Keyframe detection in visual lifelogs

The SenseCam is a wearable camera that passively captures images. Therefore, it requires no conscious effort by a user in taking a photo. A Visual Diary from such a source could prove to be a valuable tool in assisting the elderly, individuals with neurodegenerative diseases, or other traumas. One issue with Visual Lifelogs is the large volume of image data generated. In previous work we spit a day's worth of images into more manageable segments, i.e. into distinct events or activities. However, each event coud stil consist of 80-100 images. thus, in this paper we propose a novel approach to selecting the key images within an event using a combination of MPEG-7 and Scale Invariant Feature Transform (SIFT) features

The colour of life: novel visualisations of population Lifestyles

Colour permeates our daily lives, yet we rarely take notice of it. In this work we utilise the SenseCam (a visual lifelogging tool), to investigte the predominant colours in one million minutes of human life that a group of 20 individuals encounter throughout their normal daily activities. We also compare the colours that different groups of people are exposed to in their typical days. This information is presented in using a novel colour-wheel visualisation which is a new means of illustrating that people are exposed to bright colours over longer durations of time during summer months, and more dark colours during winter months

The colour of life: interacting with SenseCam images on large multi-touch display walls

A SenseCam can provide a detailed visual archive of a person’s life, activities and experiences. However, as the number of images captured per year can extend beyond one million, gaining an insight into an individual’s lifestyle in a fast, effective and intuitive manner is a challenging prospect. In this work, we develop an interactive image browsing tool, which incorporates visualisation techniques that can capture not only a snapshot of an individual’s lifestyle over long periods of time, but also how that lifestyle varies with changing days, weeks, or years. The image retrieval tool incorporates the Colour of Life algorithms [1], which can represent an overview of millions of images with a single visualisation. The Colour of Life algorithms focus on the relationship between lifestyle and colour, by capturing the colours to which we are exposed in our lives (and therefore captured by SenseCam images), collating similar colours for specific time periods and depicting how those colours change over time with a flowing time-line – see Figure 1 which depicts the life of a SenseCam user over the period of 8 days. In this figure, time is orientated along the horizontal axis and larger vertical peaks indicate higher user activity for a given period of time. In Figure 1, the normal working week consists of the rhythmical blue, pink (work) and yellow (home) peaks and troughs for each day (with less activity at the start and end of the days), whereas time outdoors increases at the weekend, especially during the night (and hence the darker colours on the left hand side of the figure). The Colour of Life visualisation, while providing information on changes in lifestyle, does not provide sufficient context to understand the exact activities of a user for a given time period. For example, on the left of Figure 1 there is a peak of purple, that does not occur anywhere else during the 8 days of activities images – where was the user at this point in time and what was he doing? In this work, we build an interactive image browsing tool based around the Colour of Life visualisation. We exploit the use of high resolution multi-touch display walls, where we extend the Colour of Life algorithms to produce an intuitive visualisation, which incorporates image mosaicing (see Figure 2). Through this we incorporate coarse lifestyle data with more fine detailed contextual information on human activities into one interactive visualisation tool. As an additional feature, we have investigated the use of image classification within the framework of the Colour of Life. One such example is the categorisation of images as being as social (i.e. interacting with other people) or non-social. Using such a classification, we can depict a person’s social lifestyle, and how that varies over time

Chromatin Is Frequently Unknotted at the Megabase Scale.

Knots in the human genome would greatly impact diverse cellular processes ranging from transcription to gene regulation. To date, it has not been possible to directly examine the genome in vivo for the presence of knots. Recently, methods for serial fluorescent in situ hybridization have made it possible to measure the three-dimensional position of dozens of consecutive genomic loci in vivo. However, the determination of whether genomic trajectories are knotted remains challenging because small errors in the localization of a single locus can transform an unknotted trajectory into a highly knotted trajectory and vice versa. Here, we use stochastic closure analysis to determine if a genomic trajectory is knotted in the setting of experimental noise. We analyze 4727 deposited genomic trajectories of a 2-Mb-long chromatin interval from human chromosome 21. For 243 of these trajectories, their knottedness could be reliably determined despite the possibility of localization errors. Strikingly, in each of these 243 cases, the trajectory was unknotted. We note a potential source of bias insofar as knotted contours may be more difficult to reliably resolve. Nevertheless, our data are consistent with a model in which, at the scales probed, the human genome is often free of knots

