Using ORB, BoW and SVM to identify and track tagged Norway lobster Nephrops norvegicus (L.)

Sustainable capture policies of many species strongly depend on the understanding of their social behaviour. Nevertheless, the analysis of emergent behaviour in marine species poses several challenges. Usually animals are captured and observed in tanks, and their behaviour is inferred from their dynamics and interactions. Therefore, researchers must deal with thousands of hours of video data. Without loss of generality, this paper proposes a computer vision approach to identify and track specific species, the Norway lobster, Nephrops norvegicus. We propose an identification scheme were animals are marked using black and white tags with a geometric shape in the center (holed triangle, filled triangle, holed circle and filled circle). Using a massive labelled dataset; we extract local features based on the ORB descriptor. These features are a posteriori clustered, and we construct a Bag of Visual Words feature vector per animal. This approximation yields us invariance to rotation and translation. A SVM classifier achieves generalization results above 99%. In a second contribution, we will make the code and training data publically available.Peer Reviewe

Limbs detection and tracking of head-fixed mice for behavioral phenotyping using motion tubes and deep learning

The broad accessibility of affordable and reliable recording equipment and its relative ease of use has enabled neuroscientists to record large amounts of neurophysiological and behavioral data. Given that most of this raw data is unlabeled, great effort is required to adapt it for behavioral phenotyping or signal extraction, for behavioral and neurophysiological data, respectively. Traditional methods for labeling datasets rely on human annotators which is a resource and time intensive process, which often produce data that that is prone to reproducibility errors. Here, we propose a deep learning-based image segmentation framework to automatically extract and label limb movements from movies capturing frontal and lateral views of head-fixed mice. The method decomposes the image into elemental regions (superpixels) with similar appearance and concordant dynamics and stacks them following their partial temporal trajectory. These 3D descriptors (referred as motion cues) are used to train a deep convolutional neural network (CNN). We use the features extracted at the last fully connected layer of the network for training a Long Short Term Memory (LSTM) network that introduces spatio-temporal coherence to the limb segmentation. We tested the pipeline in two video acquisition settings. In the first, the camera is installed on the right side of the mouse (lateral setting). In the second, the camera is installed facing the mouse directly (frontal setting). We also investigated the effect of the noise present in the videos and the amount of training data needed, and we found that reducing the number of training samples does not result in a drop of more than 5% in detection accuracy even when as little as 10% of the available data is used for training

Proyectos de los estudiantes para potenciar el aprendizaje móvil en la educación superior

Les institucions d’ensenyament afronten el repte d’oferir als estudiants eines per a l’aprenentatge mòbil (m-learning). No obstant això, l’evolució de les tecnologies fa que el desenvolupament i la millora contínua d’aquestes eines sigui una cosa molt costosa. Per exemple, és complicat avaluar les diferents alternatives tecnològiques disponibles i seleccionar la més apropiada segons el context. En aquest article, es proposa com a solució implicar els estudiants de titulacions tecnològiques en el desenvolupament d’eines d’m-learning. S’analitza com a cas d’estudi la Universitat Oberta de Catalunya i es presenten exemples d’eines desenvolupades per estudiants com a part del seu treball final de carrera. Aquests treballs permeten explorar diferents tecnologies i proporcionen informació útil per a guiar la inversió institucional en el desenvolupament d’eines d’m-learning. Així, doncs, aquest paradigma, proper al model del desenvolupament col·laboratiu en el programari lliure, permet assegurar la sostenibilitat de l’m-learning en institucions d’ensenyament.Educational institutions are facing the challenge of providing students with tools for mobile learning (m-learning). However, the evolution of technology makes the development and continuous improvement of these tools rather expensive. For example, it is difficult to assess the different technology options available and to choose which ones are best suited to a particular context. In this article, the proposed solution is to engage students on technology degree courses in the development of m-learning tools. The Open University of Catalonia (UOC) is analyzed as a case study, and several examples of tools developed by students as part of their final year projects are presented. These projects explore different technologies and provide useful information to guide institutional investment in the development of m-learning tools. Akin to the collaborative development model in the field of open source software, this paradigm therefore can ensure the sustainability of m-learning in educational institutions.Las instituciones educativas se enfrentan al reto de ofrecer a los estudiantes herramientas para aprendizaje móvil (m-learning). Sin embargo, la evolución de las tecnologías hace que el desarrollo y la mejora continua de estas herramientas sea algo muy costoso. Por ejemplo, resulta complicado evaluar las diferentes alternativas tecnológicas disponibles y seleccionar la más apropiada según el contexto. En este artículo, se propone como solución implicar a los estudiantes de titulaciones tecnológicas en el desarrollo de herramientas de m-learning. Se analiza como caso de estudio la Universitat Oberta de Catalunya y se presentan ejemplos de herramientas desarrolladas por estudiantes como parte de su trabajo final de carrera. Estos trabajos permiten explorar diferentes tecnologías y proporcionan información útil para guiar la inversión institucional en el desarrollo de herramientas de m-learning. Así pues, este paradigma, cercano al modelo del desarrollo colaborativo en el software libre, permite asegurar la sostenibilidad del m-learning en instituciones educativas

Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems

Regression problems have been widely studied in machinelearning literature resulting in a plethora of regression models and performance measures. However, there are few techniques specially dedicated to solve the problem of how to incorporate categorical features to regression problems. Usually, categorical feature encoders are general enough to cover both classification and regression problems. This lack of specificity results in underperforming regression models. In this paper,we provide an in-depth analysis of how to tackle high cardinality categor-ical features with the quantile. Our proposal outperforms state-of-the-encoders, including the traditional statistical mean target encoder, when considering the Mean Absolute Error, especially in the presence of long-tailed or skewed distributions. Besides, to deal with possible overfitting when there are categories with small support, our encoder benefits from additive smoothing. Finally, we describe how to expand the encoded values by creating a set of features with different quantiles. This expanded encoder provides a more informative output about the categorical feature in question, further boosting the performance of the regression model.Comment: Accepted at The 18th International Conference on Modeling Decisions for Artificial Intelligence (MDAI

Using ORB, BoW and SVM to identify and track tagged Norway lobster Nephrops norvegicus (L.)

7th International Workshop on Marine Technology – Martech Workshop 2016, 26-28 October 2016, Barcelona.-- 3 pages, 4 figures, 1 tableSustainable capture policies of many species strongly depend on the understanding of their social behaviour. Nevertheless, the analysis of emergent behaviour in marine species poses several challenges. Usually animals are captured and observed in tanks, and their behaviour is inferred from their dynamics and interactions. Therefore, researchers must deal with thousands of hours of video data. Without loss of generality, this paper proposes a computer vision approach to identify and track specific species, the Norway lobster, Nephrops norvegicus. We propose an identification scheme were animals are marked using black and white tags with a geometric shape in the center (holed triangle, filled triangle, holed circle and filled circle). Using a massive labelled dataset; we extract local features based on the ORB descriptor. These features are a posteriori clustered, and we construct a Bag of Visual Words feature vector per animal. This approximation yields us invariance to rotation and translation. A SVM classifier achieves generalization results above 99%. In a second contribution, we will make the code and training data publically availablePeer Reviewe