Combining face detection and novelty to identify important events in a visual lifelog

The SenseCam is a passively capturing wearable camera, worn around the neck and takes an average of almost 2,000 images per day, which equates to over 650,000 images per year. It is used to create a personal lifelog or visual recording of the wearer’s life and generates information which can be helpful as a human memory aid. For such a large amount of visual information to be of any use, it is accepted that it should be structured into “events”, of which there are about 8,000 in a wearer’s average year. In automatically segmenting SenseCam images into events, it is desirable to automatically emphasise more important events and decrease the emphasis on mundane/routine events. This paper introduces the concept of novelty to help determine the importance of events in a lifelog. By combining novelty with face-to-face conversation detection, our system improves on previous approaches. In our experiments we use a large set of lifelog images, a total of 288,479 images collected by 6 users over a time period of one month each

Structuring and augmenting a visual personal diary

This paper refers to research in the domain of visual lifelogging, whereby individuals capture much of their lives using digital cameras. The potential benefits of lifelogging include: applications to review tourist trips, memory aid applications, learning assistants, etc. The SenseCam, developed by Microsoft Research in Cambridge, UK, is a small wearable device which incorporates a digital camera and onboard sensors (motion, ambient temperature, light level, and passive infrared to detect presence of people). There exists a number of challenges in managing the vast quantities of data generated by lifelogging devices such as the SenseCam. Our work concentrates on the following areas withing visual lifelogging: Segmenting sequences of images into events (e.g. breakfast, at meeting); retrieving similar events (what other times was I at the park?); determining most important events (meeting an old friend is more important than breakfast); selection of ideal keyframe to provide an event summary; and augmenting lifeLog events with images taken by millions of users from "Web 2.0" websites (show me other pictures of the Statue of Liberty to augment my own lifelog images)

Extremal Quantum Correlations and Cryptographic Security

We investigate a fundamental property of device independent security in quantum cryptography by characterizing probability distributions which are necessarily independent of the measurement results of any eavesdropper. We show that probability distributions that are secure in this sense are exactly the extremal quantum probability distributions. This allows us to give a characterization of security in algebraic terms. We apply the method to common examples for two-party as well as multi-party setups and present a scheme for verifying security of probability distributions with two parties, two measurement settings, and two outcomes.Comment: 7 pages, 2 figures, revised version, accepted for publication in Phys. Rev. Let

Mild acetabular dysplasia and risk of osteoarthritis of the hip : a case-control study

Objective To determine whether mild variation in acetabular depth (AD) and shape is a risk factor for osteoarthritis (OA) of the hip. Methods The unaffected contralateral hip of patients with unilateral hip OA was compared with hips of asymptomatic controls without hip OA, derived from the Nottingham Genetics Osteoarthritis and Lifestyle case–control study. Standardised anteroposterior x-rays of the pelvis were used to measure centre edge (CE) angle and AD. Cut-off points for narrow CE angle and shallow AD were calculated from the control group (mean −1.96×SD). The relative risk of hip OA associated with each feature was estimated using OR and 95% CI and adjusted risks were calculated by logistic regression. Results In controls, both the CE angle and the AD were lower in the left hip than in the right hip. The CE angle related to age in both hips, and AD of the right hip was lower in men than in women. The contralateral unaffected hip in patients with unilateral hip OA had a decreased CE angle and AD compared with controls, irrespective of side. The lowest tertile of the CE angle in contralateral hips was associated with an eightfold risk of OA (aOR 8.06, 95% CI 4.87 to 13.35) and the lowest tertile of AD was associated with a 2.5-fold risk of OA (aOR 2.53, 95% CI 1.28 to 5.00). Significant increases in the risk of OA were also found as the CE angle and AD decreased

Video shot boundary detection: seven years of TRECVid activity

Shot boundary detection (SBD) is the process of automatically detecting the boundaries between shots in video. It is a problem which has attracted much attention since video became available in digital form as it is an essential pre-processing step to almost all video analysis, indexing, summarisation, search, and other content-based operations. Automatic SBD was one of the tracks of activity within the annual TRECVid benchmarking exercise, each year from 2001 to 2007 inclusive. Over those seven years we have seen 57 different research groups from across the world work to determine the best approaches to SBD while using a common dataset and common scoring metrics. In this paper we present an overview of the TRECVid shot boundary detection task, a high-level overview of the most significant of the approaches taken, and a comparison of performances, focussing on one year (2005) as an example

Automatically detecting important moments from everyday life using a mobile device

This paper proposes a new method to detect important moments in our lives. Our work is motivated by the increase in the quantity of multimedia data, such as videos and photos, which are capturing life experiences into personal archives. Even though such media-rich data suggests visual processing to identify important moments, the oft-mentioned problem of the semantic gap means that users cannot automatically identify or retrieve important moments using visual processing techniques alone. Our approach utilises on-board sensors from mobile devices to automatically identify important moments, as they are happening

Summarisation & Visualisation of Large Volumes of Time-Series Sensor Data

a number of sensors, including an electricity usage sensor supplied by Episensor. This poses our second With the increasing ubiquity of sensor data, challenge, how to summarise an extended period of presenting this data in a meaningful way to electrictiy usage data for a home user. users is a challenge that must be addressed before we can easily deploy real-world sensor network interfaces in the home or workplace. In this paper, we will present one solution to the visualisation of large quantities of sensor data that is easy to understand and yet provides meaningful and intuitive information to a user, even when examining many weeks or months of historical data. We will illustrate this visulalisation technique with two real-world deployments of sensing the person and sensing the home

Optimal tracking for pairs of qubit states

In classical control theory, tracking refers to the ability to perform measurements and feedback on a classical system in order to enforce some desired dynamics. In this paper we investigate a simple version of quantum tracking, namely, we look at how to optimally transform the state of a single qubit into a given target state, when the system can be prepared in two different ways, and the target state depends on the choice of preparation. We propose a tracking strategy that is proved to be optimal for any input and target states. Applications in the context of state discrimination, state purification, state stabilization and state-dependent quantum cloning are presented, where existing optimality results are recovered and extended.Comment: 15 pages, 8 figures. Extensive revision of text, optimality results extended, other physical applications include

Mining user activity as a context source for search and retrieval

Nowadays in information retrieval it is generally accepted that if we can better understand the context of users then this could help the search process, either at indexing time by including more metadata or at retrieval time by better modelling the user context. In this work we explore how activity recognition from tri-axial accelerometers can be employed to model a user's activity as a means of enabling context-aware information retrieval. In this paper we discuss how we can gather user activity automatically as a context source from a wearable mobile device and we evaluate the accuracy of our proposed user activity recognition algorithm. Our technique can recognise four kinds of activities which can be used to model part of an individual's current context. We discuss promising experimental results, possible approaches to improve our algorithms, and the impact of this work in modelling user context toward enhanced search and retrieval

Harnessing Science to Solve Global Poverty and Hunger

Text of the Sir John Crawford Memorial Lecture delivered by Peter Doherty, Nobel Laureate, Chairman of the Immunology Department at St. Jude Children's Research Hospital, during CGIAR International Centers Week 1998. Doherty describes the long term nature of research on scientifically complex subjects like AIDS and East Coast Fever, and the challenges of managing this research in a way that permits scientists to pursue long term solutions rather than less consequential, shorter term results. He encourages donors to provide the kind of sustained, unrestricted support that enables CGIAR Centers to pursue long-term objectives that are intrinsically uncertain but that are fundamental to substantive scientific progress