Image transformations ##################### What is a binarization ? ************************ What is a convolution ? *********************** No, filtering operations such as mean filtering are not considered morphological operations. They are part of a different category of image processing techniques known as spatial filtering or convolutional filtering. Filtering Operations ******************** Purpose ======= Filtering operations like mean filtering (or averaging) are used to smooth images, reduce noise, and sometimes enhance specific features such as edges. How It Works ============ Mean filtering involves convolving an image with a filter kernel (e.g., a 3x3 matrix where each value is 1/9). This operation computes the average of the pixel values within the kernel's neighborhood and assigns this average to the central pixel. Common Types of Filters ======================= - **Mean Filter**: Reduces noise by averaging the pixel values in the neighborhood. - **Gaussian Filter**: Applies a Gaussian function for weighted averaging, often used for blurring. - **Median Filter**: Replaces the central pixel with the median of the neighborhood values, effective at reducing salt-and-pepper noise. Morphological Operations ************************ Purpose ======= Morphological operations are used to process the shape or structure of objects within an image, such as removing noise, separating objects, filling gaps, and enhancing object boundaries. How It Works ============ These operations rely on the interaction between an image and a structuring element (kernel). The operations focus on modifying the geometry of objects based on the shape and size of the structuring element. Common Morphological Operations =============================== - **Erosion**: Removes pixels on object boundaries, shrinking the objects. - **Dilation**: Adds pixels to object boundaries, expanding the objects. - **Opening**: Erosion followed by dilation, used to remove small objects/noise. - **Closing**: Dilation followed by erosion, used to close small holes/gaps. Effect ====== Alters the shape of objects in the image without averaging pixel values; instead, it manipulates the binary or grayscale structure of the image. Other ressource *************** You can find demonstration of different image filters using convolution on the website `geekosophers.com `_.