Mahotas

Step 1: Load microscopy image using mahotas.imread().

Step 2: Apply a Gaussian filter to reduce noise.

Step 3: Use Otsu's thresholding to create a binary mask of cells.

Step 4: Apply watershed segmentation to separate touching cells based on the binary mask and gradient information.

Step 1: Load satellite image using mahotas.imread().

Step 2: Convert the image to grayscale.

Step 3: Extract Haralick texture features to characterize different land cover types.

Step 4: Classify land cover types based on the extracted texture features using a machine learning algorithm.

Step 1: Acquire an image of the manufactured part.

Step 2: Apply image preprocessing steps (e.g., normalization, noise reduction).

Step 3: Use edge detection (Sobel) to identify potential defects.

Step 4: Calculate shape features (e.g., area, eccentricity) to classify defects.

Step 1: Load an iris image.

Step 2: Preprocess the image to normalize illumination and contrast.

Step 3: Extract Local Binary Patterns (LBP) from the iris region.

Step 4: Use the extracted LBP features for iris recognition.

Step 1: Load a microscopy image of the material's microstructure.

Step 2: Apply morphological operations (e.g., dilation, erosion) to enhance features.

Step 3: Segment different phases or grains in the microstructure.

Step 4: Measure the size and shape of the grains to characterize the material's properties.