detectMultiScale

detectMultiScale is a function commonly found in computer vision libraries, notably OpenCV, used for object detection. Its primary purpose is to scan an image at multiple scales to locate objects of a specific type, such as faces, eyes, or cars. The function works by applying a classifier, often a pre-trained Haar Cascade or Local Binary Pattern (LBP) based classifier, to different regions of the input image.

The "multi-scale" aspect is crucial. The classifier is applied not only to the original image but also to scaled versions of the image. This allows the detection of objects regardless of their size within the image. For instance, a face classifier might be applied to the image at its original size, then to a smaller version, and then to a larger version. This process ensures that both large and small instances of the target object can be identified.

Key parameters for detectMultiScale typically include the image itself, the scale factor, and minimum and maximum neighbor counts. The scale factor determines how much the image is reduced at each step of the scaling process. The neighbor parameters help in filtering out false positives by requiring that detected objects have a certain number of overlapping detections in their vicinity. The function returns a list of rectangles, where each rectangle represents the bounding box of a detected object.