from drug development, aerospace composites, and infrastructure engineering. Traditional static imaging techniques often fall short when it comes to capturing real time changes in particle morphology due to thermal cycling, oxidation, and shear forces. Dynamic image analysis offers a powerful solution by continuously capturing and processing visual data to track size variations with high temporal and spatial resolution. This approach leverages precision video capture, controlled spectral lighting, and deep learning classifiers to monitor individual particles as they undergo transformations during aging processes. Unlike conventional methods that rely on discrete measurements and post-hoc computational evaluation, dynamic image analysis enables real time feedback, allowing researchers to observe coalescence, fragmentation, precipitation, or solvation as they occur. The system typically operates within controlled environmental chambers where temperature, humidity, or atmospheric composition can be precisely regulated to simulate aging conditions. Each frame captured by the camera is processed using contour extraction and threshold-based isolation to isolate particles from the background, followed by automated measurement of key parameters such as sphericity index, length-to-width ratio, and total exposed surface. Over time, these measurements are compiled into temporal profiles uncovering non-linear degradation behaviors. Machine learning models are then trained to classify different types of particle behavior—such as coalescence versus fragmentation—based on historical data and known material properties. This not only increases accuracy but also reduces human bias in data interpretation. Validation is achieved through cross referencing with other analytical techniques like laser diffraction or electron microscopy, ensuring that the dynamic measurements correlate with established benchmarks. One of the most compelling applications of this technology is in the study of structural ceramics, where long-term hydration shifts porosity and grain morphology. By compressing years of aging into time-scaled degradation simulations, dynamic image analysis provides practical data for lifespan prediction and failure mitigation. Similarly, in drug powder stability analysis, monitoring API crystallization or amorphous conversion during shelf life, helps predict drug efficacy and dissolution rate. The scalability of dynamic image analysis also makes it suitable for industrial quality control, where inline monitoring can detect deviations early and prevent batch failures. As computational power increases and algorithms become more sophisticated, the ability to analyze complex, multi particle systems in three dimensions is becoming feasible. Future developments may integrate this technology with AI-driven simulation platforms that evolve in tandem with observed microstructural changes. Ultimately, dynamic image analysis transforms static reporting into dynamic insight, 粒子径測定 giving scientists and engineers the tools to anticipate and control how materials change over time. This capability is not merely an improvement in measurement—it is a paradigm shift in how we study aging at the microscale.