Data Mining - Concepts and Techniques.

Statistics for Spatial Data.

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

This chapter describes three use cases that leverage the functionalities in WIPP. The use cases focus on the following problems:

1.

Cell count and single cell detection




2.

Stem cell colony growth computation




3.

Feature variability analysis

Example Use Cases

The properties and structure of the cellular microenvironment can influence cell behavior. Sites of cell adhesion to the extracellular matrix (ECM) initiate intracellular signaling that directs cell functions such as proliferation, differentiation, and apoptosis. Electrospun fibers mimic the fibrous nature of native ECM proteins and cell culture in fibers affects cell shape and dimensionality, which can drive specific functions, such as the osteogenic differentiation of primary human bone marrow stromal cells (hBMSCs), by. In order to probe how scaffolds affect cell shape and behavior, cell-fiber contacts were imaged to assess their shape and dimensionality through a novel approach. Fluorescent polymeric fiber scaffolds were made so that they could be imaged by confocal fluorescence microscopy. Fluorescent polymer films were made as a planar control. hBSMCs were cultured on the fluorescent substrates and the cells and substrates were imaged. Two different image analysis approaches, one having geometrical assumptions and the other having statistical assumptions, were used to analyze the 3D structure of cell-scaffold contacts. The cells cultured in scaffolds contacted the fibers in multiple planes over the surface of the cell, while the cells cultured on films had contacts confined to the bottom surface of the cell. Shape metric analysis indicated that cell-fiber contacts had greater dimensionality and greater 3D character than the cell-film contacts. These results suggest that cell adhesion site-initiated signaling could emanate from multiple planes over the cell surface during culture in fibers, as opposed to emanating only from the cell's basal surface during culture on planar surfaces.

Measuring dimensionality of cell-scaffold contacts of primary human bone marrow stromal cells cultured on electrospun fiber scaffolds.

We present an optimization methodology for improving the measurement accuracy of image features for low signal to noise ratio (SNR) images. By superimposing known background noise with high quality images in various proportions, we produce a degraded image set spanning a range of SNRs with reference feature values established from the unmodified high quality images. We then experiment with a variety of image processing spatial filters applied to the degraded images and identify which filter produces an image whose feature values most closely correspond to the reference values. When using the best combination of three filters and six kernel sizes for each feature, the average correlation of feature values between the degraded and high quality images increased from 0:6 (without filtering) to 0:92 (with feature-specific filters), a 53% improvement. Selecting a single filter is more practical than having a separate filter per feature. However, this results in a 1:95% reduction in correlation and a 10% increase in feature residual root mean square error compared to selecting the optimal filter and kernel size per feature. We quantified the tradeoff between a practical solution for all features and featurespecific solution to support decision making.

/pdf/methodology-for-increasing-the-measurement-accuracy-of-image-zpv9nki3is.pdf

Methodology for Increasing the Measurement Accuracy of Image Features

The ability to aggregate information about environmental data and analysis processes across tools and services and across projects provides a powerful capability for discovering resources and coordinating projects and a means to convey the rich, community-scale context of data. In this paper, we summarize the science and engineering use cases motivating the metadata and provenance infrastructure of the Environmental Cyberinfrastructure Demonstrator (ECID) Cyberenvironment project at the National Center for Supercomputing Applications (NCSA) and discuss the requirements driving our system design. The user-level metadata and provenance capabilities being developed within ECID are described and we summarize the team’s experiences in building them, and show how our experience can inform the continuing development and refinement of collaborative environmental science environments.

/pdf/community-based-metadata-integration-for-environmental-21mn9uid5r.pdf

Community-based metadata integration for environmental research

This book chapter describes problems related to contemporary document analyses. Contemporary documents contain multiple digital objects of different type. These digital objects have to be extracted from document containers, represented as data structures, and described by features suitable for comparing digital objects. In many archival and machine learning applications, documents are compared by using multiple metrics, checked for integrity and authenticity, and grouped based on similarity. The objective of our book chapter is to describe methodologies for contemporary document processing, visual exploration, grouping and integrity verification, as well as to include computational scalability challenges and solutions.

/pdf/comprehensive-and-scalable-appraisals-of-contemporary-36dubd2zf8.pdf

Peter Bajcsy

Papers

Example Use Cases

Measuring dimensionality of cell-scaffold contacts of primary human bone marrow stromal cells cultured on electrospun fiber scaffolds.

Methodology for Increasing the Measurement Accuracy of Image Features

Community-based metadata integration for environmental research

Comprehensive and Scalable Appraisals of Contemporary Documents