In computer vision and image processing, blob detection methods are aimed at detecting regions in a digital image 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 convolution.

Given some property of interest expressed as a function of position on the image, there are two main classes of blob detectors: (i) differential methods, which are based on derivatives of the function with respect to position, and (ii) methods based on local extrema, 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 interest point detection and corner detection).

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 edge detectors or corner detectors. 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 object recognition and/or object tracking. In other domains, such as histogram analysis, blob descriptors can also be used for peak detection with application to segmentation. Another common use of blob descriptors is as main primitives for texture analysis and texture recognition. In more recent work, blob descriptors have found increasingly popular use as interest points for wide baseline stereo matching 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 ridge detection to signal the presence of elongated objects.

One of the first and also most common blob detectors is based on the Laplacian of the Gaussian (LoG). Given an input image ${\displaystyle f(x,y)}$, this image is convolved by a Gaussian kernel

at a certain scale ${\displaystyle t}$ to give a scale space representation ${\displaystyle L(x,y;t)\ =g(x,y,t)*f(x,y)}$. Then, the result of applying the Laplacian operator

is computed, which usually results in strong positive responses for dark blobs of radius ${\textstyle r^{2}=2t}$ (for a two-dimensional image, ${\textstyle r^{2}=dt}$ for a ${\textstyle d}$-dimensional image) and strong negative responses for bright blobs of similar size. A main problem when applying this operator at a single scale, however, is that the operator response is strongly dependent on the relationship between the size of the blob structures in the image domain and the size of the Gaussian kernel used for pre-smoothing. In order to automatically capture blobs of different (unknown) size in the image domain, a multi-scale approach is therefore necessary.

A straightforward way to obtain a multi-scale blob detector with automatic scale selection is to consider the scale-normalized Laplacian operator

and to detect scale-space maxima/minima, that are points that are simultaneously local maxima/minima of ${\displaystyle \nabla _{\mathrm {norm} }^{2}L}$ with respect to both space and scale (Lindeberg 1994, 1998). Thus, given a discrete two-dimensional input image ${\displaystyle f(x,y)}$ a three-dimensional discrete scale-space volume ${\displaystyle L(x,y,t)}$ is computed and a point is regarded as a bright (dark) blob if the value at this point is greater (smaller) than the value in all its 26 neighbours. Thus, simultaneous selection of interest points ${\displaystyle ({\hat {x}},{\hat {y}})}$ and scales ${\displaystyle {\hat {t}}}$ is performed according to