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

Light field cameras, also called plenoptic cameras, capture a field of light rays traveling in space, i.e., the intensity and direction of light rays. This is contrary to conventional still-picture cameras that acquire the aggregated intensity of incident light rays from all directions. A light field camera can be achieved by placing an array of microlenses between the imaging sensor and the main lens. Since a light field camera is essentially viewed as multiple cameras in a 2D array, it can function as a 3D camera based on multiple-view geometry. In crime scene investigations, 3D forensic evidence such as tire tread and shoe imprints in substances like mud or snow can often provide useful information to identify suspects and victims. This work focuses on evaluating lateral and depth resolutions of the light field cameras that are hand-held, easy to use, and affordable [1] to understand their suitability for forensic applications.

Evaluation of Lateral and Depth Resolutions of Light Field Cameras

Actin is the most abundant protein in most multicellular animal cells. It forms a diverse array of structures, particularly filaments, that participate in important processes such as cell motility, division, and contraction. The location and structure of actin filaments used in cell motility has been studied at whole cell spatial resolution. But studies at the sub-cellular level are limited by the optical diffraction limits of light microscopes and by the destructive nature of imaging at resolutions higher than half of the wavelength of visible light. Previous studies at the sub-cellular level focused on actin interaction with myosin, another protein with which it often works in concert.1 Our work focuses specifically on actin structures, and analyzes sub-cellular regions using optical confocal fluorescent microscopy images at 200 nanometer resolution. Segmentation techniques partition images (in this case, of sub-cellular regions) into segments or sets of pixels. The goal is to simplify the image and make it easier to analyze. Several sub-cellular segmentation techniques, many of which are based on the Gaussian mixture model (GMM) Bayes classifier, have been developed by researchers.2–4 GMM Bayes classifier assumes that cell features follow a Gaussian probability density function and are conditionally independent. Only a few studies have focused on how well cell features actually conform to Bayes classifier GMMs.5 Our objective is to analyze the model biases in these sub-cellular region segmentation techniques and so learn how the sub-cellular structures differ from these Gaussian assumptions. The segmentation technique developed in this work is outlined in Figure 1. We used the gray-level co-occurrence matrix Figure 1. Overview of the segmentation algorithm for biologically meaningful image representation. GMM: Gaussian Mixture Model.

Peter Bajcsy

Papers

Evaluation of Lateral and Depth Resolutions of Light Field Cameras

Biases from model assumptions in texture sub-cellular image segmentation