|
|
Dani Rowe, Jordi Gonzalez, Marco Pedersoli, & Juan J. Villanueva. (2010). On Tracking Inside Groups. MVA - Machine Vision and Applications, 21(2), 113–127.
Abstract: This work develops a new architecture for multiple-target tracking in unconstrained dynamic scenes, which consists of a detection level which feeds a two-stage tracking system. A remarkable characteristic of the system is its ability to track several targets while they group and split, without using 3D information. Thus, special attention is given to the feature-selection and appearance-computation modules, and to those modules involved in tracking through groups. The system aims to work as a stand-alone application in complex and dynamic scenarios. No a-priori knowledge about either the scene or the targets, based on a previous training period, is used. Hence, the scenario is completely unknown beforehand. Successful tracking has been demonstrated in well-known databases of both indoor and outdoor scenarios. Accurate and robust localisations have been yielded during long-term target merging and occlusions.
|
|
|
|
Ignasi Rius, Jordi Gonzalez, Javier Varona, & Xavier Roca. (2009). Action-specific motion prior for efficient bayesian 3D human body tracking. PR - Pattern Recognition, 42(11), 2907–2921.
Abstract: In this paper, we aim to reconstruct the 3D motion parameters of a human body
model from the known 2D positions of a reduced set of joints in the image plane.
Towards this end, an action-specific motion model is trained from a database of real
motion-captured performances. The learnt motion model is used within a particle
filtering framework as a priori knowledge on human motion. First, our dynamic
model guides the particles according to similar situations previously learnt. Then, the solution space is constrained so only feasible human postures are accepted as valid solutions at each time step. As a result, we are able to track the 3D configuration of the full human body from several cycles of walking motion sequences using only the 2D positions of a very reduced set of joints from lateral or frontal viewpoints.
|
|
|
|
Mikhail Mozerov, Ariel Amato, Xavier Roca, & Jordi Gonzalez. (2009). Solving the Multi Object Occlusion Problem in a Multiple Camera Tracking System. Pattern Recognition and Image Analysis, 165–171.
Abstract: An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path.
|
|
|
|
Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat, & Antonio Lopez. (2009). An iterative multiresolution scheme for SFM with missing data. JMIV - Journal of Mathematical Imaging and Vision, 34(3), 240–258.
Abstract: Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix.
Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported.
Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach.
|
|
|
|
Marçal Rusiñol, Josep Llados, & Gemma Sanchez. (2010). Symbol Spotting in Vectorized Technical Drawings Through a Lookup Table of Region Strings. PAA - Pattern Analysis and Applications, 13(3), 321–331.
Abstract: In this paper, we address the problem of symbol spotting in technical document images applied to scanned and vectorized line drawings. Like any information spotting architecture, our approach has two components. First, symbols are decomposed in primitives which are compactly represented and second a primitive indexing structure aims to efficiently retrieve similar primitives. Primitives are encoded in terms of attributed strings representing closed regions. Similar strings are clustered in a lookup table so that the set median strings act as indexing keys. A voting scheme formulates hypothesis in certain locations of the line drawing image where there is a high presence of regions similar to the queried ones, and therefore, a high probability to find the queried graphical symbol. The proposed approach is illustrated in a framework consisting in spotting furniture symbols in architectural drawings. It has been proved to work even in the presence of noise and distortion introduced by the scanning and raster-to-vector processes.
|
|
|
|
Marçal Rusiñol, & Josep Llados. (2009). A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices. IJDAR - International Journal on Document Analysis and Recognition, 12(2), 83–96.
Abstract: Symbol spotting systems are intended to retrieve regions of interest from a document image database where the queried symbol is likely to be found. They shall have the ability to recognize and locate graphical symbols in a single step. In this paper, we present a set of measures to evaluate the performance of a symbol spotting system in terms of recognition abilities, location accuracy and scalability. We show that the proposed measures allow to determine the weaknesses and strengths of different methods. In particular we have tested a symbol spotting method based on a set of four different off-the-shelf shape descriptors.
