This chapter introduces fundamental techniques for extracting and recognizing objects in binary images based on geometric and statistical features. Key shape descriptors—including area, perimeter, circularity, and centroid—are presented along with methods for contour tracing and region labeling to identify connected components. These feature parameters enable the differentiation of objects such as elongated shapes, round regions, and irregular forms. The chapter further demonstrates how feature-based selection can isolate target objects, such as bananas in a mixed-fruit image, and how small connected components can be removed as noise through area filtering. Together, these methods provide a practical foundation for automatic object recognition and feature-driven image analysis.