 Maximally stable extremal regions

Feature detection Output of a typical corner detection algorithmEdge detection Canny · CannyDeriche · Differential · Sobel · Prewitt · Roberts Cross Interest point detection Corner detection Harris operator · Shi and Tomasi · Level curve curvature · SUSAN · FAST Blob detection Laplacian of Gaussian (LoG) · Difference of Gaussians (DoG) · Determinant of Hessian (DoH) · Maximally stable extremal regions · PCBR Ridge detection Hough transform Structure tensor Affine invariant feature detection Affine shape adaptation · Harris affine · Hessian affine Feature description SIFT · SURF · GLOH · HOG · LESH Scalespace Scalespace axioms · Implementation details · Pyramids In computer vision, maximally stable extremal regions (MSER) are used as a method of blob detection in images. This technique was proposed by Matas et al.^{[1]} to find correspondences between image elements from two images with different viewpoints. This method of extracting a comprehensive number of corresponding image elements contributes to the widebaseline matching, and it has led to better stereo matching and object recognition algorithms.
Contents
Terms and Definitions
Image I is a mapping . Extremal regions are well defined on images if:
 S is totally ordered (reflexive, antisymmetric and transitive binary relations exist).
 An adjacency relation is defined.
Region Q is a contiguous subset of D. (For each there is a sequence p,a_{1},a_{2},..,a_{n},q and pAa_{1},a_{i}Aa_{i + 1},a_{n}Aq.
(Outer) Region Boundary , which means the boundary of Q is the set of pixels adjacent to at least one pixel of Q but not belonging to Q.
Extremal Region is a region such that either for all (maximum intensity region) or for all (minimum intensity region).
Maximally Stable Extremal Region Let Q_{1},..,Q_{i − 1},Q_{i},... be a sequence of nested extremal regions (). Extremal region Q_{i *} is maximally stable if and only if has a local minimum at i * . (Here denotes cardinality.) is a parameter of the method.
The concept more simply can be explained by thresholding. All the pixels below a given threshold are 'black' and all those above or equal are 'white'. If we are shown a sequence of thresholded images I_{t} with frame t corresponding to threshold t, we would see first a white image, then 'black' spots corresponding to local intensity minima will appear then grow larger. These 'black' spots will eventually merge, until the whole image is black. The set of all connected components in the sequence is the set of all extremal regions. In that sense, the concept of MSER is linked to the one of component tree of the image.^{[2]} The component tree indeed provide an easy way for implementing MSER.^{[3]}
Extremal regions
Extremal regions in this context have two important properties, that the set is closed under...
 continuous (and thus projective) transformation of image coordinates. This means it is affine invariant and it doesn't matter if the image is warped or skewed.
 monotonic transformation of image intensities. The approach is of course sensitive to natural lighting effects as change of day light or moving shadows.
Advantages of MSER
Because the regions are defined exclusively by the intensity function in the region and the outer border, this leads to many key characteristics of the regions which make them useful. Over a large range of thresholds, the local binarization is stable in certain regions, and have the properties listed below.
 Invariance to affine transformation of image intensities
 Covariance to adjacency preserving (continuous)transformation on the image domain
 Stability: only regions whose support is nearly the same over a range of thresholds is selected.
 Multiscale detection without any smoothing involved, both fine and large structure is detected.
Note however that detection of MSERs in a scale pyramid improves repeatability, and number of correspondences across scale changes.^{[4]}  The set of all extremal regions can be enumerated in worstcase O(n), where n is the number of pixels in the image.^{[5]}
Comparison to other region detectors
In Mikolajczyk et al.,^{[6]} six region detectors are studied (Harrisaffine, Hessianaffine, MSER, edgebased regions, intensity extrema, and salient regions). A summary of MSER performance in comparison to the other five follows.
