👉 Monitors, particularly in the context of computer vision and image processing, are mathematical models that aim to capture and describe the hierarchical features present in images. These models typically use convolutional neural networks (CNNs) to learn a series of filters or kernels that detect various patterns and structures at different scales and locations within an image. Each filter in a CNN is designed to respond to specific features, such as edges, textures, and shapes, by sliding over the image and performing dot products between the filter weights and the corresponding pixel values. This process, known as convolution, allows the network to extract rich, multi-level representations of the input image, which can then be used for tasks like object detection, image classification, and segmentation. The hierarchical nature of these features enables monitors to effectively capture complex visual information, making them powerful tools in the field of machine learning and computer vision.
