The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

Special Section on Attacking and Protecting Artificial Intelligence

Modern artificial intelligence systems largely rely on advanced algorithms, including machine learning techniques such as deep learning. The research community has invested significant efforts in understanding these algorithms, optimally tuning them, and improving their performance, but it has mostly neglected the security facet of the problem. Recent attacks and exploits demonstrated that machine learning-based algorithms are susceptible to attacks targeting computer systems, including backdoors, hardware trojans and fault attacks, but are also susceptible to a range of attacks specifically targeting them, such as adversarial input perturbations. Implementations of machine learning algorithms are often crucial proprietary assets for companies thus need to be protected. It follows that implementations of artificial intelligence-based algorithms are an attractive target for piracy and illegitimate use and, as such, they need to be protected as all other IPs. This is equally important for machine learning algorithms running on remote servers vulnerable to micro-architectural exploits.Published versio

Tracking Using Continuous Shape Model Learning in the Presence of Occlusion

This paper presents a Bayesian framework for a new model-based learning method, which is able to track nonrigid objects in the presence of occlusions, based on a dynamic shape description in terms of a set of corners. Tracking is done by estimating the new position of the target in a multimodal voting space. However, occlusion events and clutter may affect the model learning, leading to a distraction in the estimation of the new position of the target as well as incorrect updating of the shape model. This method takes advantage of automatic decisions regarding how to learn the model in different environments, by estimating the possible presence of distracters and regulating corner updating on the basis of these estimations. Moreover, by introducing the corner feature vector classification, the method is able to continue learning the model dynamically, even in such situations. Experimental results show a successful tracking along with a more precise estimation of shape and motion during occlusion events

Use of Time-Frequency Analysis and Neural Networks for Mode Identification in a Wireless Software-Defined Radio Approach

The use of time-frequency distributions is proposed as a nonlinear signal processing technique that is combined with a pattern recognition approach to identify superimposed transmission modes in a reconfigurable wireless terminal based on software-defined radio techniques. In particular, a software-defined radio receiver is described aiming at the identification of two coexistent communication modes: frequency hopping code division multiple access and direct sequence code division multiple access. As a case study, two standards, based on the previous modes and operating in the same band (industrial, scientific, and medical), are considered: IEEE WLAN 802.11b (direct sequence) and Bluetooth (frequency hopping). Neural classifiers are used to obtain identification results. A comparison between two different neural classifiers is made in terms of relative error frequency

