Validation of age determination methods and growth studies of the sand sole Pegusa lascaris (Soleidae) from the eastern-central Atlantic

[EN]The age and growth of the sand sole Pegusa lascaris from the Canarian Archipelago were studied from 2107 fish collected between January 2005 and December 2007. To find an appropriate method for age determination, sagittal otoliths were observed by surface-reading and frontal section and the results were compared. The two methods did not differ significantly in estimated age but the surface-reading method is superior in terms of cost and time efficiency. The sand sole has a moderate life span, with ages up to 10 years recorded. Individuals grow quickly in their first two years, attaining approximately 48% of their maximum standard length; after the second year, their growth rate drops rapidly as energy is diverted to reproduction. Males and females show dimorphism in growth, with females reaching a slightly greater length and age than males. Von Bertalanffy, seasonalized von Bertalanfy, Gompertz, and Schnute growth models were fitted to length-at-age data. Akaike weights for the seasonalized von Bertalanffy growth model indicated that the probability of choosing the correct model from the group of models used was >0.999 for males and females. The seasonalized von Bertalanffy growth parameters estimated were: L? = 309 mm standard length, k = 0.166 yr?1, t0 = ?1.88 yr, C = 0.347, and ts = 0.578 for males; and L? = 318 mm standard length, k = 0.164 yr?1, t0 = ?1.653 yr, C = 0.820, and ts = 0.691 for females. Fish standard length and otolith radius are closely correlated (R2 = 0.902). The relation between standard length and otolith radius is described by a power function (a = 85.11, v = 0.906)

An integrated marine data repository based on the arc marine model: a pilot Project in the Canary Islands

Trabajo realizado por Antonio Machado Carrillo, Juan Antonio Bermejo e Ignacio Lorenzo

On the use of simple geometric descriptors provided by RGB-D sensors for re-identification

[EN]The re-identification problem has been commonly accomplished using appearance features based on salient points and color information. In this paper, we focus on the possibilities that simple geometric features obtained from depth images captured with RGB-D cameras may offer for the task, particularly working under severe illumination conditions. The results achieved for different sets of simple geometric features extracted in a top-view setup seem to provide useful descriptors for the re-identification task, which can be integrated in an ambient intelligent environment as part of a sensor network.

On the use of a low-cost thermal sensor to improve kinect people detection in a mobile robot

[EN]Detecting people is a key capability for robots that operate in populated environments. In this paper, we have adopted a hierarchical approach that combines classifiers created using supervised learning in order to identify whether a person is in the view-scope of the robot or not. Our approach makes use of vision, depth and thermal sensors mounted on top of a mobile platform.

A comparison of face and facial feature detectors based on the viola-jones general object detection framework

[EN]The human face provides useful information during interaction; therefore, any system integrating Vision- BasedHuman Computer Interaction requires fast and reliable face and facial feature detection. Different approaches have focused on this ability but only open source implementations have been extensively used by researchers. A good example is the Viola–Jones object detection framework that particularly in the context of facial processing has been frequently used.

Viola-Jones based detectors: How much affects the training set?

[EN]This paper presents a study on the facial feature detection performance achieved using the Viola-Jones framework. A set of classi- ers using two di erent focuses to gather the training samples is created and tested on four di erent datasets covering a wide range of possibili- ties. The results achieved should serve researchers to choose the classi er that better ts their demands.

Improving gender classification accuracy in the wild

[EN]In this paper, we focus on gender recognition in challenging large scale scenarios. Firstly, we review the literature results achieved for the problem in large datasets, and select the currently hardest dataset: The Images of Groups. Secondly, we study the extraction of features from the face and its local context to improve the recognition accuracy. Diff erent descriptors, resolutions and classfii ers are studied, overcoming previous literature results, reaching an accuracy of 89.8%.

Kinship verification in the wild: the first kinship verification competition

In this paper, we present the Kinship Verification in the Wild Competition: the first kinship verification competition which is held in conjunction with the International Joint Conference on Biometrics 2014, Clearwater, Florida, USA. The key goal of this competition is to compare the performance of different methods on a new-collected dataset with the same evaluation protocol and develop the first standardized benchmark for kinship verification in the wild.

Exploring the use of local descriptors for fish recognition in LifeCLEF 2015

[EN]This paper summarizes the proposal made by the SIANI team for the LifeCLEF 2015 Fish task. The approach makes use of standard detection techniques, applying a multiclass SVM based classifier on large enough Regions Of Interest (ROIs) automatically extracted from the provided video frames. The selection of the detection and classification modules is based on the best performance achieved for the validation dataset consisting of 20 annotated videos. For that dataset, the best classification achieved for an ideal detection module, reaches an accuracy around 40%.

The FG 2015 kinship verification in the wild evaluation

[ES]The aim of the Kinship Verification in the Wild Evaluation (held in conjunction with the 2015 IEEE International Conference on Automatic Face and Gesture Recognition, Ljubljana, Slovenia) was to evaluate different kinship verification algorithms. For this task, two datasets were made available and three possible experimental protocols (unsupervised, image-restricted, and image-unrestricted) were designed. Five institutions submitted their results to the evaluation: (i) Politecnico di Torino, Italy; (ii) LIRIS-University of Lyon, France; (iii) Universidad de Las Palmas de Gran Canaria, Spain; (iv) Nanjing University of Aeronautics and Astronautics, China; and (v) Bar Ilan University, Israel. Most of the participants tackled the image-restricted challenge and experimental results demonstrated better kinship verification performance than the baseline methods provided by the organizers.

On using periocular biometric for gender classification in the wild

[EN]Gender information may serve to automatically modulate interaction to the user needs, among other applications. Within the Computer Vision community, gender classification (GC) has mainly been accomplished with the facial pattern. Periocular biometrics has recently attracted researchers attention with successful results in the context of identity recognition. But, there is a lack of experimental evaluation of the periocular pattern for GC in the wild. The aim of this paper is to study the performance of this specific facial area in the currently most challenging large dataset for the problem.

Exploring the use of local binary patterns as focus measure

[EN]In this work local binary patterns based focus measures are presented. Local binary patterns (LBP) have been introduced in computer vision tasks like texture classification or face recognition. In applications where recognition is based on LBP, a computational saving can be achieved with the use of LBP in the focus measures. The behavior of the proposed measures is studied to test if they fulfill the properties of the focus measures and then a comparison with some well know focus measures is carried out in different scenarios.

The design of a syllabus for translation students: learning outcomes in the subjects of spanish language

[EN]In the new design of educational programs in European Higher Education Area (EHEA), what defines a subject it is Learning Outcomes (LO). These LO, as explicit and precise declarations, turn into the center of teaching and learning process. Keeping this change is mind, our research examines the Educational Guides (EG) of Spanish Language (SL) through a list of verbs, according to the graduation of educacionational objectives of Bllom's Taxonomy (2014-2015)

Multi-scale score level fusion of local descriptors for gender classification in the wild

The 2015 FRVT gender classification (GC) report evidences the problems that current approaches tackle in situations with large variations in pose, illumination, background and facial expression. The report suggests that both commercial and research solutions are hardly able to reach an accuracy over 90% for The Images of Groups dataset, a proven scenario exhibiting unrestricted or in the wild conditions. In this paper, we focus on this challenging dataset, stepping forward in GC performance by observing: 1) recent literature results combining multiple local descriptors, and 2) the psychophysics evidences of the greater importance of the ocular and mouth areas to solve this task...