 Region density  in comparison to the others MSER offers the most variety detecting about 2600 regions for a textured blur scene and 230 for a light changed. scene, and variety is generally considered to be good. Also MSER had a repeatability of 92% for this test.
 Region size  MSER tended to detect many small regions, versus large regions which are more likely to be occluded or to not cover a planar part of the scene. Though large regions may be slightly easier to match.
 Viewpoint change  MSER outperforms the five other region detectors in both the original images and those with repeated texture motifs.
 Scale change  Following Hessianaffine detector, MSER comes in second under a scale change and inplane rotation.
 Blur  MSER proved to be the most sensitive to this type of change in image, which is the only area that this type of detection is lacking in.
Note however that this evaluation did not make use of multiresolution detection, which has been shown to improve repeatability under blur.^{[4]}  Light change  MSER showed the highest repeatability score for this type of scene, with all the other having good robustness as well.
MSER consistently resulted in the highest score through many tests, proving it to be a reliable region detector.^{[6]}
Implementation
The original algorithm of Matas et al.^{[1]} is in the number of pixels, which is almost linear. It proceeds by first sorting the pixels by intensity. This would take time, using BINSORT. After sorting, pixels are marked in the image, and the list of growing and merging connected components and their areas is maintained using the unionfind algorithm. This would take time. In practice these steps are very fast. During this process, the area of each connected component as a function of intensity is stored producing a data structure. A merge of two components is viewed as termination of existence of the smaller component and an insertion of all pixels of the smaller component into the larger one. In the extremal regions, the 'maximally stable' ones are those corresponding to thresholds where the relative area change as a function of relative change of threshold is at a local minimum, i.e. the MSER are the parts of the image where local binarization is stable over a large range of thresholds.^{[1]}^{[6]}
The component tree is the set of all connected components of the thresholds of the image, ordered by inclusion. Efficient (quasilinear whatever the range of the weights) algorithms for computing it do exist.^{[2]} Thus this structure offers an easy way for implementing MSER.^{[3]}
More recently, Nister and Stewenius have proposed a truly (if the weight are small integers) worstcase method in,^{[5]} which is also much faster in practice. This algorithm is similar to the one of Ph. Salembier et al. ^{[7]}.
Robust widebaseline algorithm
The purpose of this algorithm is to match MSERs to establish correspondence points between images. First MSER regions are computed on the intensity image (MSER+) and on the inverted image (MSER). Measurement regions are selected at multiple scales: the size of the actual region, 1.5x, 2x, and 3x scaled convex hull of the region. Matching is accomplished in a robust manner, so it is better to increase the distinctiveness of large regions without being severely affected by clutter or nonplanarity of the region's preimage. A measurement taken from an almost planar patch of the scene with stable invariant description are called a 'good measurement'. Unstable ones or those on nonplanar surfaces or discontinuities are called 'corrupted measurements'. The robust similarity is computed: For each on region A,k regions from the other image with the corresponding ith measurement nearest to are found and a vote is cast suggesting correspondence of A and each of . Votes are summed over all measurements, and using probability analysis, we pick out the 'good measurements' as the 'corrupt measurements' will likely spread their votes randomly. By applying RANSAC to the centers of gravity of the regions, we can compute a rough epipolar geometry. An affine transformation between pairs of potentially corresponding regions is computed, and correspondences define it up to a rotation, which is then determined by epipolar lines. The regions are then filtered, and the ones with correlation of their transformed images above a threshold are chosen. RANSAC is applied again with a more narrow threshold, and the final eipolar geometry is estimated by the eightpoint algorithm.
