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 ...

Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution extracted keypoints play an essential role reliability accuracy image orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study effect applying various information content criteria to keypoint select...

The equalisation topic is well researched and a variety of solutions are available. The MAP sequence detector provides the lowest symbol error rate (SER) attainable, and the MLSE offers a near optimal solution. However, these optimal techniques are not yet practical for high-level modulation schemes, due to their computational complexity. Linear equaliser or linear-combiner DFE are practical sc...

Motivation A new interest region operator and feature descriptor called Center-Surround Distribution Distance (CSDD) is based on comparing feature distributions between a central foreground region and a surrounding ring of background pixels. In addition to finding light(dark) blobs surrounded by a dark(light) background, CSDD also detects blobs with arbitrary color distribution that “stand out”...

Accurate image correspondence is crucial for estimating multiple-view geometry. In this paper, we present a registration-based method for improving accuracy of the image correspondences. We apply the method to fundamental matrix estimation under practical situations where there are both erroneous matches (outliers) and small feature location errors. Our registration-based method can correct fea...

This paper describes Southampton’s submissions to the 2009 ImageCLEF photo annotation task. For the task we used an annotation system based on the idea of constructing semantic spaces, which was developed previously at Southampton. To represent the image content, we used a combination of different SIFT and Colour-SIFT features detected using the difference-of-Gaussian and MSER techniques. These...

For the instance search task, we are given a set of query images with the corresponding textual meta-data and objects masks to retrieve video shots containing query objects from FLICKR video database. We extract meaningful regions in the key-frames using Maximally Stable Extremal Regions (MSER) and use SIFT descriptors for representation. We use standard Bag of visual Word (BoW) model to repres...

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...

Region feature detectors and descriptors have become a successful and popular alternative to point descriptors in image analysis due to their high robustness and repeatability, leading to a significant interest in the shape analysis community in finding analogous approaches in the 3D world. Recent works have successfully extended the maximally stable extremal region (MSER) detection algorithm t...

For learning an object representation a huge amount of labeled data is needed. To minimize the labeling effort this paper proposes a new approach for learning from unlabeled data. The main idea is to combine a tracker and a learning method by directly feeding the learning algorithm with patches obtained by the tracker. In particular we apply an MSER based tracker and batch PCA for learning. But...