This chapter provides a detailed exploration of segmentation one of the most essential processes in computer vision—dividing an image into meaningful regions to facilitate tasks such as object detection, recognition, and scene understanding. It begins by defining segmentation as the classification of pixels into homogeneous regions based on attributes like intensity, color, or texture, distinguishing objects from the background. The chapter first introduces threshold-based segmentation, explaining how pixels are divided according to intensity values and discussing both ideal cases with well-separated distributions and real cases with overlapping ones. Techniques such as median filtering are presented to improve segmentation quality by reducing noise.