Spatial feature extraction involves identifying geometric or positional characteristics of objects in images or videos. Traditional methods use techniques like edge detection (e.g., Sobel or Canny) and feature descriptors (e.g., SIFT, SURF) to extract key points and their spatial relationships.
Deep learning models, especially convolutional neural networks (CNNs), automate spatial feature extraction by learning hierarchical patterns from raw data. Initial layers capture simple features like edges, while deeper layers detect complex structures like shapes or textures.
These spatial features are used in tasks like object detection, scene recognition, and 3D reconstruction, forming the basis for many computer vision applications.