boundarydetection

Boundary detection is the process of identifying the boundaries between regions or objects within data, particularly in images. It seeks to locate transitions in attributes such as intensity, color, or texture that indicate a boundary. In computer vision, boundary detection is closely related to edge detection but often aims to delineate complete object contours and may integrate contextual information and prior knowledge.

Techniques for boundary detection range from classical low-level methods to modern learning-based approaches. Classical methods compute gradient or second-derivative responses (such as Sobel, Canny, or Laplacian operators) and apply steps like non-maximum suppression and hysteresis to produce boundaries. Region-based and contour-based methods, including active contours (snakes) and level set methods, attempt to link boundary fragments into closed contours. Graph-based methods and watershed segmentation treat boundaries as cuts or region boundaries within a data graph. In recent years, supervised and self-supervised learning trains neural networks to predict boundary probability maps or distance transforms, with post-processing to produce clean contours. Applications span image segmentation, object recognition, medical image analysis, and autonomous driving.

Evaluation of boundary detection commonly uses boundary-specific metrics that compare detected boundaries to ground truth, such as precision, recall, and F-measure computed with a tolerance for localization. Datasets like BSDS500 provide standardized benchmarks and metrics such as optimal boundary F-score (ODS) and best per-image score (OIS). Challenges include cluttered textures, thin or fragmented boundaries, occlusion, and varying illumination, as well as integrating boundary information across scales, modalities, and tasks.