David Aldavert, Ricardo Toledo, Arnau Ramisa, & Ramon Lopez de Mantaras. (2009). Efficient Object Pixel-Level Categorization using Bag of Features: Advances in Visual Computing. In 5th International Symposium on Visual Computing (Vol. 5875, 44–55). Springer Berlin Heidelberg.
Abstract: In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost.
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Bogdan Raducanu, & Fadi Dornaika. (2009). Natural Facial Expression Recognition Using Dynamic and Static Schemes. In 5th International Symposium on Visual Computing (Vol. 5875, 730–739). LNCS. Springer Berlin Heidelberg.
Abstract: Affective computing is at the core of a new paradigm in HCI and AI represented by human-centered computing. Within this paradigm, it is expected that machines will be enabled with perceiving capabilities, making them aware about users’ affective state. The current paper addresses the problem of facial expression recognition from monocular videos sequences. We propose a dynamic facial expression recognition scheme, which is proven to be very efficient. Furthermore, it is conveniently compared with several static-based systems adopting different magnitude of facial expression. We provide evaluations of performance using Linear Discriminant Analysis (LDA), Non parametric Discriminant Analysis (NDA), and Support Vector Machines (SVM). We also provide performance evaluations using arbitrary test video sequences.
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David Aldavert, Ricardo Toledo, Arnau Ramisa, & Ramon Lopez de Mantaras. (2009). Visual Registration Method For A Low Cost Robot: Computer Vision Systems. In 7th International Conference on Computer Vision Systems (Vol. 5815, 204–214). LNCS. Springer Berlin Heidelberg.
Abstract: An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most methods estimate the transformation relating the maps from matches established between low level feature extracted from sensor data. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a vocabulary tree approach to identify putative corresponding maps followed by the Newton minimization algorithm to find the transformation that relates both maps. The proposed method is evaluated in a typical office environment showing good performance.
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Mohammad Rouhani, & Angel Sappa. (2009). A Novel Approach to Geometric Fitting of Implicit Quadrics. In 8th International Conference on Advanced Concepts for Intelligent Vision Systems (Vol. 5807, 121–132). LNCS. Springer Berlin Heidelberg.
Abstract: This paper presents a novel approach for estimating the geometric distance from a given point to the corresponding implicit quadric curve/surface. The proposed estimation is based on the height of a tetrahedron, which is used as a coarse but reliable estimation of the real distance. The estimated distance is then used for finding the best set of quadric parameters, by means of the Levenberg-Marquardt algorithm, which is a common framework in other geometric fitting approaches. Comparisons of the proposed approach with previous ones are provided to show both improvements in CPU time as well as in the accuracy of the obtained results.
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Francesco Ciompi, Oriol Pujol, E Fernandez-Nofrerias, J. Mauri, & Petia Radeva. (2009). ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences. In 12th International Conference on Medical Image and Computer Assisted Intervention (Vol. 5762). LNCS. Springer Berlin Heidelberg.
Abstract: The measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. In this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from Intravascular ultrasound sequences. The segmentation is tackled as a classification problem where the contextual information is exploited by means of Conditional Random Fields (CRFs). A multi-class classification framework is proposed, and inference is achieved by combining binary CRFs according to the Error-Correcting-Output-Code technique. The results are validated against manually segmented sequences. Finally, the method is compared with other state-of-the-art classifiers.
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Sergio Escalera, Alicia Fornes, Oriol Pujol, & Petia Radeva. (2009). Multi-class Binary Symbol Classification with Circular Blurred Shape Models. In 15th International Conference on Image Analysis and Processing (Vol. 5716, 1005–1014). LNCS. Springer Berlin Heidelberg.
Abstract: Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-of-the-art descriptors, showing significant performance improvements.
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L.Tarazon, D. Perez, N. Serrano, V. Alabau, Oriol Ramos Terrades, A. Sanchis, et al. (2009). Confidence Measures for Error Correction in Interactive Transcription of Handwritten Text. In 15th International Conference on Image Analysis and Processing (Vol. 5716, pp. 567–574). LNCS. Springer Berlin Heidelberg.
Abstract: An effective approach to transcribe old text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the human supervisor, and the supervisor is assisted by the system to complete the transcription task as efficiently as possible. In this paper, we focus on a particular system prototype called GIDOC, which can be seen as a first attempt to provide user-friendly, integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. More specifically, we focus on the handwriting recognition part of GIDOC, for which we propose the use of confidence measures to guide the human supervisor in locating possible system errors and deciding how to proceed. Empirical results are reported on two datasets showing that a word error rate not larger than a 10% can be achieved by only checking the 32% of words that are recognised with less confidence.
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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.
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Miquel Ferrer, Ernest Valveny, F. Serratosa, I. Bardaji, & Horst Bunke. (2009). Graph-based k-means clustering: A comparison of the set versus the generalized median graph. In 13th International Conference on Computer Analysis of Images and Patterns (Vol. 5702, 342–350). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph.
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Debora Gil, Aura Hernandez-Sabate, Mireia Burnat, Steven Jansen, & Jordi Martinez-Vilalta. (2009). Structure-Preserving Smoothing of Biomedical Images. In 13th International Conference on Computer Analysis of Images and Patterns (Vol. 5702, pp. 427–434). LNCS. Springer Berlin Heidelberg.
