Akustisches Bildverständnis für Sehbehinderte basierend auf einem modularen Computer Visions Sonifikations Modell

Die vorliegende Arbeit beschreibt ein System das blinden Menschen einen direkt erfahrbaren Zugang zu Bildern mit Hilfe akustischer Signale anbietet. Der Benutzer exploriert ein Bild interaktiv auf einem berührungsempfindlichen Bildschirm und erhält eine akustische Rückmeldung über den Bildinhalt an der jeweiligen Fingerposition. Die Gestaltung eines solchen Systems beinhaltet zwei größere Herausforderungen: Welche ist die relevante Bildinformation, und wie kann möglichst viel Information in einem Audiosignal untergebracht werden. Wir behandeln diese Probleme basierend auf einem modularen Computer Vision Sonikations Modell, welches wir als grundlegendes Gerüst für die Aufnahme, Exploration und Sonikation von visueller Information zur Unterstützung blinder Menschen vorstellen. Es werden einige Ansätze vorgestellt, welche hierzu die Information auf verschiedenen Abstraktionsebenen kombinieren. So z.B. sehr grundlegende Information wie Farbe, Kanten und Rauigkeit und komplexere Information welche durch die Verwendung von Machine Learning Algorithmen gewonnen werden kann. Diese Machine Learning Algorithmen behandeln sowohl das Erkennen von Objekten als auch die Klassikation von Bildregionen in "künstlich" und "natürlich", basierend auf einem neu entwickelten Typs eines probabilistischen graphischen Modells. Wir zeigen, dass dieser Mehr-Ebenen Ansatz dem Benutzer direkten Zugang zum Wesen und Position von Objekten und Strukturen im Bild ermöglicht und gleichzeitig das Potential neuester Entwicklungen im Bereich Computer Vision und Machine Learning ausnutzt. Während der Exploration kann der Benutzer erkannte "künstliche" Strukturen oder bestimmte natürliche Regionen als Referenzpunkte verwenden um andere natürliche Regionen mit Hilfe deren individueller Position, Farbe und Texturen zu klassizieren. Wir werden zeigen, dass geburtsblinde Teilnehmer diese Strategie erfolgreich einsetzen um ganze Szenen zu interpretieren und zu verstehen.This thesis presents a system that strives to give visually impaired people direct perceptual access to images via an acoustic signal. The user explores the image actively on a touch screen or touch pad and receives auditory feedback about the image content at the current position. The design of such a system involves two major challenges: what is the most useful and relevant image information, and how can as much information as possible be captured in an audio signal. We address those problems, based on a Modular Computer Vision Sonication Model, which we propose as a general framework for acquisition, exploration and sonication of visual information to support visually impaired people. General approaches are presented that combine low-level information, such as color, edges, and roughness, with mid- and high-level information obtained from Machine Learning algorithms. This includes object recognition and the classication of regions into the categories "man-made" versus "natural" based on a novel type of discriminative graphical model. We argue that this multi-level approach gives users direct access to the identity and location of objects and structures in the image, yet it still exploits the potential of recent developments in Computer Vision and Machine Learning. During exploration, the user can utilize detected man made structures or specic natural regions as reference points to classify other natural regions by their individual location, color and texture. We show that congenital blind participants employ that strategy successfully to interpret and understand whole scenes

A Modular Computer Vision Sonification Model For The Visually Impaired

Presented at the 18th International Conference on Auditory Display (ICAD2012) on June 18-21, 2012 in Atlanta, Georgia.Reprinted by permission of the International Community for Auditory Display, http://www.icad.org.This paper presents a Modular Computer Vision Sonification Model which is a general framework for acquisition, exploration and sonification of visual information to support visually impaired people. The model exploits techniques from Computer Vision and aims to convey as much information as possible about the image to the user, including color, edges and what we refer to as Orientation maps and Micro-Textures. We deliberatively focus on low level features to provide a very general image analysis tool. Our sonification approach relies on MIDI using "real-world" instead of synthetic instruments. The goal is to provide direct perceptual access to images or environments actively and in real time. Our system is already in use, at an experimental stage, at a local residential school, helping congenital blind children develop various cognitive abilities such as geometric understanding and spatial sense as well as offering an intuitive approach to colors and textures

microProtein Prediction Program (miP3): A Software for Predicting microProteins and Their Target Transcription Factors

