Sergio Escalera. (2008). Coding and Decoding Design of ECOCs for Multi-class Pattern and Object Recognition A (Petia Radeva, & Oriol Pujol, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Many real problems require multi-class decisions. In the Pattern Recognition field,
many techniques have been proposed to deal with the binary problem. However,
the extension of many 2-class classifiers to the multi-class case is a hard task. In
this sense, Error-Correcting Output Codes (ECOC) demonstrated to be a powerful
tool to combine any number of binary classifiers to model multi-class problems. But
there are still many open issues about the capabilities of the ECOC framework. In
this thesis, the two main stages of an ECOC design are analyzed: the coding and
the decoding steps. We present different problem-dependent designs. These designs
take advantage of the knowledge of the problem domain to minimize the number
of classifiers, obtaining a high classification performance. On the other hand, we
analyze the ECOC codification in order to define new decoding rules that take full
benefit from the information provided at the coding step. Moreover, as a successful
classification requires a rich feature set, new feature detection/extraction techniques
are presented and evaluated on the new ECOC designs. The evaluation of the new
methodology is performed on different real and synthetic data sets: UCI Machine
Learning Repository, handwriting symbols, traffic signs from a Mobile Mapping System, Intravascular Ultrasound images, Caltech Repository data set or Chaga’s disease
data set. The results of this thesis show that significant performance improvements
are obtained on both traditional coding and decoding ECOC designs when the new
coding and decoding rules are taken into account.
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Patricia Marquez, Debora Gil, Aura Hernandez-Sabate, & Daniel Kondermann. (2013). When Is A Confidence Measure Good Enough? In 9th International Conference on Computer Vision Systems (Vol. 7963, pp. 344–353). LNCS. Springer Link.
Abstract: Confidence estimation has recently become a hot topic in image processing and computer vision.Yet, several definitions exist of the term “confidence” which are sometimes used interchangeably. This is a position paper, in which we aim to give an overview on existing definitions,
thereby clarifying the meaning of the used terms to facilitate further research in this field. Based on these clarifications, we develop a theory to compare confidence measures with respect to their quality.
Keywords: Optical flow, confidence measure, performance evaluation
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David Vazquez, Jiaolong Xu, Sebastian Ramos, Antonio Lopez, & Daniel Ponsa. (2013). Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes. In CVPR Workshop on Ground Truth – What is a good dataset? (pp. 706–711). IEEE.
Abstract: Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs.
Keywords: Pedestrian Detection; Domain Adaptation
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David Augusto Rojas. (2009). Colouring Local Feature Detection for Matching (Vol. 133). Master's thesis, , Bellaterra, Barcelona.
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Juan Diego Gomez. (2009). Toward Robust Myocardial Blush Grade Estimation in Contrast Angiography (Vol. 134). Master's thesis, , Bellaterra, Barcelona.
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Olivier Penacchio. (2009). Relative Density of L, M, S photoreceptors in the Human Retina (Vol. 135). Master's thesis, , Bellaterra, Barcelona.
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Xavier Boix. (2009). Learning Conditional Random Fields for Stereo (Vol. 136). Master's thesis, , Bellaterra, Barcelona.
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Shida Beigpour. (2009). Physics-based Reflectance Estimation Applied to Recoloring (Vol. 137). Master's thesis, , Bellaterra, Barcelona.
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Jaume Gibert. (2009). Learning structural representations and graph matching paradigms in the context of object recognition (Vol. 143). Master's thesis, , .
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Jose Carlos Rubio. (2009). Graph matching based on graphical models with application to vehicle tracking and classification at night (Vol. 144). Master's thesis, , Bellaterra, Barcelona.
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Farshad Nourbakhsh. (2009). Colour logo recognition (Vol. 145). Master's thesis, , Bellaterra, Barcelona.
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Enric Sala. (2009). Off-line person-dependent signature verification (Vol. 146). Master's thesis, , Bellaterra, Barcelona.
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Wenjuan Gong. (2009). Action priors for human pose tracking by particle filter. Master's thesis, , Bellaterra, Barcelona.
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Diego Alejandro Cheda. (2009). Monocular egomotion estimation for ADAS application (Vol. 148). Ph.D. thesis, , Bellaterra, Barcelona.
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Javier Marin. (2009). Virtual learning for real testing (Vol. 150). Master's thesis, , bell.
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