We present a method for tracking Maximally Stable Homogeneous Regions (MSHR) in images with an arbitrary number of channels. MSHR are conceptionally very similar to Maximally Stable Extremal Regions (MSER) and Maximally Stable Color Regions (MSCR), but can also be applied to hyperspectral and color images while remaining extremely efficient. The presented approach makes use of the edge-based co...

Interest point approaches that extract local features from images are commonly used in human action recognition field. In this paper, a comparison study is performed in which different interest point approaches are used. Each approach is discussed with its advantages and drawbacks. Common keypoints extractor like scale invariant features transform (SIFT), speeded up robust features (SURF), etc....

This work aim one’s effort is to estimate tentative correspondence in a wide baseline image pairs with a new method called True Tentative Correspondences (TTC). This method use Maximally Stable Extremal Regions (MSER) as features which were put into correspondence. Input to our algorithm is a widebaseline image pair and output is a set where one element consist of eight tentative correspondence...

A novel distinguished region detector, complementary to existing approaches like Harris-corner detectors, Difference of Gaussian detectors (DoG) or Maximally Stable Extremal Regions (MSER) is proposed. The basic idea is to find distinguished regions by clusters of interest points. In order to determine the number of clusters we use the concept of maximal stableness across scale. Therefore, the ...

We present an efficient method for detecting planar bilateral symmetries under perspective projection. The method uses local affine frames (LAFs) constructed on maximally stable extremal regions or any other affine covariant regions detected in the image to dramatically improve the process of detecting symmetric objects under perspective distortion. In contrast to the previous work no Hough tra...

Finding correspondence regions between images is fundamental to recovering three dimensional information from multiple frames of the same scene and content based image retrieval. To be good, correspondence regions should be easily found, richly characterised and have a good trade-off between density and uniqueness. Maximally stable extremal regions (MSER’s) are amongst the best known methods to...

Methods based on distinguished regions (transformation covariant detectable regions) have achieved considerable success in object recognition, retrieval and matching problems in both still images and videos. The chapter focuses on a method exploiting local coordinate systems (local affine frames) established on maximally stable extremal regions. We provide a taxonomy of affine-covariant constru...

In this paper we propose a novel interest operator robust to photometric and geometric transformations. Our operator is closely related to the grayscale MSER but it works on the HSV color space, as opposed to the most popular operators in the literature, which are intensity based. It combines a fine and a coarse overlapped quantization of the HSV color space to find maximally stable extremal re...

Text localization from scene images is a challenging task that finds application in many areas. In this work, we propose a novel hybrid text localization approach that exploits Multi-resolution Maximally Stable Extremal Regions to discard false-positive detections from the text confidence maps generated by a Fast Feature Pyramid based sliding window classifier. The use of a multi-scale approach...