Abstract
Falls are very rare and extremely difficult to acquire in free living conditions. Due to this, most of prior work on fall detection has focused on simulated datasets acquired in scenarios that mimic the real-world context, however, the validation of systems trained with simulated falls remains unclear. This work presents a transfer learning approach for combining a dataset of simulated falls and non-falls, obtained from young volunteers, with the real-world FARSEEING dataset, in order to train a set of supervised classifiers for discriminating between falls and non-falls events. The objective is to analyze if a combination of simulated and real falls could enrich the model. In the real-world, falls are a sporadic event, which results in imbalanced datasets. In this work, several methods for imbalance learning were employed: SMOTE, Balance Cascade and Ranking models. The Balance Cascade obtained less misclassifications in the validation set.There was an improvement when mixing the real falls and simulated non-falls compared to the case when only simulated falls were used for training. When testing with a mixed set with real falls and simulated non-falls, it is even more important to train with a mixed set. Moreover, it was possible to onclude that a model trained with simulated falls generalize better when tested with real falls, than the opposite. The overall accuracy obtained for the combination of different datasets were above 95 %.

Abstract
Ordinal arrangement of objects is a common property in biomedical images. Traditional methods to deal with semantic image segmentation in this setting are ad-hoc and application specific. In this paper, we propose ordinal-aware deep learning architectures for image segmentation that enforce pixelwise consistency by construction. We validated the proposed architectures on several real-life biomedical datasets and achieved competitive results in all cases.

Abstract
We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology. The method is based on a mixture of shared hidden Markov models (HMMs), which are jointly trained in order to exploit the knowledge of the graph structure and in such a way that the obtained mixtures tend to be sparse. Experiments in different application domains demonstrate the effectiveness and versatility of the method.

Abstract
Rat’s gait analysis plays an important role in the assessment of the impact of certain drugs on the treatment of osteoarthritis. Since movement-evoked pain is an early characteristic of this degenerative joint disease, the affected animal modifies its behavior to protect the injured joint from load while walking, altering its gait’s parameters, which can be detected through a video analysis. Because commercially available video-based gait systems still present many limitations, researchers often choose to develop a customized system for the acquisition of the videos and analyze them manually, a laborious and time-consuming task prone to high user variability. Therefore, and bearing in mind the recent advances in machine learning and computer vision fields, as well as their presence in many tracking and recognition applications, this work is driven by the need to find a solution to automate the detection and quantification of the animal’s gait changes making it an easier, faster, simpler and more robust task. Thus, a comparison between different methodologies to detect and segment the animal under degraded luminance conditions is presented in this paper as well as an algorithm to detect, segment and classify the animal’s paws.

Abstract
Fingerprint recognition has been widely studied for more than 45 years and yet it remains an intriguing pattern recognition problem. This paper focuses on the foreground mask estimation which is crucial for the accuracy of a fingerprint recognition system. The method consists of a robust cluster-based fingerprint segmentation framework incorporating an additional step to deal with pixels that were rejected as foreground in a decision considered not reliable enough. These rejected pixels are then further analysed for a more accurate classification. The procedure falls in the paradigm of classification with reject option- a viable option in several real world applications of machine learning and pattern recognition, where the cost of misclassifying observations is high. The present work expands a previous method based on the fuzzy C-means clustering with two variations regarding: i) the filters used; and ii) the clustering method for pixel classification as foreground/background. Experimental results demonstrate improved results on FVC datasets comparing with state-of-the-art methods even including methodologies based on deep learning architectures.

Abstract
Traditional image segmentation algorithms operate by iteratively working over an image, as if refining a segmentation until a stopping criterion is met. Deep learning has replaced traditional approaches, achieving state-of-the-art performance in many problems, one of them being image segmentation. However, the concept of segmentation refinement is not present anymore, since usually the images are segmented in a single step. This work focuses on the refinement of image segmentations using deep convolutional neural networks, with the addition of a quality prediction output. The output from a state-of-the-art base segmenter is refined, simultaneously improving it and trying to predict its quality. We show that the quality concept can be used as a regularizer while training a network for direct segmentation refinement.