Aus der Einführung: "Die Informationstechnik ist seit den Anfängen von CAD vor ca. 50 Jahren ein wesentlicher Impulsgeber für die Produktentwicklung und hat maßgeblichen Anteil an Prozessinnovationen wie dem Global Engineering oder der Digitalen Fabrik. Längst geht es aber heute nicht mehr allein um die Geometriebeschreibung zukünftiger Produkte, sondern um die möglichst umfassende Begleitung und Ergänzung des realen Produkts durch das virtuelle Produkt: von der ersten Idee bis zum Recycling. Die umfassende Vision des virtuellen Produkts als Pendant zum realen Produkt (Spur & Krause 1997) ist untrennbar mit dem Fortschritt der Informationstechnologie verbunden."

abstract: I present my work on a scalable and programmable I/O controller for region-based computing, which will be used in a rhythmic pixel-based camera pipeline. I provide a breakdown of the development and design of the I/O controller and how it fits in to rhythmic pixel regions, along with a studyon memory traffic of rhythmic pixel regions and how this translates to energy efficiency. This rhythmic pixel region-based camera pipeline has been jointly developed through Dr. Robert LiKamWa’s research lab. High spatiotemporal resolutions allow high precision for vision applications, such as for detecting features for augmented reality or face detection. High spatiotemporal resolution also comes with high memory throughput, leading to higher energy usage. This creates a tradeoff between high precision and energy efficiency, which becomes more important in mobile systems. In addition, not all pixels in a frame are necessary for the vision application, such as pixels that make up the background. Rhythmic pixel regions aim to reduce the tradeoff by creating a pipeline that allows an application developer to specify regions to capture at a non-uniform spatiotemporal resolution. This is accomplished by encoding the incoming image, and only sending the pixels within these specified regions. Later these encoded representations will be decoded to a standard frame representation usable by traditional vision applications. My contribution to this effort has been the design, testing and evaluation of the I/O controller. / Dissertation/Thesis / Masters Thesis Computer Science 2020

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20738号 / 情博第652号 / 新制||情||112(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 黒田 知宏, 教授 大手 信人, 教授 松田 哲也 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Aus der Einführung: "Die Informationstechnik ist seit den Anfängen von CAD vor ca. 50 Jahren ein wesentlicher Impulsgeber für die Produktentwicklung und hat maßgeblichen Anteil an Prozessinnovationen wie dem Global Engineering oder der Digitalen Fabrik. Längst geht es aber heute nicht mehr allein um die Geometriebeschreibung zukünftiger Produkte, sondern um die möglichst umfassende Begleitung und Ergänzung des realen Produkts durch das virtuelle Produkt: von der ersten Idee bis zum Recycling. Die umfassende Vision des virtuellen Produkts als Pendant zum realen Produkt (Spur & Krause 1997) ist untrennbar mit dem Fortschritt der Informationstechnologie verbunden."

The digitalisation of information now affects most fields of human activity. From the social sciences to biology to physics, the volume, velocity, and variety of data exhibit exponential growth trends. With such rates of expansion, efforts to understand and make sense of datasets of such scale, how- ever driven and directed, progress only at an incremental pace. The challenges are significant. For instance, the ability to display an ever growing amount of data is physically and naturally bound by the dimensions of the average sized display. A synergistic interplay between statistical analysis and visualisation approaches outlines a path for significant advances in the field of data exploration. We can turn to statistics to provide principled guidance for prioritisation of information to display. Using statistical results, and combining knowledge from the cognitive sciences, visual techniques can be used to highlight salient data attributes. The purpose of this thesis is to explore the link between computer science, statistics, visualization, and the cognitive sciences, to define and develop more systematic approaches towards the design of glyphs. Glyphs represent the variables of multivariate data records by mapping those variables to one or more visual channels (e.g., colour, shape, and texture). They offer a unique, compact solution to the presentation of a large amount of multivariate information. However, composing a meaningful, interpretable, and learnable glyph can pose a number of problems. The first of these problems exist in the subjectivity involved in the process of data to visual channel mapping, and in the organisation of those visual channels to form the overall glyph. Our first contribution outlines a computational technique to help systematise many of these otherwise subjective elements of the glyph design process. For visual information compression, common patterns (motifs) in time series or graph data for example, may be replaced with more compact, visual representations. Glyph-based techniques can provide such representations that can help users find common patterns more quickly, and at the same time, bring attention to anomalous areas of the data. However, replacing any data with a glyph is not going to make tasks such as visual search easier. A key problem is the selection of semantically meaningful motifs with the potential to compress large amounts of information. A second contribution of this thesis is a computational process for systematic design of such glyph libraries and their subsequent glyphs. A further problem in the glyph design process is in their evaluation. Evaluation is typically a time-consuming, highly subjective process. Moreover, domain experts are not always plentiful, therefore obtaining statistically significant evaluation results is often difficult. A final contribution of this work is to investigate if there are areas of evaluation that can be performed computationally.

abstract: The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos. The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss. In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2017

Yes / Video capsule endoscopy (VCE) is a revolutionary technology for the early diagnosis of gastric disorders. However, owing to the high redundancy and subtle manifestation of anomalies among thousands of frames, the manual construal of VCE videos requires considerable patience, focus, and time. The automatic analysis of these videos using computational methods is a challenge as the capsule is untamed in motion and captures frames inaptly. Several machine learning (ML) methods, including recent deep convolutional neural networks approaches, have been adopted after evaluating their potential of improving the VCE analysis. However, the clinical impact of these methods is yet to be investigated. This survey aimed to highlight the gaps between existing ML-based research methodologies and clinically significant rules recently established by gastroenterologists based on VCE. A framework for interpreting raw frames into contextually relevant frame-level findings and subsequently merging these findings with meta-data to obtain a disease-level diagnosis was formulated. Frame-level findings can be more intelligible for discriminative learning when organized in a taxonomical hierarchy. The proposed taxonomical hierarchy, which is formulated based on pathological and visual similarities, may yield better classification metrics by setting inference classes at a higher level than training classes. Mapping from the frame level to the disease level was structured in the form of a graph based on clinical relevance inspired by the recent international consensus developed by domain experts. Furthermore, existing methods for VCE summarization, classification, segmentation, detection, and localization were critically evaluated and compared based on aspects deemed significant by clinicians. Numerous studies pertain to single anomaly detection instead of a pragmatic approach in a clinical setting. The challenges and opportunities associated with VCE analysis were delineated. A focus on maximizing the discriminative power of features corresponding to various subtle lesions and anomalies may help cope with the diverse and mimicking nature of different VCE frames. Large multicenter datasets must be created to cope with data sparsity, bias, and class imbalance. Explainability, reliability, traceability, and transparency are important for an ML-based diagnostics system in a VCE. Existing ethical and legal bindings narrow the scope of possibilities where ML can potentially be leveraged in healthcare. Despite these limitations, ML based video capsule endoscopy will revolutionize clinical practice, aiding clinicians in rapid and accurate diagnosis.