Abstract
Many real life problems require the classification of items in naturally ordered classes. These problems are traditionally handled by conventional methods for nominal classes, ignoring the order. This paper introduces a new training model for feedforward neural networks, for multiclass classification problems, where the classes are ordered. The proposed model has just one output unit which takes values in the interval [0,1]; this interval is then subdivided into K subintervals (one for each class), according to a specific probabilistic model. A comparison is made with conventional approaches, as well as with other architectures specific for ordinal data proposed in the literature. The new model compares favourably with the other methods under study, in the synthetic dataset used for evaluation.

Abstract
Text line segmentation in freestyle handwritten documents remains an open document analysis problem. Curvilinear text lines and small gaps between neighbouring text lines present a challenge to algorithms developed for machine-printed or hand-printed documents. We investigate a general-purpose, knowledge-free method for the automatic detection of text lines based on a stable path approach. Lines affected by curvature and inclination are robustly detected. The proposed methodology was tested on a modern set of handwritten images made available on the ICDAR 2009 handwriting segmentation competition, with promissing results. © 2009 IEEE.

Abstract
Breast cancer conservative treatment (BCCT) is considered the gold standard of breast cancer treatment. However, aesthetic results are heterogeneous and difficult to evaluate in a standardised way. The limited reproducibility of subjective aesthetic evaluation in BCCT forced the research on objective methods. A recent computer system was developed to objectively and automatically evaluate the aesthetic result of BCCT In this system, the detection of the breast contour on the digital photograph of the patient is necessary to extract the features subsequently used in the evaluation process. In this paper we extend an algorithm based on the shortest path on a graph to detect automatically the breast contour. The advantage of graph algorithms is that they are guaranteed to find the global optimum of the problem; the difficulty is that they make it hard to enforce shape constraints. We define and compare different techniques to introduce the a priory knowledge of the mammary contour. Experimental results show that the proposed techniques consistently outperform the base method.

Abstract
Many music works produced in the last century still exist only as original manuscripts or as photocopies. Preserving them entails their digitalization and consequent accessibility in a digital format easy-to-manage which encourages browsing, retrieval, search and analysis while providing a generalized access to the digital material. The manual process to carry out this task is very time consuming and error prone. Automatic optical music recognition (OMR) has emerged as a partial solution to this problem. However, the full potential of this process only reveals itself when integrated in a system that provides seamless access to browsing, retrieval, search and analysis. We address this demand by proposing a modular, flexible and scalable framework that fully integrates the abovementioned functionalities. A web based system to carry out the automatic recognition process, allowing the creation and management of a music corpus, while providing generalized access to it, is a unique and innovative approach to the problem. A prototype has been implemented and is being used as a test platform for OMR algorithms.

Abstract
This paper addresses the problem of unsupervised speaker change detection. Three systems based on the Bayesian Information Criterion (BIC) are tested. The first system investigates the AudioSpectrumCentroid and the AudioWaveformEnvelope features, implements a dynamic thresholding followed by a fusion scheme, and finally applies BIC. The second method is a real-time one that uses a metric-based approach employing the line spectral pairs and the BIC to validate a potential speaker change point. The third method consists of three modules. In the first module, a measure based on second-order statistics is used; in the second module, the Euclidean distance and T-2 Hotelling statistic are applied; and in the third module, the BIC is utilized. The experiments are carried out on a dataset created by concatenating speakers from the TIMIT database, that is referred to as the TIMIT data set. A comparison between the performance of the three systems is made based on t-statistics.

Abstract
Ordinal classification is a form of multi-class classification where there is an inherent ordering between the classes, but not a meaningful numeric difference between them. Although conventional methods, designed for nominal classes or regression problems, can be used to solve the ordinal data problem, there are benefits in developing models specific to this kind of data. This paper introduces a new rationale to include the information about the order in the design of a classification model. The method encompasses the inclusion of consistency constraints between adjacent decision regions. A new decision tree and a new nearest neighbour algorithms are then designed under that rationale. An experimental study with artificial and real data sets verifies the usefulness of the proposed approach. © 2010 IEEE.