9,721 research outputs found
Quality Classified Image Analysis with Application to Face Detection and Recognition
Motion blur, out of focus, insufficient spatial resolution, lossy compression
and many other factors can all cause an image to have poor quality. However,
image quality is a largely ignored issue in traditional pattern recognition
literature. In this paper, we use face detection and recognition as case
studies to show that image quality is an essential factor which will affect the
performances of traditional algorithms. We demonstrated that it is not the
image quality itself that is the most important, but rather the quality of the
images in the training set should have similar quality as those in the testing
set. To handle real-world application scenarios where images with different
kinds and severities of degradation can be presented to the system, we have
developed a quality classified image analysis framework to deal with images of
mixed qualities adaptively. We use deep neural networks first to classify
images based on their quality classes and then design a separate face detector
and recognizer for images in each quality class. We will present experimental
results to show that our quality classified framework can accurately classify
images based on the type and severity of image degradations and can
significantly boost the performances of state-of-the-art face detector and
recognizer in dealing with image datasets containing mixed quality images.Comment: 6 page
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Educational videos for practitioners attending Baby Friendly Hospital Initiative workshops supporting breastfeeding positioning, attachment and hand expression skills: Effects on knowledge and confidence
UNICEF Baby Friendly Initiative (BFHI) is the global standard for maternity and community services requiring all practitioners to be trained to support mothers in the essential skills of supporting positioning and attachment, and hand expression. These studies aim to rigorously assess knowledge in nurses, midwives, and doctors in these skills, tested before and after watching short videos demonstrating these skills. Practitioners were attending BFHI education, and the video study was additional. In Phase 1 clinicians in England were randomised to one of two videos (practitioner role play or clinical demonstration). The results showed improvements in knowledge and confidence, and a preference for clinical demonstration by mothers and infants. The clinical demonstration video was evaluated in China in Phase 2 where expert trainers viewed the video after completing the BHFI workshop, and in Phase 3 practitioners viewed the video before the BHFI workshop. Phase 2 with expert trainers only showed improvement in knowledge of hand expression but not positioning and attachment. In Phase 3 clinicians showed improved knowledge for both skills. In all Phases there were statistically significant improvements in confidence in practice in both skills.
Viewing short videos increased knowledge, particularly about teaching hand expression, and confidence in both skills
A YBCO RF-SQUID magnetometer and its applications
An applicable RF-superconducting quantum interference detector (SQUID) magnetometer was made using a bulk sintered yttrium barium copper oxide (YBCO). The temperature range of the magnetometer is 77 to 300 K and the field range 0 to 0.1T. At 77 K, the equivalent flux noise of the SQUID is 5 x 10 to minus 4 power theta sub o/square root of Hz at the frequency range of 20 to 200 Hz. The experiments show that the SQUID noise at low-frequency end is mainly from 1/f noise. A coil test shows that the magnetic moment sensitivity delta m is 10 to the minus 6th power emu. The RF-SQUID is shielded in a YBCO cylinder with a shielding ability B sub in/B sub ex of about 10 to the minus 6th power when external dc magnetic field is about a few Oe. The magnetometer is successfully used in characterizing superconducting thin films
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