Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction

The ultimate goal of many image-based modeling systems is to render photo-realistic novel views of a scene without visible artifacts. Existing evaluation metrics and benchmarks focus mainly on the geometric accuracy of the reconstructed model, which is, however, a poor predictor of visual accuracy. Furthermore, using only geometric accuracy by itself does not allow evaluating systems that either lack a geometric scene representation or utilize coarse proxy geometry. Examples include light field or image-based rendering systems. We propose a unified evaluation approach based on novel view prediction error that is able to analyze the visual quality of any method that can render novel views from input images. One of the key advantages of this approach is that it does not require ground truth geometry. This dramatically simplifies the creation of test datasets and benchmarks. It also allows us to evaluate the quality of an unknown scene during the acquisition and reconstruction process, which is useful for acquisition planning. We evaluate our approach on a range of methods including standard geometry-plus-texture pipelines as well as image-based rendering techniques, compare it to existing geometry-based benchmarks, and demonstrate its utility for a range of use cases.Comment: 10 pages, 12 figures, paper was submitted to ACM Transactions on Graphics for revie

Molecular characterization of the viaB locus encoding the biosynthetic machinery for Vi capsule formation in Salmonella Typhi

The Vi capsular polysaccharide (CPS) of Salmonella enterica serovar Typhi, the cause of human typhoid, is important for infectivity and virulence. The Vi biosynthetic machinery is encoded within the viaB locus composed of 10 genes involved in regulation of expression (tviA), polymer synthesis (tviB-tviE), and cell surface localization of the CPS (vexA-vexE). We cloned the viaB locus from S. Typhi and transposon insertion mutants of individual viaB genes were characterized in Escherichia coli DH5α. Phenotype analysis of viaB mutants revealed that tviB, tviC, tviD and tviE are involved in Vi polymer synthesis. Furthermore, expression of tviB-tviE in E. coli DH5α directed the synthesis of cytoplasmic Vi antigen. Mutants of the ABC transporter genes vexBC and the polysaccharide copolymerase gene vexD accumulated the Vi polymer within the cytoplasm and productivity in these mutants was greatly reduced. In contrast, de novo synthesis of Vi polymer in the export deficient vexA mutant was comparable to wild-type cells, with drastic effects on cell stability. VexE mutant cells exported the Vi, but the CPS was not retained at the cell surface. The secreted polymer of a vexE mutant had different physical characteristics compared to the wild-type Vi

A Meta-Analysis of Hippocampal and Amygdala Volumes in Patients Diagnosed With Dissociative Identity Disorder

14 pagesDissociative Identity Disorder (DID), an illness characterized by multiple personality states, has long been a controversial diagnosis within the psychiatric community. Demonstrating a neuroanatomical basis for the disorder may help to resolve the controversy. Current literature on the neuroanatomy associated with DID has focused on the hippocampus and amygdala and are inconclusive. This meta-analysis pools the results from n = 3 studies to compare the mean size of these two structures between DID patients, non-DID patients, and healthy controls. Patients diagnosed with both DID & PTSD were found to have smaller hippocampi bilaterally (p< .001) compared to healthy controls; no significant difference was seen in the amygdala. When comparing DID to PTSD patients, the left hippocampus was smaller (p< .001), with a trend for a smaller right hippocampus (p = .06). A comparison of the amygdala was not possible due to a lack of data. These findings suggest that a smaller hippocampus is seen in DID patients beyond what is seen for PTSD, provides neuroanatomical evidence for the memory impairment often seen in DID patients (i.e., amnesia experienced by the host and alters), and presents a potentially novel means to understand this disorder

Silver Oxide Coatings with High Silver-Ion Elution Rates and Characterization of Bactericidal Activity.

This paper reports the synthesis and characterization of silver oxide films for use as bactericidal coatings. Synthesis parameters, dissolution/elution rate, and bactericidal efficacy are reported. Synthesis conditions were developed to create AgO, Ag₂O, or mixtures of AgO and Ag₂O on surfaces by reactive magnetron sputtering. The coatings demonstrate strong adhesion to many substrate materials and impede the growth of all bacterial strains tested. The coatings are effective in killing Escherichia coli and Staphylococcus aureus, demonstrating a clear zone-of-inhibition against bacteria growing on solid media and the ability to rapidly inhibit bacterial growth in planktonic culture. Additionally, the coatings exhibit very high elution of silver ions under conditions that mimic dynamic fluid flow ranging between 0.003 and 0.07 ppm/min depending on the media conditions. The elution of silver ions from the AgO/Ag₂O surfaces was directly impacted by the complexity of the elution media, with a reduction in elution rate when examined in complex cell culture media. Both E. coli and S. aureus were shown to bind ~1 ppm Ag⁺/mL culture. The elution of Ag⁺ resulted in no increases in mammalian cell apoptosis after 24 h exposure compared to control, but apoptotic cells increased to ~35% by 48 and 72 h of exposure. Taken together, the AgO/Ag₂O coatings described are effective in eliciting antibacterial activity and have potential for application on a wide variety of surfaces and devices

