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

While hyperspectral data are very rich in information, processing the hyperspectral data poses several challenges regarding computational requirements, information redundancy removal, relevant information identification, and modeling accuracy. In this paper we present a new methodology for combining unsupervised and supervised methods under classification accuracy and computational requirement constraints that is designed to perform hyperspectral band (wavelength range) selection and statistical modeling method selection. The band and method selections are utilized for prediction of continuous ground variables using airborne hyperspectral measurements. The novelty of the proposed work is in combining strengths of unsupervised and supervised band selection methods to build a computationally efficient and accurate band selection system. The unsupervised methods are used to rank hyperspectral bands while the accuracy of the predictions of supervised methods are used to score those rankings. We conducted experiments with seven unsupervised and three supervised methods. The list of unsupervised methods includes information entropy, first and second spectral derivative, spatial contrast, spectral ratio, correlation, and principal component analysis ranking combined with regression, regression tree, and instance-based supervised methods. These methods were applied to a data set that relates ground measurements of soil electrical conductivity with airborne hyperspectral image values. The outcomes of our analysis led to a conclusion that the optimum number of bands in this domain is the top four to eight bands obtained by the entropy unsupervised method followed by the regression tree supervised method evaluation. Although the proposed band selection approach is demonstrated with a data set from the precision agriculture domain, it applies in other hyperspectral application domains.

Methodology For Hyperspectral Band Selection

Automated microscopy can image specimens larger than the microscope’s field of view (FOV) by stitching overlapping image tiles. It also enables time-lapse studies of entire cell cultures in multiple imaging modalities. We created MIST (Microscopy Image Stitching Tool) for rapid and accurate stitching of large 2D time-lapse mosaics. MIST estimates the mechanical stage model parameters (actuator backlash, and stage repeatability ‘r’) from computed pairwise translations and then minimizes stitching errors by optimizing the translations within a (4r)2 square area. MIST has a performance-oriented implementation utilizing multicore hybrid CPU/GPU computing resources, which can process terabytes of time-lapse multi-channel mosaics 15 to 100 times faster than existing tools. We created 15 reference datasets to quantify MIST’s stitching accuracy. The datasets consist of three preparations of stem cell colonies seeded at low density and imaged with varying overlap (10 to 50%). The location and size of 1150 colonies are measured to quantify stitching accuracy. MIST generated stitched images with an average centroid distance error that is less than 2% of a FOV. The sources of these errors include mechanical uncertainties, specimen photobleaching, segmentation, and stitching inaccuracies. MIST produced higher stitching accuracy than three open-source tools. MIST is available in ImageJ at isg.nist.gov.

/pdf/mist-accurate-and-scalable-microscopy-image-stitching-tool-344u9c0ugx.pdf

MIST: Accurate and Scalable Microscopy Image Stitching Tool with Stage Modeling and Error Minimization

Hyperspectral remote sensing produces large volumes of data, quite often requiring hundreds of megabytes to
gigabytes of memory storage for a small geographical area for one-time data collection. Although the high spectral resolution
of hyperspectral data is quite useful for capturing and discriminating subtle differences in geospatial characteristics of the
target, it contains redundant information at the band level. The objective of this study was to identify those bands that contain
the most information needed for characterizing a specific geospatial feature with minimal redundancy. Band selection is performed
with both unsupervised and supervised approaches. Five methods (three unsupervised and two supervised) are proposed
and compared to identify hyperspectral image bands to characterize soil electrical conductivity and canopy coverage
in agricultural fields. The unsupervised approach includes information entropy measure and first and second derivatives
along the spectral axis. The supervised approach selects hyperspectral bands based on supplemental ground truth data using
principal component analysis (PCA) and artificial neural network (ANN) based models. Each hyperspectral image band was
ranked using all five methods. Twenty best bands were selected by each method with the focus on soil and plant canopy characterization
in precision agriculture. The results showed that each of these methods may be appropriate for different applications.
The entropy measure and PCA were quite useful for selecting bands with the most information content, while derivative
methods could be used for identifying absorption features. ANN measure was the most useful in selecting bands specific to
a target characteristic with minimum information redundancy. The results also indicated that a combination of wavebands
with different bandwidths will allow use of fewer than 20 bands used in this study to represent the information contained in
the top 20 bands, thus reducing image data dimensionality and volume considerably.

/pdf/hyperspectral-image-data-mining-for-band-selection-in-4aifb1vt9i.pdf

Hyperspectral image data mining for band selection in agricultural applications

We present a new automatic grid alignment algorithm for detecting two-dimensional (2-D) arrays of spots in DNA microarray images. Our motivation for this work is the lack of automation in high-throughput microarray data analysis that leads to a) spatial inaccuracy of located spots and hence inaccuracy of extracted information from a spot and b) inconsistency of extracted features due to manual selection of grid alignment parameters. The proposed grid alignment algorithm is novel in the sense that 1) it can detect irregularly row- and column-spaced spots in a 2-D array, 2) it is independent of spot color and size, 3) it is general to localize a grid of other primitive shapes than the spot shapes, 4) it can perform grid alignment on any number of input channels, 5) it reduces the number of free parameters to minimum by data driven optimization of most algorithmic parameters, and 6) it has a built-in speed versus accuracy tradeoff mechanism to accommodate user's requirements on performance time and accuracy of the results. The developed algorithm also automatically identifies multiple blocks of 2-D arrays, as it is the case in microarray images, and compensates for grid rotations in addition to grid translations.

/pdf/gridline-automatic-grid-alignment-dna-microarray-scans-2joew3plea.pdf

Gridline: automatic grid alignment DNA microarray scans

We present a gesture recognition algorithm from Euler angles acquired using multiple orientation sensors. This algorithm is a part of a system for controlling unmanned aerial vehicles (UAVs) in the presence of manned aircrafts on an aircraft deck. After exploring multiple approaches to arm gesture recognition, we investigate a real-time arm gesture recognition system using the IS-300 Pro Precision Motion Tracker by InterSense. Our work consists of (1) analyzing several gesture recognition approaches leading to a selection of an active sensor, (2) gesture modeling using Euler angles, (3) low-level gesture characterization, and (4) model-based gesture classification algorithms. We have implemented and tested the proposed real-time arm gesture recognition system in a laboratory environment with a robot that represents an UAV surrogate.

/pdf/recognition-of-arm-gestures-using-multiple-orientation-4jcu4i8szz.pdf

Peter Bajcsy

Papers

Methodology For Hyperspectral Band Selection

MIST: Accurate and Scalable Microscopy Image Stitching Tool with Stage Modeling and Error Minimization

Hyperspectral image data mining for band selection in agricultural applications

Gridline: automatic grid alignment DNA microarray scans

Recognition of arm gestures using multiple orientation sensors: gesture classification