MATLAB Image Measurement Course Experiments

In the MATLAB environment, we can conduct experimental programs for image measurement courses. These include implementing image edge detection using smoothed operators, applying spatial neighborhood averaging techniques, performing image binarization and thinning operations, and executing image enhancement (sharpening) procedures. The smoothed operator implementation typically involves convolution with specific kernel matrices to detect gradient changes in pixel intensity. Spatial neighborhood averaging utilizes functions like imfilter() with averaging kernels to reduce noise while preserving edges. For image binarization, the imbinarize() function with appropriate thresholding algorithms (such as Otsu's method) converts grayscale images to binary format, while thinning operations employ morphological operations like bwmorph() with 'thin' parameters. Image sharpening experiments often use Laplacian filters or unsharp masking techniques through fspecial() and imfilter() combinations. Additionally, we can perform image processing using median filtering, which effectively removes salt-and-pepper noise while preserving edges through the medfilt2() function. These experimental programs help us gain deeper understanding of image measurement principles and methods, thereby enhancing our practical implementation skills.