This algorithm can be tested here (Epipolar or homography geometry constrained matches): [WBS Image Matcher [1]
Extensions and Adaptations
 The MSER algorithm has been adapted to colour images, by replacing thresholding of the intensity function with agglomerative clustering, based on colour gradients.^{[8]}
 The MSER algorithm can be used to track colour objects, by performing MSER detection on the Mahalanobis distance to a colour distribution.^{[3]}
 By detecting MSERs in multiple resolutions, robustness to blur, and scale change can be improved.^{[4]}
Other Applications
 Shape Descriptors for Maximally Stable Extremal Regions
 Efficient Maximally Stable Extremal Region (MSER) Tracking
 Ntree DisjointSet Forests for Maximally Stable Extremal Regions
 Video Google and Object Level Grouping for Video Shots
 RealTime Extraction of Maximally Stable Extremal Regions on an FPGA
 Maximally Stable Colour Regions for Recognition and Matching
See also
External links
 VLFeat, an open source computer vision library in C (with a MEX interface to MATLAB), including an implementation of MSER
 OpenCV, an open source computer vision library in C/C++, including an implementation of Linear Time MSER
 Detector Repeatabilty Study, Kristian Mikolajczyk Binaries (Win/Linux to compute MSER/HarrisAffine... . Binary used in his repeatability study.
References
 ^ ^{a} ^{b} ^{c} J. Matas, O. Chum, M. Urba, and T. Pajdla. "Robust wide baseline stereo from maximally stable extremal regions." Proc. of British Machine Vision Conference, pages 384396, 2002.
 ^ ^{a} ^{b} L. Najman and M. Couprie: "Building the component tree in quasilinear time"; IEEE Transaction on Image Processing, Volume 15, Numbers 11 , 2006, pp 35313539
 ^ ^{a} ^{b} ^{c} Donoser, M. and Bischof, H. Efficient Maximally Stable Extremal Region (MSER) Tracking CVPR, 2006.
 ^ ^{a} ^{b} ^{c} Forssen, PE. and Lowe, D.G. "Shape Descriptors for Maximally Stable Extremal Regions" ICCV, 2007.
 ^ ^{a} ^{b} Nister, D. and Stewenius, H., "Linear Time Maximally Stable Extremal Regions", ECCV, 2008.
 ^ ^{a} ^{b} ^{c} K. Mikolajczyk, 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 12 / November, 2005, pp 4372
 ^ Salembier, Philippe; A. Oliveras and L. Garrido (1998). "Antiextensive Connected Operators for Image and Sequence Processing". IEEE Transactions on Image Processing 7 (4): 555570. http://imatge.upc.edu/pub/ps/IEEE_IP98_Salembier_Oliveras_Garrido.ps.gz.
 ^ Forssen, PE. Maximally Stable Colour Regions for Recognition and Matching, CVPR, 2007.
Categories:
Wikimedia Foundation. 2010.
Look at other dictionaries:
Blob detection — Feature detection Output of a typical corner detection algorithm … Wikipedia
Harris affine region detector — In the fields of computer vision and image analysis, the Harris affine region detector belongs to the category of feature detection. Feature detection is a preprocessing step of several algorithms that rely on identifying characteristic points or … Wikipedia
Hessian Affine region detector — The Hessian Affine region detector is a feature detector used in the fields of computer vision and image analysis. Like other feature detectors, the Hessian Affine detector is typically used as a preprocessing step to algorithms that rely on… … Wikipedia
Détection de zones d'intérêt — En vision par ordinateur et en traitement d images, la détection de zones d intérêt d une image numérique (feature detection en anglais) consiste à mettre en évidence des zones de cette image jugées « intéressantes » pour l analyse, c… … Wikipédia en Français
Kadir Brady saliency detector — The Kadir Brady saliency detector is designed to detect representative region in images. It performs well in the context of object class recognition. It was invented by http://www.robots.ox.ac.uk/ timork/ Timor Kadir] and [http://www.robots.ox.ac … Wikipedia
Scaleinvariant feature transform — Feature detection Output of a typical corner detection algorithm … Wikipedia
Corner detection — Feature detection Output of a typical corner detection algorithm … Wikipedia
Object recognition (computer vision) — Feature detection Output of a typical corner detection algorithm … Wikipedia