Stabilisierung sub- und pertrochantärer Femurfrakturen mit dem PFNΑ®

Zusammenfassung: Operationsziel: Primär belastungsstabile Osteosynthese per- und subtrochantärer Femurfrakturen mit intramedullärem Kraftträger, besonders auch in osteoporotischem Knochen. Rasche Wiederherstellung der Anatomie und Funktion des verletzten Beins. Indikationen: Sämtliche per- und subtrochantäre Frakturen der AO-Klassifikation 31-A. Kontraindikationen: Offene Wachstumsfugen und ungeeignete Femuranatomie (pathologische Antekurvation bzw. fehlverheilte Schaftfrakturen). Operationstechnik: Wenn möglich geschlossene, bei Bedarf offene Reposition der Faktur auf dem Extensionstisch. Intramedulläre, unaufgebohrte Nagelung und Frakturfixation durch Einbringen einer Spiralklinge über einen Führungsdraht in das Kopf-Halsfragment. Möglichkeit zur dynamischen oder statischen Verriegelung im Femurschaft. Operative Nachsorge: Rasche Mobilisation ab dem ersten postoperativen Tag mit schmerzadaptierter Vollbelastung. Thromboseprophylaxe für 6Wochen mit Fondaparinux, Rivaroxaban oder niedermolekularem Heparin (NMH), alternativ orale Antikoagulation. Ergebnisse: Im Rahmen einer AO-Multizenterstudie an 11 europäischen Kliniken wurden zwischen April 2004 und Juni 2005 313Patienten (Durchschnittsalter 80,6Jahre, 77% Frauen, 23% Männer) mit 315 instabilen pertrochantären Frakturen mittels PFNΑ® ("proximal femoral nail antirotation") operativ stabilisiert [24]. Bei 82% handelte es sich um 31-A2-Frakturen, bei 18% um 31-A3-Frakturen. Die durchschnittliche Operationszeit betrug 56min für die A2-Frakturen und 66min für die A3-Frakturen. Die durchschnittliche Liegedauer im Akutspital betrug 12Tage. Bei 72% der Patienten konnte ein Repositions- und Stabilisierungsergebnis erreicht werden, welches eine unmittelbare postoperative Vollbelastung erlaubte. Insgesamt wurden 165Komplikationen beobachtet, 117 davon waren nicht auf das Implantat zu beziehen. 46 operationsbedingte Komplikationen führten zu 28 ungeplanten Re-Operationen (u.a. 7Femurschaftfrakturen, 4 azetabuläre Penetrationen). 56% der Patienten konnten über ein ganzes Jahr nachkontrolliert werden. Nach einem Jahr waren 89% der Frakturen konsolidiert. Die höchsten Komplikationsraten wiesen Frakturen der Morphologie 31-A2.3 sowie Patienten älter als 90Jahre auf. Mit dem PFNA® wurde damit eine mit den Resultaten anderer intra- und extramedullärer Implantate vergleichbare Anzahl operationsbedingter Komplikationen (14,6%) beschriebe

Comparison among Cognitive Radio Architectures for Spectrum Sensing

Recently, the growing success of new wireless applications and services has led to overcrowded licensed bands, inducing the governmental regulatory agencies to consider more flexible strategies to improve the utilization of the radio spectrum. To this end, cognitive radio represents a promising technology since it allows to exploit the unused radio resources. In this context, the spectrum sensing task is one of the most challenging issues faced by a cognitive radio. It consists of an analysis of the radio environment to detect unused resources which can be exploited by cognitive radios. In this paper, three different cognitive radio architectures, namely, stand-alone single antenna, cooperative and multiple antennas, are proposed for spectrum sensing purposes. These architectures implement a relatively fast and reliable signal processing algorithm, based on a feature detection technique and support vector machines, for identifying the transmissions in a given environment. Such architectures are compared in terms of detection and classification performances for two transmission standards, IEEE 802.11a and IEEE 802.16e. A set of numerical simulations have been carried out in a challenging scenario, and the advantages and disadvantages of the proposed architectures are discussed

A fast cardiac electromechanics model coupling the Eikonal and the nonlinear mechanics equations

We present a new model of human cardiac electromechanics for the left ventricle where electrophysiology is described by a Reaction-Eikonal model and which enables an off-line resolution of the reaction model, thus entailing a big saving of computational time. Subcellular dynamics is coupled with a model of tissue mechanics, which is in turn coupled with a Windkessel model for blood circulation. Our numerical results show that the proposed model is able to provide a physiological response to changes in certain variables (end-diastolic volume, total peripheral resistance, contractility). We also show that our model is able to reproduce with high accuracy and with a considerably lower computational time the results that we would obtain if the monodomain model should be used in place of the Eikonal model

Abnormality detection using graph matching for multi-task dynamics of autonomous systems

Self-learning abilities in autonomous systems are essential to improve their situational awareness and detection of normal/abnormal situations. In this work, we propose a graph matching technique for activity detection in autonomous agents by using the Gromov-Wasserstein framework. A clustering approach is used to discretise continuous agents' states related to a specific task into a set of nodes with similar objectives. Additionally, a probabilistic transition matrix between nodes is used as edges weights to build a graph. In this paper, we extract an abnormal area based on a sub-graph that encodes the differences between coupled of activities. Such sub-graph is obtained by applying a threshold on the optimal transport matrix, which is obtained through the graph matching procedure. The obtained results are evaluated through experiments performed by a robot in a simulated environment and by a real autonomous vehicle moving within a University Campus