Extracting Texture Image Features

To extract texture image features, we can consider using the following methods: mean value, standard deviation, smoothness, third moment, uniformity, and entropy. These features help us comprehensively understand image texture information, playing crucial roles in image processing and analysis. The mean value indicates the overall brightness level of an image, which can be calculated using functions like mean() in MATLAB or np.mean() in Python by averaging all pixel values. Standard deviation measures the degree of variation in pixel values, typically computed through std() function implementation that quantifies data dispersion. Smoothness describes the image's texture refinement level, often derived from standard deviation calculations using (1 - 1/(1 + σ²)) formula implementation. The third moment reveals image skewness characteristics, implementable via moment calculation algorithms that detect histogram asymmetry. Uniformity evaluates image consistency degree, commonly calculated through sum(p(z)²) where p(z) represents probability distribution function. Entropy assesses image complexity, implemented using -sum(p(z)*log2(p(z))) formula that measures information content. By extracting these texture features through appropriate coding implementations, we can better understand and characterize images, providing enhanced information and possibilities for subsequent image processing tasks.