103 research outputs found
Stroke Based Painterly Rendering
International audienceMany traditional art forms are produced by an artist sequentially placing a set of marks, such as brush strokes, on a canvas. Stroke based Rendering (SBR) is inspired by this process, and underpins many early and contemporary Artistic Stylization algorithms. This Chapter outlines the origins of SBR, and describes key algorithms for placement of brush strokes to create painterly renderings from source images. The chapter explores both local greedy, and global optimization based approaches to stroke placement. The issue of creative control in SBR is also briefly discussed
Screen codes: efficient data transfer from video displays to mobile devices
We present âScreen codesâ - a space- and time-efficient, aesthetically compelling method for transferring data from a display to a camera-equipped mobile device. Screen codes encode data as a grid of luminosity fluctuations within an arbitrary image, displayed on the video screen and decoded on a mobile device. These âtwinklingâ images are a form of âvisual hyperlinkâ, by which users can move dynamically generated content to and from their mobile devices. They help bridge the âcontent divideâ between mobile and fixed computing
ComeHere: Exploiting ethereum for secure sharing of health-care data
The problem of protecting sensitive data like medical records, and enabling the access only to authorized entities is currently a challenge. Current solutions often require trusting some centralized entity which is in charge of managing the data. The disruptive technology of blockchains may offer the possibility to change the current scenario and give to the users the control on their personal data. In this paper we propose ComeHere, a system able to store medical records and to exploit the blockchain technology to control and track the access right transfer on the blockchain. The paper shows the current status of the project, presents a preliminary proof-of-concept implementation and discusses the future improvements of the system, and some critical issues which are still open.Engineering and Physical Sciences Research Council (EPSRC)BioBeats Group Lt
Skeletons from Sketches of Dancing Poses
Abstract-The contribution of this paper is a sketch parser able to recognize the several components of a skeleton described using the drawing of a stick-man. We describe the sketch parser in detail, and briefly outline how it is applied to form the frontend of a sketch based retrieval system capable of searching for human poses in archival dance footage
A phase field method for tomographic reconstruction from limited data
Classical tomographic reconstruction methods fail for problems in which there is extreme temporal and spatial sparsity in the measured data. Reconstruction of coronal mass ejections (CMEs), a space weather phenomenon with potential negative effects on the Earth, is one such problem. However, the topological complexity of CMEs renders recent limited data reconstruction methods inapplicable. We propose an energy function, based on a phase field level set framework, for the joint segmentation and tomographic reconstruction of CMEs from measurements acquired by coronagraphs, a type of solar telescope. Our phase field model deals easily with complex topologies, and is more robust than classical methods when the data are very sparse. We use a fast variational algorithm that combines the finite element method with a trust region variant of Newtonâs method to minimize the energy. We compare the results obtained with our model to classical regularized tomography for synthetic CME-like images
Deep learning with wearable based heart rate variability for prediction of mental and general health
This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordData Availability:
The data collected in this study resides in a secure network and access to data for further analysis would require further ethics approval due to the data containing sensitive participant information, but may be available upon request.The ubiquity and commoditisation of wearable biosensors (fitness bands) has led to a deluge of personal healthcare data, but with limited analytics typically fed back to the user. The feasibility of feeding back more complex, seemingly unrelated measures to users was investigated, by assessing whether increased levels of stress, anxiety and depression (factors known to affect cardiac function) and general health measures could be accurately predicted using heart rate variability (HRV) data from wrist wearables alone. Levels of stress, anxiety, depression and general health were evaluated from subjective questionnaires completed on a weekly or twice-weekly basis by 652 participants. These scores were then converted into binary levels (either above or below a set threshold) for each health measure and used as tags to train Deep Neural Networks (LSTMs) to classify each health measure using HRV data alone. Three data input types were investigated: time domain, frequency domain and typical HRV measures. For mental health measures, classification accuracies of up to 83% and 73% were achieved, with five and two minute HRV data streams respectively, showing improved predictive capability and potential future wearable use for tracking stress and well-being.Engineering and Physical Sciences Research Council (EPSRC
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