Detect MSER features and return
regions = detectMSERFeatures(I)example
regions = detectMSERFeatures(I,Name,Value)
Code Generation Support:
Supports MATLAB Function block: No
For code generation, the function outputs
regions.PixelList as an array.
The region sizes are defined in
Generated code for this function uses a precompiled platform-specific shared library.
Code Generation Support, Usage Notes, and Limitations
Read image and detect MSER regions.
I = imread('cameraman.tif'); regions = detectMSERFeatures(I);
Visualize MSER regions which are described by pixel lists stored inside the returned 'regions' object.
figure; imshow(I); hold on; plot(regions, 'showPixelList', true, 'showEllipses', false);
Display ellipses and centroids fit into the regions.
figure; imshow(I); hold on; plot(regions); % by default, plot displays ellipses and centroids
I— Input imageM-by-N 2-D grayscale image
Input image, specified in grayscale. It must be real and nonsparse.
Specify optional comma-separated pairs of
Name is the argument
Value is the corresponding
Name must appear
inside single quotes (
You can specify several name and value pair
arguments in any order as
[30 14000], specifies the size of the region in pixels.
'ThresholdDelta'— Step size between intensity threshold levels
2(default) | percent numeric value
Step size between intensity threshold levels, specified as
the comma-separated pair consisting of '
and a numeric value in the range (0,100]. This value is expressed
as a percentage of the input data type range used in selecting extremal
regions while testing for their stability. Decrease this value to
return more regions. Typical values range from 0.8 to 4.
'RegionAreaRange'— Size of the region
[30 14000](default) | two-element vector
Size of the region in pixels, specified as the comma-separated
pair consisting of '
RegionAreaRange' and a two-element
vector. The vector, [minArea maxArea],
allows the selection of regions containing pixels to be between minArea and maxArea,
'MaxAreaVariation'— Maximum area variation between extremal regions
0.25(default) | positive scalar
Maximum area variation between extremal regions at varying intensity
thresholds, specified as the comma-separated pair consisting of '
and a positive scalar value. Increasing this value returns a greater
number of regions, but they may be less stable. Stable regions are
very similar in size over varying intensity thresholds. Typical values
range from 0.1 to 1.0.
'ROI'— Rectangular region of interest[1 1
I,1)] (default) | vector
Rectangular region of interest, specified as a vector. The vector
must be in the format [x y width height].
When you specify an
ROI, the function detects
corners within the area located at [x y]
of size specified by [width height]
. The [x y] elements specify
the upper left corner of the region.
The MSER detector incrementally steps through the intensity
range of the input image to detect stable regions. The
determines the number of increments the detector tests for stability.
You can think of the threshold delta value as the size of a cup to
fill a bucket with water. The smaller the cup, the more number of
increments it takes to fill up the bucket. The bucket can be thought
of as the intensity profile of the region.
object checks the variation of the region area size between different
intensity thresholds. The variation must be less than the value of
to be considered stable.
At a high level, MSER can be explained, by thinking of the intensity profile of an image representing a series of buckets. Imagine the tops of the buckets flush with the ground, and a hose turned on at one of the buckets. As the water fills into the bucket, it overflows and the next bucket starts filling up. Smaller regions of water join and become bigger bodies of water, and finally the whole area gets filled. As water is filling up into a bucket, it is checked against the MSER stability criterion. Regions appear, grow and merge at different intensity thresholds.
 Nister, D., and H. Stewenius, "Linear Time Maximally Stable Extremal Regions", Lecture Notes in Computer Science. 10th European Conference on Computer Vision, Marseille, France: 2008, no. 5303, pp. 183–196.
 Matas, J., O. Chum, M. Urba, and T. Pajdla. "Robust wide baseline stereo from maximally stable extremal regions." Proceedings of British Machine Vision Conference, pages 384-396, 2002.
 Obdrzalek D., S. Basovnik, L. Mach, and A. Mikulik. "Detecting Scene Elements Using Maximally Stable Colour Regions," Communications in Computer and Information Science, La Ferte-Bernard, France; 2009, vol. 82 CCIS (2010 12 01), pp 107–115.
 Mikolajczyk, K., T. Tuytelaars, C. Schmid, A. Zisserman, T. Kadir, and L. Van Gool, "A Comparison of Affine Region Detectors"; International Journal of Computer Vision, Volume 65, Numbers 1–2 / November, 2005, pp 43–72 .