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Purpose: To reduce image noise and detail. Basic Principle: It uses a Gaussian kernel to smooth the image by averaging the pixels based on their spatial closeness and intensity similarity.

Purpose: To reduce noise in images while preserving edges. Basic Principle: It combines the domain filter and the range filter to preserve sharp edges by including a Gaussian filter in the space domain and a Gaussian filter function in the range domain of the intensity-value differences.

Purpose: To create binary images from grayscale images. Basic Principle: It assigns pixel values based on a threshold; pixels above the threshold are set to one value, those below to another.

Purpose: To segment the image into clusters. Basic Principle: It partitions the image into K clusters where each pixel belongs to the cluster with the nearest mean intensity value.

Purpose: To determine the motion of objects in a visual scene. Basic Principle: It estimates the motion of objects between two consecutive frames taken at times $t$ and $t+dt$ based on the visual motion observed.

Purpose: To reduce the dimensionality of image data. Basic Principle: PCA transforms the data to a new coordinate system where the greatest variance by any projection of the data comes to lie on the first coordinate.

Purpose: To detect lines, circles, and other shapes in images. Basic Principle: It is a feature extraction technique that uses a voting scheme to determine the presence of a shape in a pixel.

Purpose: To identify the points in an image where the image brightness changes sharply. Basic Principle: It uses mathematical methods to find points of discontinuity in brightness.

Purpose: To improve the contrast in an image. Basic Principle: It spreads out the most frequent intensity values over the entire range.

Purpose: To reduce the number of colors in an image. Basic Principle: It reduces the image file size by limiting the number of colors, which is beneficial for web graphics.

Purpose: To detect connected regions within a binary image. Basic Principle: It scans an image and groups its pixels into components based on pixel connectivity, then labels the components.

Purpose: To detect and describe local features in images. Basic Principle: It extracts distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene.

Purpose: To segment images into different regions. Basic Principle: It considers the gradient magnitude of an image as a topographic surface and finds the lines that separate different basins.

Purpose: To analyze and process image structures. Basic Principle: It works on the shape and structure within an image using operations like dilation and erosion.