Abstract
Many music works produced in the past are currently available only as original manuscripts or as photocopies. Preserving them entails their digitalization and consequent accessibility in a machine-readable format, which encourages browsing, retrieval, search and analysis while providing a generalized access to the digital material. Carrying this task manually is very time consuming and error prone. While optical music recognition (OMR) systems usually perform well on printed scores, the processing of handwritten music by computers remains below the expectations. One of the fundamental stages to carry out this task is the detection and subsequent removal of staff lines. In this paper we integrate a general-purpose, knowledge-free method for the automatic detection of staff lines based on stable paths, into a recently developed staff line removal toolkit. Lines affected by curvature, discontinuities, and inclination are robustly detected. We have also developed a staff removal algorithm adapting an existing line removal approach to use the stable path algorithm at the detection stage, Experimental results show that the proposed technique outperforms well-established algorithms. The developed algorithm will now be integrated in a web based system providing seamless access to browsing, retrieval, search and analysis of submitted scores.

Abstract
Support vector machines (SVMs) were initially proposed to solve problems with two classes. Despite the myriad of schemes for multiclassification with SVMs proposed since then, little work has been done for the case where the classes are ordered. Usually one constructs a nominal classifier and a posteriori defines the order. The definition of an ordinal classifier leads to a better generalisation. Moreover, most of the techniques presented so far in the literature can generate ambiguous regions. All-at-Once methods have been proposed to solve this issue. In this work we devise a new SVM methodology based on the unimodal paradigm with the All-at-Once scheme for the ordinal classification. © 2010 IEEE.

Abstract
In this work we consider the problem of binary classification where the classifier may abstain instead of classifying each observation, leaving the critical items for human evaluation. This article motivates and presents a novel method to learn the reject region on complex data. Observations are replicated and then a single binary classifier determines the decision plane. The proposed method is an extension of a method available in the literature for the classification of ordinal data. Our method is compared with standard techniques on synthetic and real datasets, emphasizing the advantages of the proposed approach.

Abstract
The optical recognition of handwritten musical scores by computers remains far from ideal. Most OMR algorithms rely on an estimation of the staffline thickness and the vertical line distance within the same staff. Subsequent operation can use these values as references, dismissing the need for some predetermined threshold values. In this work we improve on previous conventional estimates for these two reference lengths. We start by proposing a new method for binarized music scores and then extend the approach for gray-level music scores. An experimental study with 50 images is used to assess the interest of the novel method. © 2010 IEEE.

Abstract
The restoration and recovery of a defective skull can be performed through operative techniques to implant a customized prosthesis. Recently, image processing and surface reconstruction methods have been used for digital prosthesis design. In this paper we present a framework for prosthesis modeling. Firstly, we take the computed tomography (CT) of the skull and perform bone segmentation by thresholding. The obtained binary volume is processed by morphological operators, frame-by-frame, to get the inner and outer boundaries of the bone. These curves are used to initialize a 2D deformable model that generates the prosthesis boundary in each CT frame. In this way, we can fill the prosthesis volume which is the input for a marching cubes technique that computes the digital model of the target geometry. In the experimental results we demonstrate the potential of our technique and compare it with a related one.

Abstract
The cosmetic result is an important endpoint for breast cancer conservative treatment (BCCT), but the verification of this outcome remains without a standard. Objective assessment methods are preferred to overcome the drawbacks of subjective evaluation. In this paper a novel algorithm is proposed, based on support vector machines, for the classification of ordinal categorical data. This classifier is then applied as a new methodology for the objective assessment of the aesthetic result of BCCT. Based on the new classifier, a semi-objective score for quantification of the aesthetic results of BCCT was developed, allowing the discrimination of patients into four classes.