Experiences of aiding autobiographical memory Using the SenseCam

Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible

Effects of environmental colour on mood: a wearable life colour capture device

Colour is everywhere in our daily lives and impacts things like our mood, yet we rarely take notice of it. One method of capturing and analysing the predominant colours that we encounter is through visual lifelogging devices such as the SenseCam. However an issue related to these devices is the privacy concerns of capturing image level detail. Therefore in this work we demonstrate a hardware prototype wearable camera that captures only one pixel - of the dominant colour prevelant in front of the user, thus circumnavigating the privacy concerns raised in relation to lifelogging. To simulate whether the capture of dominant colour would be sufficient we report on a simulation carried out on 1.2 million SenseCam images captured by a group of 20 individuals. We compare the dominant colours that different groups of people are exposed to and show that useful inferences can be made from this data. We believe our prototype may be valuable in future experiments to capture colour correlated associated with an individual's mood

Detecting lies about consumer attitudes using the timed antagonistic response alethiometer.

The Timed Antagonistic Response Alethiometer (TARA) is a true-false statement classification task that diagnoses lying on the basis of slower average response speeds. Previous research (Gregg in Applied Cognitive Psychology, 21, 621-647, 2007) showed that a computer-based TARA was about 80 % accurate when its statements conveyed demographic facts or religious views. Here, we tested the TARA's diagnostic potential when its statements conveyed attitudes-here, toward both branded and generic consumer products-across different versions of the TARA (Exps. 1a, 1b, and 1c), as well as across consecutive administrations (Exp. 2). The results generalized well across versions, and maximal accuracy rates exceeding 80 % were obtained, although accuracy declined somewhat upon readministration. Overall, the TARA shows promise as a comparatively cheap, convenient, and diagnostic index of lying about attitudes

Smart tablecloths - ambient feedback of domestic electricity consumption

In this work we discuss the topic of ambiently informing individuals of their home electricity usage, with the ultimate goal being to induce positive change and reduction in users’energy usage. We believe that simple ambient feedback, integrated into the surroundings as the colour of a home textile, may provide a powerful motivator in better raising awareness of electricity comsumption. This demonstrator shows the use of an illuminated colour-changing fabric to provide feedback on realtime energy use

Increasing trust in new data sources: crowdsourcing image classification for ecology

Crowdsourcing methods facilitate the production of scientific information by non-experts. This form of citizen science (CS) is becoming a key source of complementary data in many fields to inform data-driven decisions and study challenging problems. However, concerns about the validity of these data often constrain their utility. In this paper, we focus on the use of citizen science data in addressing complex challenges in environmental conservation. We consider this issue from three perspectives. First, we present a literature scan of papers that have employed Bayesian models with citizen science in ecology. Second, we compare several popular majority vote algorithms and introduce a Bayesian item response model that estimates and accounts for participants' abilities after adjusting for the difficulty of the images they have classified. The model also enables participants to be clustered into groups based on ability. Third, we apply the model in a case study involving the classification of corals from underwater images from the Great Barrier Reef, Australia. We show that the model achieved superior results in general and, for difficult tasks, a weighted consensus method that uses only groups of experts and experienced participants produced better performance measures. Moreover, we found that participants learn as they have more classification opportunities, which substantially increases their abilities over time. Overall, the paper demonstrates the feasibility of CS for answering complex and challenging ecological questions when these data are appropriately analysed. This serves as motivation for future work to increase the efficacy and trustworthiness of this emerging source of data.Comment: 25 pages, 10 figure

Passively recognising human activities through lifelogging

Lifelogging is the process of automatically recording aspects of one’s life in digital form. This includes visual lifelogging using wearable cameras such as the SenseCam and in recent years many interesting applications for this have emerged and are being actively researched. One of the most interesting of these, and possibly the most far-reaching, is using visual lifelogs as a memory prosthesis but there are also applications in job-specific activity recording, general lifestyle analysis and market analysis. In this work we describe a technique which allowed us to develop automatic classifiers for visual lifelogs to infer different lifestyle traits or characteristics. Their accuracy was validated on a set of 95 k manually annotated images and through one-on-one interviews with those who gathered the images. These automatic classifiers were then applied to a collection of over 3 million lifelog images collected by 33 individuals sporadically over a period of 3.5 years. From this collection we present a number of anecdotal observations to demonstrate the future potential of lifelogging to capture human behaviour. These anecdotes include: the eating habits of office workers; to the amount of time researchers spend outdoors through the year; to the observation that retired people in our study appear to spend quite a bit of time indoors eating with friends. We believe this work demonstrates the potential of lifelogging techniques to assist behavioural scientists in future