Impact of posterior subcapsular opacification on vision and visual function among subjects undergoing cataract surgery in rural China: Study of Cataract Outcomes and Up-Take of Services (SCOUTS) in the Caring is Hip Project, report 5.

AIM: To study the effect of posterior capsular opacification (PCO) on vision and visual function in patients undergoing cataract surgery in rural China, and to compare this with the effect of refractive error. METHODS: Patients undergoing cataract surgery in at least one eye by local surgeons in a rural setting between 8 August and 31 December 2005 were examined with slit lamp grading of PCO 10-14 months after surgery. Subjects with any PCO associated with best-corrected visual acuity of 6/7.5 or worse, or with grade 2+ or worse PCO without visual decrement, were offered YAG laser capsulotomy. Vision and self-reported visual function were assessed, and various demographic and clinical factors potentially associated with PCO were recorded. RESULTS: Of 313 patients operated on within the study window, 239 (76%) could be contacted by telephone; study examinations were performed on 176 (74%). Examined subjects had a mean (SD) age of 69.4 (10.5) years, 116 (67%) were female, and 149 (86%) had been blind (presenting visual acuity < or = 6/60) in the operated eye before surgery. PCO of grade 1 or above was present in 34 of 204 operated eyes (16.7%). Those with PCO had significantly worse presenting vision (p = 0.007) but not visual function (p>0.3) than those without PCO. Women had a significantly higher prevalence of PCO (20.9%) than did men (8.6%, p<0.05). Of 19 eyes undergoing capsulotomy with best-corrected visual acuity measured the next day, 13 (68%) improved by one or more lines, and seven (37%) improved by two or more lines. Despite a higher uptake of capsulotomy (95%) as opposed to refraction (35%) in this cohort, the yield in terms of eyes with poor presenting visual acuity (< 6/18) that could be improved was higher for refraction (26% = 9/35) than for capsulotomy (9% = 3/35). CONCLUSION: The prevalence of PCO and impact on vision and visual function in this cohort was modest 1 year after surgery. However, PCO prevalence increases with time. Follow-up of this cohort is underway to determine the effectiveness of this early intervention in identifying and treating subjects who will eventually experience clinically significant PCO.

On covariate factor detection and removal for robust gait recognition

We propose a novel bolt-on module capable of boosting the robustness of various single compact 2D gait representations. Gait recognition is negatively influenced by covariate factors including clothing and time which alter the natural gait appearance and motion. Contrary to traditional gait recognition, our bolt-on module remedies this by a dedicated covariate factor detection and removal procedure which we quantitatively and qualitatively evaluate. The fundamental concept of the bolt-on module is founded on exploiting the pixel-wise composition of covariate factors. Results demonstrate how our bolt-on module is a powerful component leading to significant improvements across gait representations and datasets yielding state-of-the-art results.

Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands

The application of custom classification techniques and posterior probability modeling (PPM) using Worldview-2 multispectral imagery to archaeological field survey is presented in this paper. Research is focused on the identification of Neolithic felsite stone tool workshops in the North Mavine region of the Shetland Islands in Northern Scotland. Sample data from known workshops surveyed using differential GPS are used alongside known non-sites to train a linear discriminant analysis (LDA) classifier based on a combination of datasets including Worldview-2 bands, band difference ratios (BDR) and topographical derivatives. Principal components analysis is further used to test and reduce dimensionality caused by redundant datasets. Probability models were generated by LDA using principal components and tested with sites identified through geological field survey. Testing shows the prospective ability of this technique and significance between 0.05 and 0.01, and gain statistics between 0.90 and 0.94, higher than those obtained using maximum likelihood and random forest classifiers. Results suggest that this approach is best suited to relatively homogenous site types, and performs better with correlated data sources. Finally, by combining posterior probability models and least-cost analysis, a survey least-cost efficacy model is generated showing the utility of such approaches to archaeological field survey.