Haralickdeskriptorer

Haralick descriptors, also known as Haralick texture features, are a set of statistical measures used in image analysis and computer vision to quantify the texture of an image. These descriptors were introduced by Robert Haralick in 1973 and are derived from the co-occurrence matrix, a statistical tool that captures the spatial relationships between pixels in an image.

The co-occurrence matrix is constructed by counting the number of times each pair of pixel intensities occurs in a given spatial relationship, such as horizontally, vertically, or diagonally. From this matrix, a set of 14 statistical features can be computed, each providing a different measure of the texture characteristics of the image. These features include measures of energy, entropy, contrast, homogeneity, and correlation, among others.

Haralick descriptors have been widely used in various applications, including medical imaging, remote sensing, and object recognition. They provide a quantitative way to describe the texture of an image, which can be useful for tasks such as image segmentation, classification, and retrieval. However, the effectiveness of Haralick descriptors can be sensitive to the choice of parameters, such as the distance and angle used to construct the co-occurrence matrix, and the size of the image region over which the features are computed. Therefore, careful consideration of these parameters is necessary to achieve optimal performance in a given application.