Building a Mean Model for Background and Background Subtraction for Image Segmentation

In this context, we can utilize background mean modeling and background subtraction techniques for image segmentation. Background mean modeling is a method that calculates a background model based on pixel values in the image. By comparing pixel values in the image with the background model, we can separate foreground objects from the background. This typically involves computing the average or median of pixel values over a sequence of frames to create a stable background representation. Background subtraction is another widely used image segmentation approach that extracts foreground objects by subtracting the background model from the current frame. This operation can be implemented using absolute difference calculations between the current frame and the reference background model. Through the combination of these two methods, we can achieve more accurate image segmentation and effectively extract target objects of interest. The implementation often involves thresholding operations to distinguish foreground pixels from background noise, followed by morphological operations to refine the segmentation results.