Electronic Letters on Computer Vision and Image Analysis (ELCVIA - Universitat Autònoma de Barcelona)
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Algebraic Topology-Based Image Deformation: A Unified Model
Get PDFIn this paper, a new method for image deformation is presented. It is based upon decomposition of the deformation problem into basic physical laws. Unlike other methods that solve a differential or an energetic formulation of the physical laws involved, we encode the basic laws using computational algebraic topology. Conservative laws are translated into exact global values and constitutive laws are judiciously approximated. In order to illustrate the effectiveness of our model, we deal with both small- and large-scale deformation, utilizing elasticity theory and the viscous fluid model, respectively. The proposed approach is validated through a series of tests on optical flow estimation and image registration
Combining Particle Filter and Population-based Metaheuristics for Visual Articulated Motion Tracking
Get PDFVisual tracking of articulated motion is a complex task with high computational costs. Because of the fact that articulated objects are usually represented as a set of linked limbs, tracking is performed with the support of a model. Model-based tracking allows determining object pose in an effortless way and handling occlusions. However, the use of articulated models generates a multidimensional state-space and, therefore, the tracking becomes computationally very expensive or even infeasible. Due to the dynamic nature of the problem, some sequential estimation algorithms like particle filters are usually applied to visual tracking. Unfortunately, particle filter fails in high dimensional estimation problems such as articulated objects or multiple object tracking. These problems are called \emph{dynamic optimization problems}. Metaheuristics, which are high level general strategies for designing heuristics procedures, have emerged for solving many real world combinatorial problems as a way to efficiently and effectively exploring the problem search space. Path relinking (PR) and scatter search (SS) are evolutionary metaheuristics successfully applied to several hard optimization problems. PRPF and SSPF algorithms respectively hybridize both, particle filter and these two population-based metaheuristic schemes. In this paper, We present and compare two different hybrid algorithms called Path Relinking Particle Filter (PRPF) and Scatter Search Particle Filter (SSPF), applied to 2D human motion tracking. Experimental results show that the proposed algorithms increase the performance of standard particle filters
Trajectory Analysis for Sport and Video Surveillance
Get PDFIn video surveillance and sports analysis applications, trajectories offer the possibility of extracting rich information on the underlying behavior of the moving targets. To this end we introduce an extension of Point Distribution Models (PDM) to analyze trajectories in their spatial, temporal and spatiotemporal dimensions. These trajectory models represent trajectories as an average trajectory and a set of deformation modes, in the spatial, temporal and spatiotemporal domains. Thus any given trajectory can be expressed in terms of its modes, which in turn can be ascribed to a particular behavior. The proposed analysis tool has been tested on trajectory data extracted from a vision system that was tracking radio-guided cars running inside a circuit. This affords an easier interpretation of results, because the shortest lap provides a reference behavior. Besides showing an actual analysis we discuss how to normalize trajectories to have a meaningful analysis
Ear Biometrics Based on Geometrical Feature Extraction
Get PDFBiometrics identification methods proved to be very efficient, more natural and easy for users than traditional methods of human identification. In fact, only biometrics methods truly identify humans, not keys and cards they posses or passwords they should remember. The future of biometrics will surely lead to systems based on image analysis as the data acquisition is very simple and requires only cameras, scanners or sensors. More importantly such methods could be passive, which means that the user does not have to take active part in the whole process or, in fact, would not even know that the process of identification takes place. There are many possible data sources for human identification systems, but the physiological biometrics seem to have many advantages over methods based on human behaviour. The most interesting human anatomical parts for such passive, physiological biometrics systems based on images acquired from cameras are face and ear. Both of those methods contain large volume of unique features that allow to distinctively identify many users and will be surely implemented into efficient biometrics systems for many applications. The article introduces to ear biometrics and presents its advantages over face biometrics in passive human identification systems. Then the geometrical method of feature extraction from human ear images in order to perform human identification is presented
A K Nearest Classifier design
Get PDFThis paper presents a multi-classifier system design controlled by the topology of the learning data. Our work also introduces a training algorithm for an incremental self-organizing map (SOM). This SOM is used to distribute classification tasks to a set of classifiers. Thus, the useful classifiers are activated when new data arrives. Comparative results are given for synthetic problems, for an image segmentation problem from the UCI repository and for a handwritten digit recognition problem
