Feature Extraction for Three-Category Inspection Images

In this document, we will discuss various aspects of three-category inspection images. First, we will introduce image feature extraction methods, a crucial step for extracting meaningful information from images. This typically involves algorithms like Histogram of Oriented Gradients (HOG) or Local Binary Patterns (LBP) implemented through functions such as cv2.HOGDescriptor() in OpenCV. Next, we will examine image preprocessing procedures, which prepare images for subsequent analysis and processing using techniques like Gaussian blurring (cv2.GaussianBlur()) and histogram equalization (cv2.equalizeHist()). We will then explore shape feature extraction methods from target image regions based on regional markers, employing contour detection (cv2.findContours()) and region property analysis (regionprops() in MATLAB) to identify and describe specific objects or areas. Finally, we will discuss how to calculate relevant feature parameters, utilizing mathematical formulas and statistical methods to quantify the importance and relevance of image features, such as calculating aspect ratios, circularity, and moment invariants. Through detailed discussion and explanation of these key concepts, we can better understand the processing workflow and methodologies for three-category inspection images.