Silver Oxide Coatings with High Silver-Ion Elution Rates and Characterization of Bactericidal Activity.

This paper reports the synthesis and characterization of silver oxide films for use as bactericidal coatings. Synthesis parameters, dissolution/elution rate, and bactericidal efficacy are reported. Synthesis conditions were developed to create AgO, Ag₂O, or mixtures of AgO and Ag₂O on surfaces by reactive magnetron sputtering. The coatings demonstrate strong adhesion to many substrate materials and impede the growth of all bacterial strains tested. The coatings are effective in killing Escherichia coli and Staphylococcus aureus, demonstrating a clear zone-of-inhibition against bacteria growing on solid media and the ability to rapidly inhibit bacterial growth in planktonic culture. Additionally, the coatings exhibit very high elution of silver ions under conditions that mimic dynamic fluid flow ranging between 0.003 and 0.07 ppm/min depending on the media conditions. The elution of silver ions from the AgO/Ag₂O surfaces was directly impacted by the complexity of the elution media, with a reduction in elution rate when examined in complex cell culture media. Both E. coli and S. aureus were shown to bind ~1 ppm Ag⁺/mL culture. The elution of Ag⁺ resulted in no increases in mammalian cell apoptosis after 24 h exposure compared to control, but apoptotic cells increased to ~35% by 48 and 72 h of exposure. Taken together, the AgO/Ag₂O coatings described are effective in eliciting antibacterial activity and have potential for application on a wide variety of surfaces and devices

Novel View Prediction Error as a Quality Metric for Image-Based Modeling and Rendering

Image-based modeling and rendering (IBMR) is a sub-discipline of visual computing whose objective it is to capture images of a scene in the real world, construct a model of the world using the captured image data, and use this model to synthesize images of the world from previously unobserved viewpoints. This so-called novel (or virtual) view prediction has traditionally been tackled from two sides: On one side the computer vision community has pursued the construction of geometric models from sets of images only. On the other side the computer graphics community has worked on producing photo-realistic renderings from hand-modeled, virtual scenes and has further come up with algorithms that allow for the synthesis of novel views from input photos of real-world scenes either directly without any geometric models or with approximate, hand-modeled geometry models. The wealth of different IBMR systems also brought in its wake various quality evaluation systems that are more or less tailored to the properties of specific IBMR systems. In recent years, computer vision and graphics have grown together, slowly approaching the goal of novel view prediction on scenes without restrictions. However, the fragmentation of evaluation systems has still not been overcome. This thesis makes two main complementary contributions: We first present a novel texture mapping algorithm that assigns a static texture to polygonal 3D models, given images that are registered in the same coordinate frame as the model. Our texturing algorithm takes into consideration real-world scenes' properties such as illumination and exposure changes between images, non-rigid scene parts, unreconstructed occluders such as pedestrians, and images with pixel footprints that vary by orders of magnitude. We address the size (\ie, the number of images and the number of polygons in the geometry model) of real-world datasets with a novel Markov random field solver that solves the main bottleneck of our texturing framework orders of magnitude faster than related work. Conceptually, we can think of our texturing framework as closing the gap between image-based 3D reconstruction and photo-realistic rendering, thereby turning 3D reconstructions into full-fledged IBMR representations. Second, we introduce an evaluation scheme for IBMR methods that is guided by the definition of IBMR: Novel view prediction error evaluates how well an IBMR algorithm predicts novel views by dividing all input images into training and test images, keeping the test images secret, giving the training images to the IBMR algorithm, letting it predict the test images, and comparing its predictions with the actual test images. In this thesis we verify that (if used in conjunction with suitable image comparison metrics) this scheme fulfills a range of basic, intuitive conditions. We further compare our scheme with traditional, geometric 3D reconstruction evaluation schemes, show in a user study how our scheme relates to human judgment of the quality of novel view predictions, and present a new, general IBMR benchmark based on our evaluation scheme