Detect MSER features and return MSERRegions object
regions = detectMSERFeatures(I) returns an MSERRegions object, regions, containing information about MSER features detected in the 2-D grayscale input image, I. This object uses Maximally Stable Extremal Regions (MSER) algorithm to find regions.
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 regions.Lengths.
Generated code for this function uses a precompiled platform-specific shared library.
Code Generation Support, Usage Notes, and Limitations
I = imread('cameraman.tif'); regions = detectMSERFeatures(I);
figure; imshow(I); hold on; plot(regions, 'showPixelList', true, 'showEllipses', false);
figure; imshow(I); hold on; plot(regions); % by default, plot displays ellipses and centroids
Input image, specified in grayscale. It must be real and nonsparse.
Data Types: uint8 | int16 | uint16 | single | double
Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.Example: 'RegionAreaRange',[30 14000], specifies the size of the region in pixels.
Step size between intensity threshold levels, specified as the comma-separated pair consisting of 'ThresholdDelta' 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.
Maximum area variation between extremal regions at varying intensity thresholds, specified as the comma-separated pair consisting of 'MaxAreaVariation' 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.
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 ThresholdDelta parameter 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.
The MSER object checks the variation of the region area size between different intensity thresholds. The variation must be less than the value of the MaxAreaVariation parameter 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 .