Holistic Video Stitching for Street Panorama

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryIn this paper, we address how to automatically generate a panorama for a street view from a long video sequence. We model the panorama as a low-rank matrix and formulate the problem as one of robust recovery of the low-rank matrix from highly incomplete, corrupted, deformed measurements (the video frames). We leverage powerful high-dimensional convex optimization tools from compressive sensing of sparse signals and low-rank matrices to solve this problem. In particular, we show how the new method can effectively remove severe occlusions or corruptions (caused by trees, cars, or reflections, etc.), and obtain clean, intrinsic street panoramas that are consistent with all frames. We also show how our method can automatically and robustly establish pixel-wise accurate registration among all the video frames. We demonstrate the effectiveness of our method by conducting extensive experimental comparison with other popular video stitching methods such as AutoStitch and Adobe Photoshop.National Science Foundation / NSF IIS 11-1601

Compact projection: Simple and efficient near neighbor search with practical memory requirements

Image similarity search is a fundamental problem in computer vision. Efficient similarity search across large im-age databases depends critically on the availability of com-pact image representations and good data structures for in-dexing them. Numerous approaches to the problem of gen-erating and indexing image codes have been presented in the literature, but existing schemes generally lack explicit estimates of the number of bits needed to effectively index a given large image database. We present a very simple al-gorithm for generating compact binary representations of imagery data, based on random projections. Our analysis gives the first explicit bound on the number of bits needed to effectively solve the indexing problem. When applied to real image search tasks, these theoretical improvements trans-late into practical performance gains: experimental results show that the new method, while using significantly less memory, is several times faster than existing alternatives. 1

The LDL-C/ApoB ratio predicts cardiovascular and all-cause mortality in the general population

Abstract Background Generally, low-density lipoprotein (LDL) particle size can be inferred from the LDL cholesterol concentration to total apolipoprotein B concentration ratio (LDL-C/ApoB ratio, hereinafter called LAR), which is a good predictor of cardiovascular disease. However, the predictive ability of LAR for mortality risk in the general population is still unclear. This study aimed to explore the association between LAR and cardiovascular as well as all-cause mortality among American adults. Methods The present study was a secondary analysis of existing data from the National Health and Nutrition Examination Survey (NHANES). The final analysis included 12,440 participants from 2005 to 2014. Survival differences between groups were visualized using Kaplan‒Meier curves and the log-rank test. The association of LAR with cardiovascular and all-cause mortality was evaluated using multivariate Cox regression and restricted cubic spline analysis. Age, sex, coronary artery disease, diabetes, lipid-lowering medication use and hypertriglyceridemia were analyzed in subgroup analyses. Results The median age in the study cohort was 46.0 years [interquartile range (IQR): 31.0–62.0], and 6,034 (48.5%) participants were male. During the follow-up period, there were 872 (7.0%) all-cause deaths and 150 (1.2%) cardiovascular deaths. Compared with individuals without cardiovascular events, those who experienced cardiovascular deaths had a lower LAR (1.13 vs. 1.25) (P < 0.001). The adjusted Cox regression model indicated that lower LAR was an independent risk factor for both cardiovascular [hazard ratio (HR) = 0.304, 95% confidence interval (CI): 0.114–0.812] and all-cause mortality (HR = 0.408, 95% CI: 0.270–0.617). Moreover, a significant age interaction was observed (P for interaction < 0.05), and there was a strong association between LAR and mortality among participants over 65 years of age. Further analysis showed an inverse association between LAR and both cardiovascular and all-cause mortality. Conclusions LAR can independently predict cardiovascular and all-cause mortality in the general population