Abstract: Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images.
Keywords: non-linear smoothing; differential geometry; anatomical structures segmentation; cardiac magnetic resonance; computerized tomography.
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Maria Salamo, Sergio Escalera, & Petia Radeva. (2009). Quality Enhancement based on Reinforcement Learning and Feature Weighting for a Critiquing-Based Recommender. In 8th International Conference on Case-Based Reasoning (Vol. 5650, 298–312). LNCS. Springer Berlin Heidelberg.
Abstract: Personalizing the product recommendation task is a major focus of research in the area of conversational recommender systems. Conversational case-based recommender systems help users to navigate through product spaces, alternatively making product suggestions and eliciting users feedback. Critiquing is a common form of feedback and incremental critiquing-based recommender system has shown its efficiency to personalize products based primarily on a quality measure. This quality measure influences the recommendation process and it is obtained by the combination of compatibility and similarity scores. In this paper, we describe new compatibility strategies whose basis is on reinforcement learning and a new feature weighting technique which is based on the user’s history of critiques. Moreover, we show that our methodology can significantly improve recommendation efficiency in comparison with the state-of-the-art approaches.
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Miquel Ferrer, Dimosthenis Karatzas, Ernest Valveny, & Horst Bunke. (2009). A Recursive Embedding Approach to Median Graph Computation. In 7th IAPR – TC–15 Workshop on Graph–Based Representations in Pattern Recognition (Vol. 5534, 113–123). LNCS. Springer Berlin Heidelberg.
Abstract: The median graph has been shown to be a good choice to infer a representative of a set of graphs. It has been successfully applied to graph-based classification and clustering. Nevertheless, its computation is extremely complex. Several approaches have been presented up to now based on different strategies. In this paper we present a new approximate recursive algorithm for median graph computation based on graph embedding into vector spaces. Preliminary experiments on three databases show that this new approach is able to obtain better medians than the previous existing approaches.
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Oscar Camara, Estanislao Oubel, Gemma Piella, Simone Balocco, Mathieu De Craene, & Alejandro F. Frangi. (2009). Multi-sequence Registration of Cine, Tagged and Delay-Enhancement MRI with Shift Correction and Steerable Pyramid-Based Detagging. In 5th International Conference on Functional Imaging and Modeling of the Heart (Vol. 5528, 330–338). LNCS. Springer Berlin Heidelberg.
Abstract: In this work, we present a registration framework for cardiac cine MRI (cMRI), tagged (tMRI) and delay-enhancement MRI (deMRI), where the two main issues to find an accurate alignment between these images have been taking into account: the presence of tags in tMRI and respiration artifacts in all sequences. A steerable pyramid image decomposition has been used for detagging purposes since it is suitable to extract high-order oriented structures by directional adaptive filtering. Shift correction of cMRI is achieved by firstly maximizing the similarity between the Long Axis and Short Axis cMRI. Subsequently, these shift-corrected images are used as target images in a rigid registration procedure with their corresponding tMRI/deMRI in order to correct their shift. The proposed registration framework has been evaluated by 840 registration tests, considerably improving the alignment of the MR images (mean RMS error of 2.04mm vs. 5.44mm).
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Carlo Gatta, Juan Diego Gomez, Francesco Ciompi, Oriol Rodriguez-Leor, & Petia Radeva. (2009). Toward robust myocardial blush grade estimation in contrast angiography. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524, 249–256). LNCS. Springer Berlin Heidelberg.
Abstract: The assessment of Myocardial Blush Grade after primary angioplasty is a precious diagnostic tool to understand if the patient needs further medication or the use of specifics drugs. Unfortunately, the assessment of MBG is difficult for non highly specialized staff. Experimental data show that there is poor correlation between MBG assessment of low and high specialized staff, thus reducing its applicability. This paper proposes a method able to achieve an objective measure of MBG, or a set of parameters that correlates with the MBG. The method tracks the blush area starting from just one single frame tagged by the physician. As a consequence, the blush area is kept isolated from contaminating phenomena such as diaphragm and arteries movements. We also present a method to extract four parameters that are expected to correlate with the MBG. Preliminary results show that the method is capable of extracting interesting information regarding the behavior of the myocardial perfusion.
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Francesco Ciompi, Oriol Pujol, Oriol Rodriguez-Leor, Carlo Gatta, Angel Serrano, & Petia Radeva. (2009). Enhancing In-Vitro IVUS Data for Tissue Characterization. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524, 241–248). LNCS. Springer Berlin Heidelberg.
Abstract: Intravascular Ultrasound (IVUS) data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue, obtaining a reliable ground truth. The main drawback of this method is the few number of available study cases due to the complex procedure of histological analysis. In this work we propose a novel semi-supervised approach to enhance the in-vitro training set by including examples from in-vivo coronary plaques data set. For this purpose, a Sequential Floating Forward Selection method is applied on in-vivo data and plaque characterization performances are evaluated by Leave-One-Patient-Out cross-validation technique. Supervised data inclusion improves global classification accuracy from 89.39% to 91.82%.
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