1,102 research outputs found
Low back pain patterns over one year among 842 workers in the DPhacto study and predictors for chronicity based on repetitive measurements
Musculoskeletal pain in multiple body sites and work ability in the general working population:cross-sectional study among 10,000 wage earners
Association between lifestyle and musculoskeletal pain:cross-sectional study among 10,000 adults from the general working population
Are Insomnia Type Sleep Problems Associated With a Less Physically Active Lifestyle?:A Cross-Sectional Study Among 7,700 Adults From the General Working Population
Association between coffee or caffeine consumption and fecundity and fertility:a systematic review and dose-response meta-analysis
BIMP: A real-time biological model of multi-scale keypoint detection in V1
We present an improved, biologically inspired and multiscale keypoint operator. Models of single- and double-stopped hypercomplex cells in area V1 of the mammalian visual cortex are used to detect stable points of high complexity at multiple scales. Keypoints represent line and edge crossings, junctions and terminations at fine scales, and blobs at coarse scales. They are detected by applying first and second derivatives to responses of complex cells in combination with two inhibition schemes to suppress responses along lines and edges. A number of optimisations make our new algorithm much faster than previous biologically inspired models, achieving real-time performance on modern GPUs and competitive speeds on CPUs. In this paper we show that the keypoints exhibit state-of-the-art repeatability in standardised benchmarks, often yielding best-in-class performance. This makes them interesting both in biological models and as a useful detector in practice. We also show that keypoints can be used as a data selection step, significantly reducing the complexity in state-of-the-art object categorisation. (C) 2014 Elsevier B.V. All rights reserved
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