Digital Image Processing: Object Perimeter Measurement, Center Detection, and Minimum Bounding Rectangle Implementation

This program is designed for measuring object perimeter and center detection in digital image processing, along with drawing minimum bounding rectangles around identified objects. The implementation utilizes several core algorithms and techniques including edge detection (using operators like Sobel or Canny), thresholding methods (such as Otsu's algorithm for automatic threshold selection), morphological operations (erosion and dilation for noise removal and shape refinement), and image segmentation approaches. Through these techniques, the program accurately identifies and measures object shapes and dimensions, providing detailed information about detected objects. The implementation typically involves contour detection algorithms to calculate perimeter measurements and centroid calculations for center point detection. For the minimum bounding rectangle, the program may use rotating calipers algorithm or principal component analysis (PCA) to determine the optimal rectangle orientation. Additionally, the program outputs measurement results in both graphical and numerical formats, enabling users to better visualize and document the analysis outcomes. Common output formats include overlaying detected boundaries and rectangles on the original image, along with numerical data display of perimeter values and coordinate points. In summary, this program serves as an essential tool in digital image processing, providing robust support for scientific research and engineering applications where precise object measurement and characterization are required. The code structure typically follows a pipeline approach: preprocessing → segmentation → feature extraction → visualization, making it modular and adaptable for various image analysis tasks.