Prospect on intergalactic magnetic field measurements with gamma-ray instruments

Observing high-energy gamma-rays from Active Galactic Nuclei (AGN) offers a unique potential to probe extremely tiny values of the intergalactic magnetic field (IGMF), a long standing question of astrophysics, astropa rticle physics and cosmology. Very high energy (VHE) photons from blazars propagating along the line of sight interact with the extragalactic background light (EBL) and produce e + e − pairs. Through inverse-Compton interaction, mainly on the cosmic microwave background (CMB), these pairs generate secondary GeV-TeV compo- nents accompanying the primary VHE signal. Such secondary components would be detected in the gamma-ray range as delayed “pair echos” for very weak IGMF ( B< 10 − 16 G ), while they should result in a spatially extended ga mma-ray emission around the source for higher IGMF values ( B> 10 − 16 G ). Coordinated observations with space (i.e. Fermi) and ground- based gamma-ray instruments, such as the pre sent Cherenkov experiments H.E.S.S., MAGIC and VERITAS, the future Cherenkov Telescope Array (CTA) Observatory, and the wide-field detectors such as HAWC and LHAASO, should allow to analyze and finally detect such echos, extended emission or pair halos, and to further characterize the IGMF.

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.

Combining face and facial feature detectors for face detection performance improvement

[EN]In this paper, we experimentally study the combination of face and facial feature detectors to improve face detection performance. The face detection problem, as suggeted by recent face detection challenges, is still not solved. Face detectors traditionally fail in large-scale problems and/or when the face is occluded or di erent head rotations are present. The combination of face and facial feature detectors is evaluated with a public database. The obtained results evidence an improvement in the positive detection rate while reducing the false detection rate. Additionally, we prove that the integration of facial feature detectors provides useful information for pose estimation and face alignment.