beeldfeatures

Beeldfeatures are measurable properties or patterns in images that can be used to describe and compare visual content. In computer vision, they are often used to represent image content in a compact form to enable tasks such as recognition, matching, retrieval, or 3D reconstruction. Beeldfeatures can be categorized as local features, which describe specific points or regions, or global features, which summarize the image as a whole.

Local beeldfeatures involve detecting keypoints or interest points and computing a descriptor that captures the appearance around each point. Well-known local feature families include SIFT (Scale-Invariant Feature Transform), SURF (Speeded-Up Robust Features), and ORB (Oriented FAST and Rotated BRIEF), along with binary descriptors such as BRIEF, FREAK, and AKAZE. These descriptors aim to be distinctive and robust to transformations such as scale, rotation, and illumination changes. Increasingly, learning-based approaches using neural networks are used to create powerful learned features.

Global beeldfeatures describe an entire image with a single representation, such as color histograms, GIST, or HOG (Histogram of Oriented Gradients). Global features are useful for image-level tasks like broad content classification or efficient indexing.

Processing typically involves detection of features, computation of descriptors, and matching or comparison using distance measures (for example, Euclidean distance for real-valued descriptors or Hamming distance for binary descriptors). Applications include image stitching and panorama creation, 3D reconstruction and structure-from-motion, simultaneous localization and mapping (SLAM), object recognition, and content-based image retrieval.

Limitations include computational cost and sensitivity to viewpoint changes, illumination, blur, and occlusion. While traditional beeldfeatures remain foundational, modern systems increasingly combine them with deep learning to improve robustness and performance.