David Masip, Agata Lapedriza, & Jordi Vitria. (2008). Multitask Learning: An Application to Incremental Face Recognition. In 3rd International Conference on Computer Vision Theory and Applications (Vol. 1, 585–590).
|
David Masip, Agata Lapedriza, & Jordi Vitria. (2007). Face Verification Sharing Knowledge from Different Subjects. In 2nd International Conference on Computer Vision Theory and Applications (Vol. 2, 268–289).
|
David Masip, Agata Lapedriza, & Jordi Vitria. (2009). Boosted Online Learning for Face Recognition. TSMCB - IEEE Transactions on Systems, Man and Cybernetics part B, 39(2), 530–538.
Abstract: Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight.
|
David Masip. (2003). Dimensionality reduction techniques applied to nearest neighbor classification.
|
David Masip. (2005). Face Classification Using Discriminative Features and Classifier Combination (Jordi Vitria, Ed.). Ph.D. thesis, , .
|
David Lloret, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2002). Motion-induced error correction in ultrasound imaging..
|
David Lloret, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2003). Ultrasound to MR Volume Registration for Brain Sinking Measurement.
|
David Lloret, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2003). Ultrasound to magnetic resonance volume registration for brain sinking measurement.
|
David Lloret, Joan Serrat, Antonio Lopez, A. Soler, & Juan J. Villanueva. (2000). Retinal image registration using creases as anatomical landmarks..
Abstract: Retinal images are routinely used in ophthalmology to study the optical nerve head and the retina. To assess objectively the evolution of an illness, images taken at different times must be registered. Most methods so far have been designed specifically for a single image modality, like temporal series or stereo pairs of angiographies, fluorescein angiographies or scanning laser ophthalmoscope (SLO) images, which makes them prone to fail when conditions vary. In contrast, the method we propose has shown to be accurate and reliable on all the former modalities. It has been adapted from the 3D registration of CT and MR image to 2D. Relevant features (also known as landmarks) are extracted by means of a robust creaseness operator, and resulting images are iteratively transformed until a maximum in their correlation is achieved. Our method has succeeded in more than 100 pairs tried so far, in all cases including also the scaling as a parameter to be optimized
|
David Lloret, & Joan Serrat. (1999). System for calibration of a stereotatic frame..
|
David Lloret, & Derek L.G. Hill. (1999). System for live fusion of 2-D ultrasound scans to pre-interventional MR volumes of a patient..
|
David Lloret, C. Mariño, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2001). Landmark-based registration of full SLO video sequences..
|
David Lloret, Antonio Lopez, Joan Serrat, & Juan J. Villanueva. (1999). Creaseness-based computer tomography and magnetic resonance registration: Comparison with the mutual information method..
Abstract: This paper describes a method which uses the skull as a landmark for automatic registration of computer tomography to magnetic resonance (MR) images. First, the skull is extracted from both images using a new creaseness operator. Then, the resulting creaseness images are used to build a hierarchic structure which permits a robust and fast search. We have justified experimentally the performance of several choices of our algorithm, and we have thoroughly tested its accuracy and robustness against the well-known mutual information method for five different pairs of images. We have found both comparable, and for certain MR images the proposed method achieves better performance.
|
David Lloret, Antonio Lopez, & Joan Serrat. (1998). 3-D image Processing and Modeling, workshop on non-linear model-based image analysis..
|
David Lloret, Antonio Lopez, & Joan Serrat. (1998). Precise registration of CT and MR volumes based on a new creaseness measure.
|