Frequent Graph Discovery: Application to Line Drawing Document Images
Get PDFIn this paper a sequence of steps is applied to a graph representation of line drawings using concepts from data mining. This process finds frequent subgraphs and then association rules between these subgraphs. The distant aim is the automatic discovery of symbols and their relations, which are parts of the document model. The main outcome of our work is firstly an algorithm that finds frequent subgraphs in a single graph setting and secondly a modality to find rules and meta-rules between the discovered subgraphs. The searched structures are closed [1] and disjunct subgraphs. One aim of this study is to use the discovered symbols for classification and indexation of document images when a supervised approach is not at hand. The relations found between symbols can be used in segmentation of noisy and occluded document images. The results show that this approach is suitable for patterns, symbols or relation discovery
Complex articulated object tracking
Get PDFIn this paper new results are presented for tracking complex multi-body objects. The theoretical framework is based on robotics techniques and uses an a-priori model of the object including a general mechanical link description. A new kinematic-set formulation takes into account that articulated degrees of freedom are directly observable from the camera and therefore their estimation does not need to pass via a kinematic-chain back to the root. By doing this the tracking techniques are efficient and precise leading to real-time performance and accurate measurements. The system is locally based upon an accurate modeling of a distance criteria. A general method is given for defining any type of mechanical link and experimental results show prismatic, rotational and helical type links. A statistical M-estimation technique is applied to improve robustness. A monocular camera system was used as a real-time sensor to verify the theory
Area and Volume Restoration in Elastically Deformable Solids
Get PDFThis paper describes an improvement of a classical energy-based model to simulate elastically deformable solids. The classical model lacks the ability to prevent the collapsing of solids under influence of external forces, such as user interactions and collision. A thorough explanation is given for the origins of instabilities, and extensions that solve the issues are proposed to the physical model. Within the original framework of the classical model a complete restoration of area and volume is introduced. The improved model is suitable for interactive simulation and can recover from volumetric collapsing, in particular upon large deformation
Contextual System of Symbol Structural Recognition based on an Object-Process Methodology
Get PDFWe present in this paper a symbol recognition system for the graphic documents. This one is based on a contextual approach for symbol structural recognition exploiting an Object-Process Methodology. It uses a processing library composed of structural recognition processings and contextual evaluation processings. These processings allow our system to deal with the multi-representation of symbols. The different processings are controlled, in an automatic way, by an inference engine during the recognition process. The factual, strategic, and descriptive knowledge are structured according to an Object-Process Methodology of the considered recognition application. The representation of symbols are then adapted during the recognition process according to their classes. This original approach for knowledge structuring allows especially the strategic and descriptive knowledge combination which increases the recognition abilities of system. We present results of this system on the GREC2003 contest of symbol recognition
Combining Model-based and Discriminative Approaches in a Modular Two-stage Classification System: Application to Isolated Handwritten Digit Recognition
Get PDFThe motivation of this work is based on two key observations. First, the classification algorithms can be separated into two main categories: discriminative and model-based approaches. Second, two types of patterns can generate problems: ambiguous patterns and outliers. While, the first approach tries to minimize the first type of error, but cannot deal effectively with outliers, the second approach, which is based on the development of a model for each class, make the outlier detection possible, but are not sufficiently discriminant. Thus, we propose to combine these two different approaches in a modular two-stage classification system embedded in a probabilistic framework. In the first stage we pre-estimate the posterior probabilities with a model-based approach and we re-estimate only the highest probabilities with appropriate Support Vector Classifiers (SVC) in the second stage. Another advantage of this combination is to reduce the principal burden of SVC, the processing time necessary to make a decision and to open the way to use SVC in classification problem with a large number of classes. Finally, the first experiments on the benchmark database MNIST have shown that our dynamic classification process allows to maintain the accuracy of SVCs, while decreasing complexity by a factor 8.7 and making the outlier rejection available