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Jordi Vitria, M. Bressan, & Petia Radeva. (2006). Bayesian classification of cork stoppers using class-conditional independent component analysis. IEEE Transactions on Systems, Man and Cybernetics (Part C), 36(6).
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Karla Lizbeth Caballero, Joel Barajas, Oriol Pujol, Neus Salvatella, & Petia Radeva. (2006). In-Vivo IVUS Tissue Classification: A Comparison Between RF Signal Analysis and Reconstructed Images. In 11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 137–146.
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Francisco Javier Orozco, Pau Baiget, Jordi Gonzalez, & Xavier Roca. (2006). Eyelids and Face Tracking in Real-Time.
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Salim Jouili, Salvatore Tabbone, & Ernest Valveny. (2010). Comparing Graph Similarity Measures for Graphical Recognition. In Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers (Vol. 6020, pp. 37–48). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique.
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Fadi Dornaika, & Franck Davoine. (2006). On appearance based face and facial action tracking. IEEE Transactions on Circuits and Systems for Video Technology, 16(9): 1838–1853.
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Fadi Dornaika, & J. Ahlberg. (2006). Fitting 3D face models for tracking and active appearance model training. Image and Vision Computing, 24(9): 1010–1024.
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Fadi Dornaika, & Franck Davoine. (2006). Facial expression recognition using auto-regressive models.
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R. Herault, Franck Davoine, Fadi Dornaika, & Y. Grandvalet. (2006). Simultaneous and robust face and facial action tracking.
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David Aldavert. (2006). Visual Simultaneous Localization and Mapping.
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David Geronimo. (2006). Model Features and Horizon Line Estimation for Pedestrian Detection in Advanced Driver Assistance Systems. Master's thesis, , .
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German Ros, J. Guerrero, Angel Sappa, Daniel Ponsa, & Antonio Lopez. (2013). Fast and Robust l1-averaging-based Pose Estimation for Driving Scenarios. In 24th British Machine Vision Conference.
Abstract: Robust visual pose estimation is at the core of many computer vision applications, being fundamental for Visual SLAM and Visual Odometry problems. During the last decades, many approaches have been proposed to solve these problems, being RANSAC one of the most accepted and used. However, with the arrival of new challenges, such as large driving scenarios for autonomous vehicles, along with the improvements in the data gathering frameworks, new issues must be considered. One of these issues is the capability of a technique to deal with very large amounts of data while meeting the realtime
constraint. With this purpose in mind, we present a novel technique for the problem of robust camera-pose estimation that is more suitable for dealing with large amount of data, which additionally, helps improving the results. The method is based on a combination of a very fast coarse-evaluation function and a robust ℓ1-averaging procedure. Such scheme leads to high-quality results while taking considerably less time than RANSAC.
Experimental results on the challenging KITTI Vision Benchmark Suite are provided, showing the validity of the proposed approach.
Keywords: SLAM
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Jaume Amores. (2015). MILDE: multiple instance learning by discriminative embedding. KAIS - Knowledge and Information Systems, 42(2), 381–407.
Abstract: While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data.
Keywords: Multi-instance learning; Codebook; Bag of words
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Albert Gordo, Florent Perronnin, Yunchao Gong, & Svetlana Lazebnik. (2014). Asymmetric Distances for Binary Embeddings. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(1), 33–47.
Abstract: In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques.
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Karla Lizbeth Caballero, Joel Barajas, Oriol Pujol, J. Mauri, & Petia Radeva. (2006). Using Radio Frequency Reconstructed IVUS Images in Tissue Classification.
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David Rotger, Petia Radeva, & O. Rodriguez. (2006). Vessel Tortuosity Extraction from IVUS Images.
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