A distinct body of literature supports the association between clinical postures of the dental practitioner and workrelated musculoskeletal disorders (WRMD). Several aids or devices have been tested to improve clinical posture in the interest of decreasing WRMD. The use of magnification lenses while performing dental procedures may increase the quality of work and decrease the likelihood of musculoskeletal problems. To date, only anecdotal and personal opinions had existed regarding the benefits of using magnification lenses, and no empirical evidence had authenticated the contention that use of magnification lenses exerts a positive change in operator posture. The objective of this study was to assess the effect magnification lenses had on the posture of dental hygiene students. Using a randomized crossover design, researchers videotaped nineteen senior dental hygiene students performing an intra-oral procedure with and without the use of magnification lenses. The tapes were then evaluated by a panel of five dental hygiene educators calibrated in the use of Branson's Posture Assessment Instrument (PAI). Results of a paired t-test indicate that the posture of the students while wearing magnification lenses was more acceptable (p=.019) than when wearing traditional safety glasses. Results of this study indicate a quantifiable change in acceptability of posture for clinicians wearing magnification lenses and suggest that the use of such lenses in dental education may be warranted.
Live fish recognition is one of the most crucial elements of fisheries survey applications where the vast amount of data is rapidly acquired. Different from general scenarios, challenges to underwater image recognition are posted by poor image quality, uncontrolled objects and environment, and difficulty in acquiring representative samples. In addition, most existing feature extraction techniques are hindered from automation due to involving human supervision. Toward this end, we propose an underwater fish recognition framework that consists of a fully unsupervised feature learning technique and an error-resilient classifier. Object parts are initialized based on saliency and relaxation labeling to match object parts correctly. A non-rigid part model is then learned based on fitness, separation, and discrimination criteria. For the classifier, an unsupervised clustering approach generates a binary class hierarchy, where each node is a classifier. To exploit information from ambiguous images, the notion of partial classification is introduced to assign coarse labels by optimizing the benefit of indecision made by the classifier. Experiments show that the proposed framework achieves high accuracy on both public and self-collected underwater fish images with high uncertainty and class imbalance.
Handegard, N. O., and Williams, K. 2008. Automated tracking of fish in trawls using the DIDSON (Dual frequency IDentification SONar). – ICES Journal of Marine Science. 65: 636–644. An application for the automated tracking of dual-frequency, identification sonar (DIDSON) data was developed and tested on fish observations taken in midwater trawls. The process incorporates target detection, multiple target tracking, and the extraction of behaviour information such as target speed and direction from the track data. The automatic tracker was evaluated using three test datasets with different target sizes, observation ranges, and densities. The targets in the datasets were tracked manually and with the automated tracker, using the manual-tracking results as the standard for estimating the performance of the automated tracking process. In the first and third dataset, where the targets were smaller and less dense, the automated tracking performed well, correctly identifying 74% and 57% of targets, respectively, and associating targets into tracks with <10% error compared with the manually tracked data. In the second dataset, where targets were dense and appeared large owing to the shorter observation range, 45% of targets were correctly identified, and the track error rate was 21%. Target speed and direction, derived from the tracking data, agreed well between the manual and automatic methods for all three test cases. Automated tracking represents a useful technique for processing DIDSON data, and a valuable alternative to time-consuming, manual data-processing, when used in appropriate conditions.
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