Michael Donoser

Efficient Maximally Stable Extremal Region (MSER) Tracking

This paper introduces a tracking method for the well known local MSER (Maximally Stable Extremal Region) detector. The component tree is used as an efficient data structure, which allows the calculation of MSERs in quasi-linear time. It is demonstrated that the tree is able to manage the required data for tracking. We show that by means of MSER tracking the computational time for the detection of single MSERs can be improved by a factor of 4 to 10. Using a weighted feature vector for data association improves the tracking stability. Furthermore, the component tree enables backward tracking which further improves the robustness. The novel MSER tracking algorithm is evaluated on a variety of scenes. In addition, we demonstrate three different applications, tracking of license plates, faces and fibers in paper, showing in all three scenarios improved speed and stability.

Saliency driven total variation segmentation

This paper introduces an unsupervised color segmentation method. The underlying idea is to segment the input image several times, each time focussing on a different salient part of the image and to subsequently merge all obtained results into one composite segmentation. We identify salient parts of the image by applying affinity propagation clustering to efficiently calculated local color and texture models. Each salient region then serves as an independent initialization for a figure/ground segmentation. Segmentation is done by minimizing a convex energy functional based on weighted total variation leading to a global optimal solution. Each salient region provides an accurate figure/ ground segmentation highlighting different parts of the image. These highly redundant results are combined into one composite segmentation by analyzing local segmentation certainty. Our formulation is quite general, and other salient region detection algorithms in combination with any semi-supervised figure/ground segmentation approach can be used. We demonstrate the high quality of our method on the well-known Berkeley segmentation database. Furthermore we show that our method can be used to provide good spatial support for recognition frameworks.

Beyond pairwise shape similarity analysis

This paper considers two major applications of shape matching algorithms: (a) query-by-example, i e retrieving the most similar shapes from a database and (b) finding clusters of shapes, each represented by a single prototype Our approach goes beyond pairwise shape similarity analysis by considering the underlying structure of the shape manifold, which is estimated from the shape similarity scores between all the shapes within a database We propose a modified mutual kNN graph as the underlying representation and demonstrate its performance for the task of shape retrieval We further describe an efficient, unsupervised clustering method which uses the modified mutual kNN graph for initialization Experimental evaluation proves the applicability of our method, e g by achieving the highest ever reported retrieval score of 93.40% on the well known MPEG-7 database.

Diffusion Processes for Retrieval Revisited

In this paper we revisit diffusion processes on affinity graphs for capturing the intrinsic manifold structure defined by pair wise affinity matrices. Such diffusion processes have already proved the ability to significantly improve subsequent applications like retrieval. We give a thorough overview of the state-of-the-art in this field and discuss obvious similarities and differences. Based on our observations, we are then able to derive a generic framework for diffusion processes in the scope of retrieval applications, where the related work represents specific instances of our generic formulation. We evaluate our framework on several retrieval tasks and are able to derive algorithms that e.\, g.~achieve a 100\% bulls eye score on the popular MPEG7 shape retrieval data set.

Irregular lattices for complex shape grammar facade parsing

High-quality urban reconstruction requires more than multi-view reconstruction and local optimization. The structure of facades depends on the general layout, which has to be optimized globally. Shape grammars are an established method to express hierarchical spatial relationships, and are therefore suited as representing constraints for semantic facade interpretation. Usually inference uses numerical approximations, or hard-coded grammar schemes. Existing methods inspired by classical grammar parsing are not applicable on real-world images due to their prohibitively high complexity. This work provides feasible generic facade reconstruction by combining low-level classifiers with mid-level object detectors to infer an irregular lattice. The irregular lattice preserves the logical structure of the facade while reducing the search space to a manageable size. We introduce a novel method for handling symmetry and repetition within the generic grammar. We show competitive results on two datasets, namely the Paris 2010 and the Graz 50. The former includes only Hausmannian, while the latter includes Classicism, Biedermeier, Historicism, Art Nouveau and post-modern architectural styles.

Using partial edge contour matches for efficient object category localization

We propose a method for object category localization by partially matching edge contours to a single shape prototype of the category. Previous work in this area either relies on piecewise contour approximations, requires meaningful supervised decompositions, or matches coarse shape-based descriptions at local interest points. Our method avoids error-prone pre-processing steps by using all obtained edges in a partial contour matching setting. The matched fragments are efficiently summarized and aggregated to form location hypotheses. The efficiency and accuracy of our edge fragment based voting step yields high quality hypotheses in low computation time. The experimental evaluation achieves excellent performance in the hypotheses voting stage and yields competitive results on challenging datasets like ETHZ and INRIA horses.

Efficient partial shape matching of outer contours

This paper introduces a novel efficient partial shape matching method named IS-Match. We use sampled points from the silhouette as a shape representation. The sampled points can be ordered which in turn allows to formulate the matching step as an order-preserving assignment problem. We propose an angle descriptor between shape chords combining the advantages of global and local shape description. An efficient integral image based implementation of the matching step is introduced which allows detecting partial matches an order of magnitude faster than comparable methods. We further show how the proposed algorithm is used to calculate a global optimal Pareto frontier to define a partial similarity measure between shapes. Shape retrieval experiments on standard shape databases like MPEG-7 prove that state-of-the-art results are achieved at reduced computational costs.

Detecting, tracking and recognizing license plates

This paper introduces a novel real-time framework which enables detection, tracking and recognition of license plates from video sequences. An efficient algorithm based on analysis of Maximally Stable Extremal Region (MSER) detection results allows localization of international license plates in single images without the need of any learning scheme. After a one-time detection of a plate it is robustly tracked through the sequence by applying a modified version of the MSER tracking framework which provides accurate localization results and additionally segmentations of the individual characters. Therefore, tracking and character segmentation is handled simultaneously. Finally, support vector machines are used to recognize the characters on the plate. An experimental evaluation shows the high accuracy and efficiency of the detection and tracking algorithm. Furthermore, promising results on a challenging data set are presented and the significant improvement of the recognition rate due to the robust tracking scheme is proved.

3D Segmentation by Maximally Stable Volumes (MSVs)

This paper introduces an efficient 3D segmentation concept, which is based on extending the well-known maximally stable extremal region (MSER) detector to the third dimension. The extension allows the detection of stable 3D regions, which we call the maximally stable volumes (MSVs). We present a very efficient way to detect the MSVs in quasi-linear time by analysis of the component tree. Two applications - 3D segmentation within simulated MR brain images and analysis of the 3D fiber network within digitized paper samples

Real time appearance based hand tracking

show that reasonably good segmentation results are achieved with low computational effort