Blob detection

In <a href="/facts/Computer_vision/Tl2Yyk66">computer vision</a> and <a href="/facts/Image_processing/zenalNCf">image processing</a>, blob detection methods are aimed at detecting regions in a <a href="/facts/Digital_image/3TNT47Mv">digital image</a> that differ in properties, such as brightness or color, compared to surrounding regions. Informally, a blob is a region of an image in which some properties are constant or approximately constant; all the points in a blob can be considered in some sense to be similar to each other. The most common method for blob detection is by using <a href="/facts/Convolution/PVPrdz9J">convolution</a>.
Given some property of interest expressed as a function of position on the image, there are two main classes of blob detectors: (i) <a href="/facts/Differential_calculus/uU1AAc4N">differential</a> methods, which are based on derivatives of the function with respect to position, and (ii) methods based on local <a href="/facts/Maxima_and_minima/ADlgnyzV">extrema</a>, which are based on finding the local maxima and minima of the function. With the more recent terminology used in the field, these detectors can also be referred to as interest point operators, or alternatively interest region operators (see also <a href="/facts/Interest_point_detection/GdfWRp5U">interest point detection</a> and <a href="/facts/Corner_detection/19pSbRDe">corner detection</a>).
There are several motivations for studying and developing blob detectors. One main reason is to provide complementary information about regions, which is not obtained from <a href="/facts/Edge_detection/9VplpIcS">edge detectors</a> or <a href="/facts/Corner_detection/19pSbRDe">corner detectors</a>. In early work in the area, blob detection was used to obtain regions of interest for further processing. These regions could signal the presence of objects or parts of objects in the image domain with application to <a href="/facts/Object_recognition/cdLpak1I">object recognition</a> and/or object <a href="/facts/Video_tracking/nVh8FB3E">tracking</a>. In other domains, such as <a href="/facts/Image_histogram/lfDPwpS2">histogram</a> analysis, blob descriptors can also be used for peak detection with application to <a href="/facts/Segmentation_(image_processing)/jofAhbxa">segmentation</a>. Another common use of blob descriptors is as main primitives for <a href="/facts/Texture_(computer_graphics)/9gDTsuXN">texture</a> analysis and texture recognition. In more recent work, blob descriptors have found increasingly popular use as <a href="/facts/Interest_point_detection/GdfWRp5U">interest points</a> for wide baseline <a href="/facts/Image_registration/y2KUwdRL">stereo matching</a> and to signal the presence of informative image features for appearance-based object recognition based on local image statistics. There is also the related notion of <a href="/facts/Ridge_detection/I695LrAS">ridge detection</a> to signal the presence of elongated objects.

Blob detection open-in-new

Blob detection