<![CDATA[Digital image processing is a branch of computer science that plays a significant role in automating object identification processes. This study presents the implementation of the K-Means Clustering algorithm for detecting the shape and material of drinking glasses based on digital images. The research methodology involves several stages, including image data collection, color space conversion from RGB to Lab, image segmentation using K-Means Clustering, and feature extraction of shape and texture. The K-Means algorithm is employed to cluster image pixels into multiple groups according to color similarity and texture patterns, thereby enabling the classification of glasses based on their material (glass, plastic, or clay) and shape. The experimental results demonstrate that the proposed method achieves a high level of accuracy in object identification and can be effectively implemented within a Matlab-based system. Consequently, this approach offers a potential solution for the automation of drinking container identification in various industrial and research applications.]]>
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