Mikhail Mozerov, V. Kober, & I.A. Ovseyevich. (2006). A Stereo Matching Algorithm with Global Smoothness Criterion.
|
Mikhail Mozerov, & V. Kober. (2006). Impulse Noise Removal with Gradient Adaptive Neighborhoods. Optical Engineering, 45: 67003.
|
Mikhail Mozerov. (2006). An Effective Stereo Matching Algorithm with Optimal Path Cost Aggregation. In 28th Annual Symposium of the German Association for Pattern Recognition, LNCS 4174: 617–626.
|
Mikhail Mozerov, Ignasi Rius, Xavier Roca, & Jordi Gonzalez. (2006). 3D Human Motion Sequences Synchronization Using Dense Matching Algorithm. In 28th Annual Symposium of the German Association for Pattern Recognition, LNCS 4174: 485–494, ISBN 978–3–540–44412–1.
|
E. Pastor, A. Agueda, Juan Andrade, M. Muñoz, Y. Perez, & E. Planas. (2006). Computing the rate of spread of linear flame fronts by thermal image processing. Fire Safety Journal, 41(8):569–579.
|
T. Alejandra Vidal, A. Sanfeliu, & Juan Andrade. (2006). Autonomous Single Camera Exploration.
|
Eduard Vazquez, Francesc Tous, Ramon Baldrich, & Maria Vanrell. (2006). n-Dimensional Distribution Reduction Preserving its Structure. In Artificial Intelligence Research and Development, M. Polit et al. (Eds.), 146: 167–175.
|
Jaume Amores. (2013). Multiple Instance Classification: review, taxonomy and comparative study. AI - Artificial Intelligence, 201, 81–105.
Abstract: Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL
methods.
Keywords: Multi-instance learning; Codebook; Bag-of-Words
|
Anton Cervantes, Gemma Sanchez, Josep Llados, Agnes Borras, & Ana Rodriguez. (2006). Biometric Recognition Based on Line Shape Descriptors. In Lecture Notes in Computer Science (Vol. 3926, 346–357,). Springer Link.
Abstract: Abstract. In this paper we propose biometric descriptors inspired by shape signatures traditionally used in graphics recognition approaches. In particular several methods based on line shape descriptors used to iden- tify newborns from the biometric information of the ears are developed. The process steps are the following: image acquisition, ear segmentation, ear normalization, feature extraction and identification. Several shape signatures are defined from contour images. These are formulated in terms of zoning and contour crossings descriptors. Experimental results are presented to demonstrate the effectiveness of the used techniques.
|
Ernest Valveny, & Philippe Dosch. (2006). A general framework for the evaluation of symbol recognition methods. International Journal on Document Analysis and Recognition.
|
Fadi Dornaika, Francisco Javier Orozco, & Jordi Gonzalez. (2006). Combined Head, Lips, Eyebrows, and Eyelids Tracking Using Adaptive Appearance Models. In IV Conference on Articulated Motion and Deformable Objects (AMDO´06), LNCS 4069: 110–119.
|
Fadi Dornaika, & Angel Sappa. (2006). 3D Face Tracking using Appearance Registration and Robust Iterative Closest Point Algorithm. In 21st International Symposium on Computer and Information Sciences (ISCIS´06), LNCS 4263: 532–541.
|
Fadi Dornaika, & Angel Sappa. (2006). Rigid and Non-Rigid Face Motion Tracking by Aligning Texture Maps and Stereo-Based 3D Models. In 8th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS´06), LNCS 4179: 675–684.
|
Fadi Dornaika, & Angel Sappa. (2006). 3D Motion from Image Derivatives using the Least Trimmed Square Regression. In International Workshop on Intelligent Computing in Pattern Analysis/Synthesis (IWICPAS´06), LNCS 4153: 76–84.
|
Philippe Dosch, & Ernest Valveny. (2006). Report on the Second Symbol Recognition Contest. In Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 381–397.
|