482 research outputs found
Local picture-repetition mode detector for video de-interlacing
The de-interlacing of video material converted from film can be perfect, provided it is possible to recognize the field-pairs that originate from the same film image. Various so-called film-detectors have been proposed for this purpose, mainly in the patent-literature. Typically, these detectors fail in cases where video overlays are merged with film material, or when nonstandard repetition patterns are used. Both problems occur frequently in television broadcast. For these hybrid and/or irregular cases, we propose a detector that can detect different picture-repetition patterns locally in the image. This detector combines fuzzy logic rules and spatio-temporal prediction to arrive at a highly robust decision signal, suitable for pixel-accurate de-interlacing of hybrid and irregular video material. In addition to an evaluation of the performance, the paper also provides a complexity analysis.Peer Reviewe
Local picture-repetition mode detector for video de-interlacing
The de-interlacing of video material converted from film can be perfect, provided it is possible to recognize the field-pairs that originate from the same film image. Various so-called film-detectors have been proposed for this purpose, mainly in the patent-literature. Typically, these detectors fail in cases where video overlays are merged with film material, or when nonstandard repetition patterns are used. Both problems occur frequently in television broadcast. For these hybrid and/or irregular cases, we propose a detector that can detect different picture-repetition patterns locally in the image. This detector combines fuzzy logic rules and spatio-temporal prediction to arrive at a highly robust decision signal, suitable for pixel-accurate de-interlacing of hybrid and irregular video material. In addition to an evaluation of the performance, the paper also provides a complexity analysis
Dense Motion Estimation for Smoke
Motion estimation for highly dynamic phenomena such as smoke is an open
challenge for Computer Vision. Traditional dense motion estimation algorithms
have difficulties with non-rigid and large motions, both of which are
frequently observed in smoke motion. We propose an algorithm for dense motion
estimation of smoke. Our algorithm is robust, fast, and has better performance
over different types of smoke compared to other dense motion estimation
algorithms, including state of the art and neural network approaches. The key
to our contribution is to use skeletal flow, without explicit point matching,
to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this
paper we describe our algorithm in greater detail, and provide experimental
evidence to support our claims.Comment: ACCV201
Subacromial ultrasound guided or systemic steroid injection for rotator cuff disease: randomised double blind study
Objective To compare the effectiveness of ultrasound guided corticosteroid injection in the subacromial bursa with systemic corticosteroid injection in patients with rotator cuff disease
Learning filter functions in regularisers by minimising quotients
Learning approaches have recently become very popular in the field of inverse problems. A large variety of methods has been established in recent years, ranging from bi-level learning to high-dimensional machine learning techniques. Most learning approaches, however, only aim at fitting parametrised models to favourable training data whilst ignoring misfit training data completely. In this paper, we follow up on the idea of learning parametrised regularisation functions by quotient minimisation as established in [3]. We extend the model therein to include higher-dimensional filter functions to be learned and allow for fit- and misfit-training data consisting of multiple functions. We first present results resembling behaviour of well-established derivative-based sparse regularisers like total variation or higher-order total variation in one-dimension. Our second and main contribution is the introduction of novel families of non-derivative-based regularisers. This is accomplished by learning favourable scales and geometric properties while at the same time avoiding unfavourable ones
L∞ Error and Bandwidth Selection for Kernel Density Estimates of Large Data
Kernel density estimates are a robust way to reconstruct a continuous distribution from a discrete point set. Typically their effectiveness is measured either in L1 or L2 error. In this paper we investigate the challenges in using L ∞ (or worst case) error, a stronger measure than L1 or L2. We present efficient solutions to two linked challenges: how to evaluate the L ∞ error between two kernel density estimates and how to choose the bandwidth parameter for a kernel density estimate built on a subsample of a large data set. 1 1
GameUp: Exergames for mobility – a project to keep elderly active
A big challenge for Europe is the demographic
shift towards an aging population. Resources in the health care
sector are limited, so it is important that the seniors of tomorrow
will be able to stay healthy and manage themselves as long
as possible, preferably also with a good quality of life. Physical
activity is very important both for mobility and for the general
well-being, but it can be hard to find motivation to exercise
alone at home. Also in rehabilitation there is a need for a more
engaging approach than a sheet of paper describing exercises
that should be performed. In the GameUp project we developed
fun and motivational exergames particularly targeting
elderly in a user centred approach. Physiotherapists ensured
that the movements and exercises were good for flexibility, leg
strength and balance. In addition to seven Kinect games, a
walking app and a professional portal were developed. The
Kinect games can be played in several levels, and those who
are at risk of falling are able to play while seated. The professional
portal ensures that the results of the project also can be
used as a tool in rehabilitation. Test results from 20 elderly
aged 65-95 as well as clinical trials of adherence to the exercises
are encouraging, and the international and multidisciplinary
team behind the project is now looking for ways to commercialize
the project outcomes
Supervoxel-Consistent Foreground Propagation in Video
Abstract. A major challenge in video segmentation is that the fore-ground object may move quickly in the scene at the same time its ap-pearance and shape evolves over time. While pairwise potentials used in graph-based algorithms help smooth labels between neighboring (su-per)pixels in space and time, they offer only a myopic view of consis-tency and can be misled by inter-frame optical flow errors. We propose a higher order supervoxel label consistency potential for semi-supervised foreground segmentation. Given an initial frame with manual annota-tion for the foreground object, our approach propagates the foreground region through time, leveraging bottom-up supervoxels to guide its es-timates towards long-range coherent regions. We validate our approach on three challenging datasets and achieve state-of-the-art results.
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