Calculation of zernike moments from binary image BW

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Calculation of zernike moments from binary image BW

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In this document, we will discuss the process of calculating Zernike moments from a binary image, specifically referred to as BW. Zernike moments are a set of orthogonal moments that are widely used in image processing and pattern recognition. These moments provide valuable information about the shape and structure of the image, allowing for various analysis and feature extraction techniques.

To calculate Zernike moments from a binary image, we need to follow a specific procedure. First, we need to convert the binary image into a normalized polar coordinate system. This transformation allows us to represent the image in terms of radial and angular coordinates, which are essential for Zernike moment calculations.

Once we have the image in the polar coordinate system, we can proceed with the actual calculation of the Zernike moments. This involves defining a set of basis functions, known as Zernike polynomials, that are used to represent the image. These polynomials are orthogonal within the unit disk and form a complete set, allowing us to accurately describe the image.

Each Zernike moment represents a specific characteristic of the image, such as its symmetry, rotation, or shape. By calculating these moments, we can extract valuable features that can be used for various applications, including object recognition, image classification, and image matching.

In conclusion, the calculation of Zernike moments from a binary image is an important technique in image processing. By following the necessary steps and utilizing the proper mathematical tools, we can obtain valuable information about an image's shape and structure. This information can be utilized in various applications, making Zernike moments a powerful tool in the field of computer vision.