An emerging concept in transcriptional regulation is that a class of truncated transcription factors (TFs), called microProteins (miPs), engages in protein-protein interactions with TF complexes and provides feedback controls. A handful of miP examples have been described in the literature but the extent of their prevalence is unclear. Here we present an algorithm that predicts miPs and their target TFs from a sequenced genome. The algorithm is called miP prediction program (miP3), which is implemented in Python. The software will help shed light on the prevalence, biological roles, and evolution of miPs. Moreover, miP3 can be used to predict other types of miP-like proteins that may have evolved from other functional classes such as kinases and receptors. The program is freely available and can be applied to any sequenced genome

Genomic diversity of a nectar yeast clusters into metabolically, but not geographically, distinct lineages

Both dispersal limitation and environmental sorting can affect genetic variation in populations, but their contribution remains unclear, particularly in microbes. We sought to determine the contribution of geographic distance (as a proxy for dispersal limitation) and phenotypic traits (as a proxy for environmental sorting), including morphology, metabolic ability and interspecific competitiveness, to the genotypic diversity in a nectar yeast species, Metschnikowia reukaufii. To measure genotypic diversity, we sequenced the genomes of 102 strains of M.\ua0reukaufii isolated from the floral nectar of hummingbird-pollinated shrub, Mimulus aurantiacus, along a 200-km coastline in California. Intraspecific genetic variation showed no detectable relationship with geographic distance, but could be grouped into three distinct lineages that correlated with metabolic ability and interspecific competitiveness. Despite ample evidence for strong competitive interactions within and among nectar yeasts, a full spectrum of the genotypic and phenotypic diversity observed across the 200-km coastline was represented even at a scale as small as 200\ua0m. Further, more competitive strains were not necessarily more abundant. These results suggest that dispersal limitation and environmental sorting might not fully explain intraspecific diversity in this microbe and highlight the need to also consider other ecological factors such as trade-offs, source-sink dynamics and niche modification

Dhamietal-MEC-Rerefence-mapping

Mapping to reference genomes

Data from: Genomic diversity of a nectar yeast clusters into metabolically, but not geographically, distinct lineages

Both dispersal limitation and environmental sorting can affect genetic variation in populations, but their contribution remains unclear, particularly in microbes. We sought to determine the contribution of geographic distance (as a proxy for dispersal limitation) and phenotypic traits (as a proxy for environmental sorting), including morphology, metabolic ability, and interspecific competitiveness, to the genotypic diversity in a nectar yeast species, Metschnikowia reukaufii. To measure genotypic diversity, we sequenced the genomes of 102 strains of M. reukaufii isolated from the floral nectar of hummingbird-pollinated shrub, Mimulus aurantiacus, along a 200-km coastline in California. Intraspecific genetic variation showed no detectable relationship with geographic distance, but could be grouped into three distinct lineages that correlated with metabolic ability and interspecific competitiveness. Despite ample evidence for strong competitive interactions within and among nectar yeasts, a full spectrum of the genotypic and phenotypic diversity observed across the 200-km coastline was represented even at a scale as small as 200 m. Furthermore, more competitive strains were not necessarily more abundant. These results suggest that dispersal limitation and environmental sorting might not fully explain intraspecific diversity in this microbe and highlight the need to also consider other ecological factors such as trade-offs, source-sink dynamics, and niche modification

Dhamietal-MEC-vcftools_filtering

SNP filtering

filtered SNP set used for population structure and clustering analysis

These SNPs were called from 109 Metchnikowia sp whole genomes mapped to MR_a10 reference genome using GATK best practices guidelines. Hard filtering of initial SNPs was performed using the GATK variant filtration tool (v3.4) and VCFtools (v1.5) as per best practices (Danecek et al. 2011), using the following parameters: base quality = 20, quality by depth = 2.0, mapping quality = 30, Fisher strand bias = 60, mapping quality rank sum =-12.5, and ReadPosRankSum = -8.0. Post InDel removal, the SNP set consisting 1.27 million SNPs across 109 strains was further filtered to exclude: non-bi-allelic SNPs, a minor allele frequency below 0.05 and polymorphisms with more than 50% missing data. To resolve SNPs in linkage, a window size of 50 SNPs advanced by 5 SNPS at a time and an r2 threshold of 0.5 was used. The final set of high confidence SNPs consisted of 88, 192 polymorphisms. See attached scripts