Description: Zernike Moments are a set of orthogonal moments used in image analysis, particularly in shape recognition and classification. These moments are invariant to rotation, scale, and translation, making them powerful tools for feature extraction in images. Their formulation is based on polynomial functions that allow for compact and efficient representation of image information. Being orthogonal, Zernike Moments minimize information redundancy, thus facilitating the analysis and comparison of different shapes. This property makes them especially useful in applications where image variability can be a challenge, such as in computer vision and medical image processing. In summary, Zernike Moments are fundamental for the mathematical representation of shapes in images, providing a solid foundation for the development of recognition and classification algorithms.
History: Zernike Moments were introduced by Dutch physicist Frits Zernike in 1934. Zernike developed these moments as part of his work in optics and image analysis, seeking a way to mathematically describe light patterns in images. His work was initially applied in the field of optics, but over time, Zernike Moments found applications in various areas, including computer vision and image processing. As technology advanced, their use expanded in the 1990s with the growth of computing and digital image analysis, becoming a standard tool in shape recognition.
Uses: Zernike Moments are used in various computer vision applications, including character recognition, object identification, and shape classification. Their ability to be invariant to rotation, scale, and translation makes them ideal for applications where images may vary in orientation and size. They are also used in medical image analysis, such as in tumor detection or cell classification, where shape and structure are crucial for diagnosis. Additionally, they are applied in various industries for pattern recognition and identification.
Examples: A practical example of the use of Zernike Moments is in handwritten character recognition, where they are used to identify letters and numbers in scanned documents. Another example is their application in shape classification in medical images, such as identifying different types of cells in a blood analysis. They have also been used in computer vision systems for object detection in images, helping to identify various relevant elements.
