The Conditional Lucas & Kanade Algorithm

The Lucas & Kanade (LK) algorithm is the method of choice for efficient dense image and object alignment. The approach is efficient as it attempts to model the connection between appearance and geometric displacement through a linear relationship that assumes independence across pixel coordinates. A drawback of the approach, however, is its generative nature. Specifically, its performance is tightly coupled with how well the linear model can synthesize appearance from geometric displacement, even though the alignment task itself is associated with the inverse problem. In this paper, we present a new approach, referred to as the Conditional LK algorithm, which: (i) directly learns linear models that predict geometric displacement as a function of appearance, and (ii) employs a novel strategy for ensuring that the generative pixel independence assumption can still be taken advantage of. We demonstrate that our approach exhibits superior performance to classical generative forms of the LK algorithm. Furthermore, we demonstrate its comparable performance to state-of-the-art methods such as the Supervised Descent Method with substantially less training examples, as well as the unique ability to "swap" geometric warp functions without having to retrain from scratch. Finally, from a theoretical perspective, our approach hints at possible redundancies that exist in current state-of-the-art methods for alignment that could be leveraged in vision systems of the future.Comment: 17 pages, 11 figure

The XMM Cluster Survey: the halo occupation number of BOSS galaxies in X-ray clusters

We present a direct measurement of the mean halo occupation distribution (HOD) of galaxies taken from the eleventh data release (DR11) of the Sloan Digital Sky Survey-III Baryon Oscillation Spectroscopic Survey (BOSS). The HOD of BOSS low-redshift (LOWZ: $0.2 < z < 0.4$) and Constant-Mass (CMASS: $0.43 <z <0.7$) galaxies is inferred via their association with the dark-matter halos of 174 X-ray-selected galaxy clusters drawn from the XMM Cluster Survey (XCS). Halo masses are determined for each galaxy cluster based on X-ray temperature measurements, and range between ${\rm log_{10}} (M_{180}/M_{\odot}) = 13-15$. Our directly measured HODs are consistent with the HOD-model fits inferred via the galaxy-clustering analyses of Parejko et al. for the BOSS LOWZ sample and White et al. for the BOSS CMASS sample. Under the simplifying assumption that the other parameters that describe the HOD hold the values measured by these authors, we have determined a best-fit alpha-index of 0.91$\pm$0.08 and $1.27^{+0.03}_{-0.04}$ for the CMASS and LOWZ HOD, respectively. These alpha-index values are consistent with those measured by White et al. and Parejko et al. In summary, our study provides independent support for the HOD models assumed during the development of the BOSS mock-galaxy catalogues that have subsequently been used to derive BOSS cosmological constraints.Comment: Accepted for publication in MNRAS; 16 pages, 9 figures, 6 tables (1 electronic

Vicariance and dispersal in southern hemisphere freshwater fish clades: a palaeontological perspective

Widespread fish clades that occur mainly or exclusively in fresh water represent a key target of biogeographical investigation due to limited potential for crossing marine barriers. Timescales for the origin and diversification of these groups are crucial tests of vicariant scenarios in which continental break‐ups shaped modern geographic distributions. Evolutionary chronologies are commonly estimated through node‐based palaeontological calibration of molecular phylogenies, but this approach ignores most of the temporal information encoded in the known fossil record of a given taxon. Here, we review the fossil record of freshwater fish clades with a distribution encompassing disjunct landmasses in the southern hemisphere. Palaeontologically derived temporal and geographic data were used to infer the plausible biogeographic processes that shaped the distribution of these clades. For seven extant clades with a relatively well‐known fossil record, we used the stratigraphic distribution of their fossils to estimate confidence intervals on their times of origin. To do this, we employed a Bayesian framework that considers non‐uniform preservation potential of freshwater fish fossils through time, as well as uncertainty in the absolute age of fossil horizons. We provide the following estimates for the origin times of these clades: Lepidosireniformes [125–95 million years ago (Ma)]; total‐group Osteoglossomorpha (207–167 Ma); Characiformes (120–95 Ma; a younger estimate of 97–75 Ma when controversial Cenomanian fossils are excluded); Galaxiidae (235–21 Ma); Cyprinodontiformes (80–67 Ma); Channidae (79–43 Ma); Percichthyidae (127–69 Ma). These dates are mostly congruent with published molecular timetree estimates, despite the use of semi‐independent data. Our reassessment of the biogeographic history of southern hemisphere freshwater fishes shows that long‐distance dispersals and regional extinctions can confound and erode pre‐existing vicariance‐driven patterns. It is probable that disjunct distributions in many extant groups result from complex biogeographic processes that took place during the Late Cretaceous and Cenozoic. Although long‐distance dispersals likely shaped the distributions of several freshwater fish clades, their exact mechanisms and their impact on broader macroevolutionary and ecological dynamics are still unclear and require further investigation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148368/1/brv12473_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148368/2/brv12473.pd

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Registration and representation in computer vision

Given two animals of the same species, could you recognise common anatomical features between them, even if they appeared in different poses? This thesis studies the representation of photometric and geometric uncertainty in such fine-grained object recognition tasks. The problem is difficult, in part, because image appearance can vary wildly with even small changes in object pose. To constrain this inherently ill-posed problem, we develop methods for aligning novel images based on their semantic content, by efficiently leveraging priors over the statistics of natural images

In defense of gradient-based alignment on densely sampled sparse features

In this chapter, we explore the surprising result that gradient-based continuous optimization methods perform well for the alignment of image/object models when using densely sampled sparse features (HOG, dense SIFT, etc.). Gradient-based approaches for image/object alignment have many desirable properties—inference is typically fast and exact, and diverse constraints can be imposed on the motion of points. However, the presumption that gradients predicted on sparse features would be poor estimators of the true descent direction has meant that gradient-based optimization is often overlooked in favor of graph-based optimization. We show that this intuition is only partly true: sparse features are indeed poor predictors of the error surface, but this has no impact on the actual alignment performance. In fact, for general object categories that exhibit large geometric and appearance variation, sparse features are integral to achieving any convergence whatsoever. How the descent directions are predicted becomes an important consideration for these descriptors. We explore a number of strategies for estimating gradients, and show that estimating gradients via regression in a manner that explicitly handles outliers improves alignment performance substantially. To illustrate the general applicability of gradient-based methods to the alignment of challenging object categories, we perform unsupervised ensemble alignment on a series of nonrigid animal classes from ImageNet