A fast distance between histograms

Iberoamerican Congress on Pattern Recognition (CIARP), 2005, Havana (Cuba)In this paper we present a new method for comparing histograms. Its main advantage is that it takes less time than previous methods. The present distances between histograms are defined on a structure called signature, which is a lossless representation of histograms. Moreover, the type of the elements of the sets that the histograms represent are ordinal, nominal and modulo. We show that the computational cost of these distances is O(z′) for the ordinal and nominal types and O(z′2) for the modulo one, where z′ is the number of non-empty bins of the histograms. In the literature, the computational cost of the algorithms presented depends on the number of bins in the histograms. In most applications, the histograms are sparse, so considering only the non-empty bins dramatically reduces the time needed for comparison. The distances we present in this paper are experimentally validated on image retrieval and the positioning of mobile robots through image recognition.Peer Reviewe

Online learning the consensus of multiple correspondences between sets.

When several subjects solve the assignment problem of two sets, differences on the correspondences computed by these subjects may occur. These differences appear due to several factors. For example, one of the subjects may give more importance to some of the elements’ attributes than another subject. Another factor could be that the assignment problem is computed through a suboptimal algorithm and different non-optimal correspondences can appear. In this paper, we present a consensus methodology to deduct the consensus of several correspondences between two sets. Moreover, we also present an online learning algorithm to deduct some weights that gauge the impact of each initial correspondence on the consensus. In the experimental section, we show the evolution of these parameters together with the evolution of the consensus accuracy. We observe that there is a clear dependence of the learned weights with respect to the quality of the initial correspondences. Moreover, we also observe that in the first iterations of the learning algorithm, the consensus accuracy drastically increases and then stabilises

Correspondence consensus of two sets of correspondences through optimisation functions.

We present a consensus method which, given the two correspondences between sets of elements generated by separate entities, enounces a final correspondence consensus considering the existence of outliers. Our method is based on an optimisation technique that minimises the cost of the correspondence while forcing (to the most) to be the mean correspondence of the two original correspondences. The method decides the mapping of the elements that the original correspondences disagree and returns the same element mapping when both correspondences agree. We first show the validity of the method through an experiment in ideal conditions based on palmprint identification, and subsequently present two practical experiments based on image retrieval

Generalised median of graph correspondences.

A graph correspondence is defined as a function that maps the elements of two attributed graphs. Due to the increasing availability of methods to perform graph matching, numerous graph correspondences can be deducted for a pair of attributed graphs. To obtain a representative prototype for a set of data structures, the concept of the median has been largely employed, as it has proven to deliver a robust sample. Nonetheless, the calculation of the exact (or generalised) median is known to be an NP-complete problem for most domains. In this paper, we present a method based on an optimisation function to calculate the generalised median graph correspondence. This method makes use of the Correspondence Edit Distance, which is a metric that considers the attributes and the local structures of the graphs to obtain more interesting and meaningful results. Experimental validation shows that this approach is capable of obtaining the generalised median in a comparable runtime with respect to state-of-the-art methods on artificial data, while maintaining the success rate for a real-application case

Fast and efficient palmprint identification of a small sample within a full image.

In some fields like forensic research, experts demand that a found sample of an individual can be matched with its full counterpart contained in a database. The found sample may present several characteristics that make this matching more difficult to perform, such as distortion and, most importantly, a very small size. Several solutions have been presented intending to solve this problem, however, big computational effort is required or low recognition rate is obtained. In this paper, we present a fast, simple, and efficient method to relate a small sample of a partial palmprint to a full one using elemental optimization processes and a voting mechanic. Experimentation shows that our method performs with a higher recognition rate than the state of the art method, when trying to identify palmprint samples with a radius as small as 2.64 cm

Obtaining the consensus of multiple correspondences between graphs through online learning.

In structural pattern recognition, it is usual to compare a pair of objects through the generation of a correspondence between the elements of each of their local parts. To do so, one of the most natural ways to represent these objects is through attributed graphs. Several existing graph extraction methods could be implemented and thus, numerous graphs, which may not only differ in their nodes and edge structure but also in their attribute domains, could be created from the same object. Afterwards, a matching process is implemented to generate the correspondence between two attributed graphs, and depending on the selected graph matching method, a unique correspondence is generated from a given pair of attributed graphs. The combination of these factors leads to the possibility of a large quantity of correspondences between the two original objects. This paper presents a method that tackles this problem by considering multiple correspondences to conform a single one called a consensus correspondence, eliminating both the incongruences introduced by the graph extraction and the graph matching processes. Additionally, through the application of an online learning algorithm, it is possible to deduce some weights that influence on the generation of the consensus correspondence. This means that the algorithm automatically learns the quality of both the attribute domain and the correspondence for every initial correspondence proposal to be considered in the consensus, and defines a set of weights based on this quality. It is shown that the method automatically tends to assign larger values to high quality initial proposals, and therefore is capable to deduce better consensus correspondences

A discrete labelling approach to attributed graph matching using SIFT features

Trabajo presentado al ICPR 2010 celebrado en Estambul (Turquía) del 23 al 26 de agosto.Local invariant feature extraction methods are widely used for image-features matching. There exist a number of approaches aimed at the refinement of the matches between image-features. It is a common strategy among these approaches to use geometrical criteria to reject a subset of outliers. One limitation of the outlier rejection design is that it is unable to add new useful matches. We present a new model that integrates the local information of the SIFT descriptors along with global geometrical information to estimate a new robust set of feature-matches. Our approach encodes the geometrical information by means of graph structures while posing the estimation of the feature-matches as a graph matching problem. Some comparative experimental results are presented.This work was supported by projects: 'CONSOLIDER-INGENIO 2010 Multimodal interaction in pattern recognition and computer vision' (V-00069), 'Robotica ubicua para entornos urbanos' (J-01225).Peer Reviewe

Semi-automatic pose estimation of a fleet of robots with embedded stereoscopic cameras.

Given a fleet of robots, automatic estimation of the relative poses between them could be inaccurate in specific environments. We propose a framework composed by the fleet of robots with embedded stereoscopic cameras providing 2D and 3D images of the scene, a human coordinator and a Human-Machine Interface. We suppose auto localising each robot through GPS or landmarks is not possible. 3D-images are used to automatically align them and deduce the relative position between robots. 2Dimages are used to reduce the alignment error in an interactive manner. A human visualises both 2D-images and the current automatic alignment, and imposes a new alignment through the Human-Machine Interface. Since the information is shared through the whole fleet, robots can deduce the position of other ones that do not visualise the same scene. Practical evaluation shows that in situations where there is a large difference between images, the interactive processes are crucial to achieve an acceptable result

Oriented matroids for shape representation and indexing

Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), Port d'Andratx (Spain)In this paper a novel method for indexing views of 3D objects is presented. The topological properties of the regions of the segmented images of the objects are used to define an index based on oriented matroid theory. Oriented matroids, which are projective invariants, encode incidence relations and relative position of the elements of the image and give local and global topological information about their spatial distribution. This indexing technique is applied to 3D object hypothesis generation from single views to reduce the number of candidates in object recognition processes.Peer Reviewe

Oriented matroids for shape representation and indexing

Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), Port d'Andratx (Spain)In this paper a novel method for indexing views of 3D objects is presented. The topological properties of the regions of the segmented images of the objects are used to define an index based on oriented matroid theory. Oriented matroids, which are projective invariants, encode incidence relations and relative position of the elements of the image and give local and global topological information about their spatial distribution. This indexing technique is applied to 3D object hypothesis generation from single views to reduce the number of candidates in object recognition processes.Peer Reviewe