Keywords: Performance evaluation; Symbol Spotting; Graphics Recognition
|
|
|
|
Miquel Ferrer, Ernest Valveny, & F. Serratosa. (2009). Median Graphs: A Genetic Approach based on New Theoretical Properties. PR - Pattern Recognition, 42(9), 2003–2012.
Abstract: Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present two major contributions. On one side, and from a theoretical point of view, we show new theoretical properties of the median graph. On the other side, using these new properties, we present a new approximate algorithm based on the genetic search, that improves the computation of the median graph. Finally, we perform a set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity. With these results, we show how the concept of the median graph can be used in real applications and leaves the box of the only-theoretical concepts, demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs.
Keywords: Median graph; Genetic search; Maximum common subgraph; Graph matching; Structural pattern recognition
|
|
|
|
Daniel Ponsa, & Antonio Lopez. (2009). Variance reduction techniques in particle-based visual contour Tracking. PR - Pattern Recognition, 42(11), 2372–2391.
Abstract: This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done.
Keywords: Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling
|
|
|
|
Pau Baiget, Carles Fernandez, Xavier Roca, & Jordi Gonzalez. (2009). Generation of Augmented Video Sequences Combining Behavioral Animation and Multi Object Tracking. Computer Animation and Virtual Worlds, 20(4), 473–489.
Abstract: In this paper we present a novel approach to generate augmented video sequences in real-time, involving interactions between virtual and real agents in real scenarios. On the one hand, real agent motion is estimated by means of a multi-object tracking algorithm, which determines real objects' position over the scenario for each time step. On the other hand, virtual agents are provided with behavior models considering their interaction with the environment and with other agents. The resulting framework allows to generate video sequences involving behavior-based virtual agents that react to real agent behavior and has applications in education, simulation, and in the game and movie industries. We show the performance of the proposed approach in an indoor and outdoor scenario simulating human and vehicle agents. Copyright © 2009 John Wiley & Sons, Ltd.
We present a novel approach to generate augmented video sequences in real-time, involving interactions between virtual and real agents in real scenarios. On the one hand, real agent motion is estimated by means of a multi-object tracking algorithm, which determines real objects' position over the scenario for each time step. On the other hand, virtual agents are provided with behavior models considering their interaction with the environment and with other agents. © 2009 Wiley Periodicals, Inc.
|
|
|
|
Javier Vazquez, C. Alejandro Parraga, Maria Vanrell, & Ramon Baldrich. (2009). Color Constancy Algorithms: Psychophysical Evaluation on a New Dataset. Journal of Imaging Science and Technology, 53(3), 031105–9.
Abstract: The estimation of the illuminant of a scene from a digital image has been the goal of a large amount of research in computer vision. Color constancy algorithms have dealt with this problem by defining different heuristics to select a unique solution from within the feasible set. The performance of these algorithms has shown that there is still a long way to go to globally solve this problem as a preliminary step in computer vision. In general, performance evaluation has been done by comparing the angular error between the estimated chromaticity and the chromaticity of a canonical illuminant, which is highly dependent on the image dataset. Recently, some workers have used high-level constraints to estimate illuminants; in this case selection is based on increasing the performance on the subsequent steps of the systems. In this paper we propose a new performance measure, the perceptual angular error. It evaluates the performance of a color constancy algorithm according to the perceptual preferences of humans, or naturalness (instead of the actual optimal solution) and is independent of the visual task. We show the results of a new psychophysical experiment comparing solutions from three different color constancy algorithms. Our results show that in more than a half of the judgments the preferred solution is not the one closest to the optimal solution. Our experiments were performed on a new dataset of images acquired with a calibrated camera with an attached neutral grey sphere, which better copes with the illuminant variations of the scene.
|
|
|
|
Marçal Rusiñol, Agnes Borras, & Josep Llados. (2010). Relational Indexing of Vectorial Primitives for Symbol Spotting in Line-Drawing Images. PRL - Pattern Recognition Letters, 31(3), 188–201.
Abstract: This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an off-the-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results.
