This paper reports on the verification of the video genre: sport, cartoon, news, commercial and music. Results for the two modes, acoustic and visual, and for combined modes show an average equal error rate (ERR) of 16%, 15% and 10%, respectively. These reflect verification accuracy and as such are believed to be the first of their kind; previously published work has focused on closed set identification, assuming the video is known to belong to one of a fixed set. The results also demonstrate the influence of the genre to be classified: the best performance for the visual mode has an EER of 4% (cartoons), and the best performance for the acoustic mode has EER of 0.6% (news). Finally, the combination of the modes presents a more consistent accuracy across the five genre with an EER of 10%.

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and the treatment of cardiovascular diseases. For example, transcatheter aortic valve implantation is an alternative to aortic valve replacement for the treatment of severe aortic stenosis, and transcatheter atrial fibrillation ablation is widely used for the treatment and the cure of atrial fibrillation. In addition, catheter-based intravascular ultrasound and optical coherence tomography imaging of coronary arteries provides important information about the coronary lumen, wall, and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial to the evaluation and the treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation and motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods. We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence, it is important to understand the application domain, clinical background, and imaging modality, so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on the background information of the transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area.

The paper describes the development and quantitative assessment of an approach to face detection (FD), with the application of image classification in mind. The approach adopted is a direct extension of an earlier approach by T.S. Huang and G.Z. Yang (1994). Huang's intensity based approach is found to be susceptible to variations in lighting conditions and complex backgrounds. It is hypothesised that by integrating colour information into Huang's approach, the number of false faces can be reduced. A skin probability map (SPM) is generated from a large quantity of labeled data (530 images containing faces and 714 images that do not) and is used to pre-process colour test images. The SPM allows image regions to be ranked in terms of their skin content, thus removing improbable face regions. The performance improvements are shown in terms of false acceptance (FA) and false rejection (FR) scores. As a front-end to Huang's approach, the benefits of skin segmentation can be seen by a reduction in the FA score from 79% to 15% with a negligible impact on FR.

Rare disease patients face a long, arduous process to obtain a correct diagnosis. This often involves multiple misdiagnoses, leading to undue treatments and surgeries; patients are left feeling unheard by their healthcare providers and are even isolated from their peers who ‘just don’t understand’. Research to support clinicians in diagnosing rare disease patients has made great strides. However, little research aims to support the patients through this challenging time; and those that do, demonstrate minimal patient input in the design process. This is the first paper that uses methodologically robust patient driven design of pre-diagnostic technologies for rare disease patients. We conducted two workshops with rare disease patients to design a probe technology and subsequently evaluated the probe using a questionnaire. This revealed that rare disease patients predominantly wanted clinician communication aids (90.5%) and social support (61.9%) from technology, rather than the informational support that is typical with patient technology.