Multiview stereo via volumetric Graph-Cuts and occlusion robust photo-consistency.

This paper presents a volumetric formulation for the multi-view stereo problem which is amenable to a computationally tractable global optimisation using Graph-cuts. Our approach is to seek the optimal partitioning of 3D space into two regions labelled as "object" and "empty" under a cost functional consisting of the following two terms: (1) A term that forces the boundary between the two regions to pass through photo-consistent locations and (2) a ballooning term that inflates the "object" region. To take account of the effect of occlusion on the first term we use an occlusion robust photo-consistency metric based on Normalised Cross Correlation, which does not assume any geometric knowledge about the reconstructed object. The globally optimal 3D partitioning can be obtained as the minimum cut solution of a weighted graph.

High-level scene structure using visibility and occlusion

We demonstrate a new method for extracting high-level scene information from the type of data available from simultaneous localisation and mapping systems. We model the scene with a collection of primitives (such as bounded planes), and make explicit use of both visible and occluded points in order to refine the model. Since our formulation allows for different kinds of primitives and an arbitrary number of each, we use Bayesian model evidence to compare very different models on an even footing. Additionally, by making use of Bayesian techniques we can also avoid explicitly finding the optimal assignment of map landmarks to primitives. The results show that explicit reasoning about occlusion improves model accuracy and yields models which are suitable for aiding data association. © 2011. The copyright of this document resides with its authors.

Improved Tracking of Multiple Humans with Trajectory Prediction and Occlusion Modeling

A combined 2D, 3D approach is presented that allows for robust tracking of moving bodies in a given environment as observed via a single, uncalibrated video camera. Tracking is robust even in the presence of occlusions. Low-level features are often insufficient for detection, segmentation, and tracking of non-rigid moving objects. Therefore, an improved mechanism is proposed that combines low-level (image processing) and mid-level (recursive trajectory estimation) information obtained during the tracking process. The resulting system can segment and maintain the tracking of moving objects before, during, and after occlusion. At each frame, the system also extracts a stabilized coordinate frame of the moving objects. This stabilized frame is used to resize and resample the moving blob so that it can be used as input to motion recognition modules. The approach enables robust tracking without constraining the system to know the shape of the objects being tracked beforehand; although, some assumptions are made about the characteristics of the shape of the objects, and how they evolve with time. Experiments in tracking moving people are described.

Facial Feature Tracking and Occlusion Recovery in American Sign Language

Facial features play an important role in expressing grammatical information in signed languages, including American Sign Language(ASL). Gestures such as raising or furrowing the eyebrows are key indicators of constructions such as yes-no questions. Periodic head movements (nods and shakes) are also an essential part of the expression of syntactic information, such as negation (associated with a side-to-side headshake). Therefore, identification of these facial gestures is essential to sign language recognition. One problem with detection of such grammatical indicators is occlusion recovery. If the signer's hand blocks his/her eyebrows during production of a sign, it becomes difficult to track the eyebrows. We have developed a system to detect such grammatical markers in ASL that recovers promptly from occlusion. Our system detects and tracks evolving templates of facial features, which are based on an anthropometric face model, and interprets the geometric relationships of these templates to identify grammatical markers. It was tested on a variety of ASL sentences signed by various Deaf native signers and detected facial gestures used to express grammatical information, such as raised and furrowed eyebrows as well as headshakes.

Homocysteine, Methylenetetrahydrofolate Reductase C677T Polymorphism, and Risk of Retnal Vein Occlusion: A Meta-analysis

Objective: To assess the role of plasma total homocysteine (tHcy) concentrations and homozygosity for the thermolabile variant of the methylenetetrahydrofolate reductase (MTHFR) C677T gene as risk factors for retinal vascular occlusive disease.

Design: Retinal vein occlusion (RVO) is an important cause of vision loss. Early meta-analyses showed that tHcy was associated with an increased risk of RVO, but a significant number of new studies have been published. Participants and/or Controls: RVO patients and controls.

Methods: Data sources included MEDLINE, Web of Science, and PubMed searches and searching reference lists of relevant articles and reviews. Reviewers searched the databases, selected the studies, and then extracted data. Results were pooled quantitatively using meta-analytic methods.

Main Outcome Measures: tHcy concentrations and MTHFR genotype.

Results: There were 25 case-control studies for tHcy (1533 cases and 1708 controls) and 18 case-control studies for MTHFR (1082 cases and 4706 controls). The mean tHcy was on average 2.8 mol/L (95% confidence
interval [CI], 1.8 –3.7) greater in the RVO cases compared with controls, but there was evidence of between-study heterogeneity (P0.001, I2 93%). There was funnel plot asymmetry suggesting publication bias. There was no evidence of association between homozygosity for the MTHFR C677T genotype and RVO (odds ratio [OR] 1.20; 95% CI, 0.84–1.71), but again marked heterogeneity (P 0.004, I2 53%) was observed.

Conclusions: There was some evidence that elevated tHcy was associated with RVO, but not homozygosity for the MTHFR C677T genotype. Both analyses should be interpreted cautiously because of marked heterogeneity between the study estimates and possible effect of publication bias on the tHcy findings.

Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Robust face recognition with partial occlusion, illumination variation and limited training data by optimal feature selection

This study investigates face recognition with partial occlusion, illumination variation and their combination, assuming no prior information about the mismatch, and limited training data for each person. The authors extend their previous posterior union model (PUM) to give a new method capable of dealing with all these problems. PUM is an approach for selecting the optimal local image features for recognition to improve robustness to partial occlusion. The extension is in two stages. First, authors extend PUM from a probability-based formulation to a similarity-based formulation, so that it operates with as little as one single training sample to offer robustness to partial occlusion. Second, they extend this new formulation to make it robust to illumination variation, and to combined illumination variation and partial occlusion, by a novel combination of multicondition relighting and optimal feature selection. To evaluate the new methods, a number of databases with various simulated and realistic occlusion/illumination mismatches have been used. The results have demonstrated the improved robustness of the new methods.