Keywords: Document image analysis and recognition, Graphics recognition, Symbol spotting ,Vectorial representations, Line-drawings
|
|
|
|
Sergio Escalera, Alicia Fornes, O. Pujol, Petia Radeva, Gemma Sanchez, & Josep Llados. (2009). Blurred Shape Model for Binary and Grey-level Symbol Recognition. PRL - Pattern Recognition Letters, 30(15), 1424–1433.
Abstract: Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance.
|
|
|
|
Mehdi Mirza-Mohammadi, Sergio Escalera, & Petia Radeva. (2009). Contextual-Guided Bag-of-Visual-Words Model for Multi-class Object Categorization. In 13th International Conference on Computer Analysis of Images and Patterns (Vol. 5702, 748–756). LNCS. Springer Berlin Heidelberg.
Abstract: Bag-of-words model (BOW) is inspired by the text classification problem, where a document is represented by an unsorted set of contained words. Analogously, in the object categorization problem, an image is represented by an unsorted set of discrete visual words (BOVW). In these models, relations among visual words are performed after dictionary construction. However, close object regions can have far descriptions in the feature space, being grouped as different visual words. In this paper, we present a method for considering geometrical information of visual words in the dictionary construction step. Object interest regions are obtained by means of the Harris-Affine detector and then described using the SIFT descriptor. Afterward, a contextual-space and a feature-space are defined, and a merging process is used to fuse feature words based on their proximity in the contextual-space. Moreover, we use the Error Correcting Output Codes framework to learn the new dictionary in order to perform multi-class classification. Results show significant classification improvements when spatial information is taken into account in the dictionary construction step.
|
|
|
|
Javier Vazquez, C. Alejandro Parraga, & Maria Vanrell. (2009). Ordinal pairwise method for natural images comparison. PER - Perception, 38, 180.
Abstract: 38(Suppl.)ECVP Abstract Supplement
We developed a new psychophysical method to compare different colour appearance models when applied to natural scenes. The method was as follows: two images (processed by different algorithms) were displayed on a CRT monitor and observers were asked to select the most natural of them. The original images were gathered by means of a calibrated trichromatic digital camera and presented one on top of the other on a calibrated screen. The selection was made by pressing on a 6-button IR box, which allowed observers to consider not only the most natural but to rate their selection. The rating system allowed observers to register how much more natural was their chosen image (eg, much more, definitely more, slightly more), which gave us valuable extra information on the selection process. The results were analysed considering both the selection as a binary choice (using Thurstone's law of comparative judgement) and using Bradley-Terry method for ordinal comparison. Our results show a significant difference in the rating scales obtained. Although this method has been used in colour constancy algorithm comparisons, its uses are much wider, eg to compare algorithms of image compression, rendering, recolouring, etc.
|
|
|
|
Robert Benavente, C. Alejandro Parraga, & Maria Vanrell. (2009). Colour categories boundaries are better defined in contextual conditions. PER - Perception, 38, 36.
Abstract: In a previous experiment [Parraga et al, 2009 Journal of Imaging Science and Technology 53(3)] the boundaries between basic colour categories were measured by asking subjects to categorize colour samples presented in isolation (ie on a dark background) using a YES/NO paradigm. Results showed that some boundaries (eg green – blue) were very diffuse and the subjects' answers presented bimodal distributions, which were attributed to the emergence of non-basic categories in those regions (eg turquoise). To confirm these results we performed a new experiment focussed on the boundaries where bimodal distributions were more evident. In this new experiment rectangular colour samples were presented surrounded by random colour patches to simulate contextual conditions on a calibrated CRT monitor. The names of two neighbouring colours were shown at the bottom of the screen and subjects selected the boundary between these colours by controlling the chromaticity of the central patch, sliding it across these categories' frontier. Results show that in this new experimental paradigm, the formerly uncertain inter-colour category boundaries are better defined and the dispersions (ie the bimodal distributions) that occurred in the previous experiment disappear. These results may provide further support to Berlin and Kay's basic colour terms theory.
|
|
