In early work in the area, blob detection was used to obtain regions of interest for further processing.
In other domains, such as histogram analysis, blob descriptors can also be used for peak detection with application to segmentation.
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.
-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
Some basic properties of blobs defined from scale-space maxima of the normalized Laplacian operator are that the responses are covariant with translations, rotations and rescalings in the image domain.
This in practice highly useful property implies that besides the specific topic of Laplacian blob detection, local maxima/minima of the scale-normalized Laplacian are also used for scale selection in other contexts, such as in corner detection, scale-adaptive feature tracking (Bretzner and Lindeberg 1998), in the scale-invariant feature transform (Lowe 2004) as well as other image descriptors for image matching and object recognition.
The scale selection properties of the Laplacian operator and other closely scale-space interest point detectors are analyzed in detail in (Lindeberg 2013a).
[1] In (Lindeberg 2013b, 2015)[2][3] it is shown that there exist other scale-space interest point detectors, such as the determinant of the Hessian operator, that perform better than Laplacian operator or its difference-of-Gaussians approximation for image-based matching using local SIFT-like image descriptors.
can also be computed as the limit case of the difference between two Gaussian smoothed images (scale space representations) In the computer vision literature, this approach is referred to as the difference of Gaussians (DoG) approach.
In a similar fashion as for the Laplacian blob detector, blobs can be detected from scale-space extrema of differences of Gaussians—see (Lindeberg 2012, 2015)[3][4] for the explicit relation between the difference-of-Gaussian operator and the scale-normalized Laplacian operator.
are also defined from an operational differential geometric definitions that leads to blob descriptors that are covariant with translations, rotations and rescalings in the image domain.
In terms of scale selection, blobs defined from scale-space extrema of the determinant of the Hessian (DoH) also have slightly better scale selection properties under non-Euclidean affine transformations than the more commonly used Laplacian operator (Lindeberg 1994, 1998, 2015).
[3] In simplified form, the scale-normalized determinant of the Hessian computed from Haar wavelets is used as the basic interest point operator in the SURF descriptor (Bay et al. 2006) for image matching and object recognition.
A hybrid operator between the Laplacian and the determinant of the Hessian blob detectors has also been proposed, where spatial selection is done by the determinant of the Hessian and scale selection is performed with the scale-normalized Laplacian (Mikolajczyk and Schmid 2004): This operator has been used for image matching, object recognition as well as texture analysis.
The images that constitute the input to a computer vision system are, however, also subject to perspective distortions.
In practice, affine invariant interest points can be obtained by applying affine shape adaptation to a blob descriptor, where the shape of the smoothing kernel is iteratively warped to match the local image structure around the blob, or equivalently a local image patch is iteratively warped while the shape of the smoothing kernel remains rotationally symmetric (Lindeberg and Garding 1997; Baumberg 2000; Mikolajczyk and Schmid 2004, Lindeberg 2008).
will perfectly match the spatial extent and the temporal duration of the blob, with scale selection performed by detecting spatio-temporal scale-space extrema of the differential expression.
Moreover, by proceeding with the watershed analogy beyond the delimiting saddle point, a grey-level blob tree was defined to capture the nested topological structure of level sets in the intensity landscape, in a way that is invariant to affine deformations in the image domain and monotone intensity transformations.
By studying how these structures evolve with increasing scales, the notion of scale-space blobs was introduced.
While the specific technique that was used in these prototypes can be substantially improved with the current knowledge in computer vision, the overall general approach is still valid, for example in the way that local extrema over scales of the scale-normalized Laplacian operator are nowadays used for providing scale information to other visual processes.
For the purpose of detecting grey-level blobs (local extrema with extent) from a watershed analogy, Lindeberg developed an algorithm based on pre-sorting the pixels, alternatively connected regions having the same intensity, in decreasing order of the intensity values.
For example, by proceeding beyond the first delimiting saddle point a "grey-level blob tree" can be constructed.
This algorithm with its applications in computer vision is described in more detail in Lindeberg's thesis[7] as well as the monograph on scale-space theory[8] partially based on that work.
[9][10] More detailed treatments of applications of grey-level blob detection and the scale-space primal sketch to computer vision and medical image analysis are given in .
[11][12][13] Matas et al. (2002) were interested in defining image descriptors that are robust under perspective transformations.
The maximally stable extremal regions can be seen as making a specific subset of the grey-level blob tree explicit for further processing.