DHHpC12 @ICHASS
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DHHpC12 @ICHASS
This is one of the spaces I'll be taking notes for the Digital Humanities High-performance Computer Collaboratory.
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Day 2: Mon. June 11, 2012; 9:00am-12:00noon

Alan B. Craig

Introduction to Visualization

 

Visualization:

* existed before the invention of computers

*representing information in a way that is maximally benefitial to the intended audience

a. information visualization

b. scientific visualization

c. other specific areas like Business Visualization, Humanities Visualization, etc.

*provides NEW insight OR a NEW way to communicate

 

1987 NSF Panel Initiative-Formal Definition

"Visualization is a method of computing. It transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations. Visualization offers a method for seeing the unseen. It enriches the process of scientifice discovery and ofsters profound and unexpected insights."

A. Craig's interpretation: it's a method of doing something

Richard Hamming: "The purpose of computing is insight, not numbers."

 Goal of visualization: leverage existing scientific methods by providing new scientific insight through visual methods. 

 

Visualization: The choice of the appropriate representation of what we are trying to do.

 

Interpolation: Generating new data based on a given numbers. 

 

Inveractive vs. Batch Visualization

Interactive: allows the ability to control in real-time; limits the amount of data; useful for analysis & exploration (more likely to lead to new discovery)

Batch: High-Quality, complex representation; no control in real time; useful for presentation, communication, high complexity (focused on presentation)

 

Visualization Process/Flow:

Data, filter, map to geometry, viewing attributes, render, display, record, loop to appropriate step...

 

Ended w/playing in Many Eyes

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GIMP - The GNU Image Manipulation Program

GIMP - The GNU Image Manipulation Program | DHHpC12 @ICHASS | Scoop.it

Kenton McHenry suggested GIMP as a free alternative to Photoshop.

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Digital Thingy-ness: Putting Materiality, Mediality, and Objects at the heart of the Digital Humanities | THATCamp CHNM 2012

Digital Thingy-ness: Putting Materiality, Mediality, and Objects at the heart of the Digital Humanities | THATCamp CHNM 2012 | DHHpC12 @ICHASS | Scoop.it

FROM BLOG: I suggest that we take a session at THATCamp to pull together an annotated bibliography, a must read list if you will, of works on thingyness that folks interested in the digital humanities but who also want to study digital things can look at . I’ve pulled together a starter list of works from some different fields that I think fit here. I have also included what about these works makes them candidates for this conversation and list.

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XSEDE | Home

XSEDE | Home | DHHpC12 @ICHASS | Scoop.it

FROM WEBSITE: XSEDE is a single virtual system that scientists can use to interactively share computing resources, data and expertise. People around the world use these resources and services — things like supercomputers, collections of data and new tools — to improve our planet.

 

Mentioned as a place to get small grants to help analyze handwritten data (small archives) 

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“Culture” in the Science Fictional Universe of “Big Data” | Robert Albro

RT @DavidRArmitage: ...or, Why the Digital Needs the Humanities (and the Interpretive Social Sciences) http://t.co/k3PR5HxY...
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Day 1: Sun. June 10, 2012; 11:30am-2:15pm (w/Lunch)

Day 1: Sun. June 10, 2012; 11:30am-2:15pm (w/Lunch) | DHHpC12 @ICHASS | Scoop.it

Kenton McHenry: Image Features

 

Neighborhoods of Pixels...so how do we do something with this!

 

Features:

1. Interest points: Points or collections of points that are somehow relevant to indentifying the contents of an image.

2. Subset of the image: Far less data than the total number of pixels.

3. Repeatability: A feature detector should be likely to detect the feature in a scene under a variety of lighting, orientation, and other variable conditions. 

 

EDGES

Changes in intensity: albedo, orientation, distance

CORNERS

BLOBS

 

McHenry uses MATLAB to demo how to read/manipulate/interpret digital images. 

 

Convolution: the concept of the function scanning the whole image

 

Continuous vs. Discrete...real world, smooth; image is static, discrete, pixels! So in MATLAB compare vector coordinates of given contrast (like vertical distinction) to vector coordinates from an image to mark what you are looking for (we're talking about borders of objects in images). To smooth out noise, average the difference of the surrounding pixals and make the center pixal that new average. 

 

CONCLUSION:

Features: interest points, repeatables, subset images

Edges: lines, circles, ...

Corners

Blobs: superpixels

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Day 1: Sun. June 10, 2012; 9:00-11:00am

Day 1: Sun. June 10, 2012; 9:00-11:00am | DHHpC12 @ICHASS | Scoop.it

Introductions

 

Kenton McHenry: Using Image Data in your Research

We have large collections of image data...what/how do we do with it? 

Goals: 

1. A high level understanding of what Computer Vision is and how we might use it.

a. A sense of what is currently possible.

b. A sense of how these things break

c. A sense of what might be possible

d. A sesne of what is pure science fiction

e. The looming opportunity in "Big Data"

2. A little bit of hands on experience. 

 

Computer Vision: What information can you get from a digital image? High level and low level (and mid). What kind of scene? Are there cars? Where are the cars? Is it day or night? What is the ground made of? 

What computer sees: raster Images...pixels, matrix of numbers. 

 

How/why is this hard? 

Image created: a scene, light bounces off scene into a sensor (eye/camera lens). Variables: 

Light/s: position, strength, geometry, color (Shows example of the light source of an image can change how the machine understands a color.)

Surface/s: orientation, color, material, nearby surfaces (absorption, diffuse reflection, specular reflection, transparency, refraction, fluorescence, surface interaction)

Sensor: lens, apeture, exposure, resolution, perspective (3D world onto a 2D plane)

 

Neighborhoods of Pixels: Individual pixels don't mean much on their own; need to look at them in context. Change in intensity from pixel to pixel. 

 

Make a computer understand images and video

*Lots of variables

*variables are not independent and interact

*problem is underconstrained (multiple scens can result in same image)

Shows videos of 3D objects where perspective makes us (and computers) interpret one way (confused). 

 

More human brain devoted to vision than anything else (is this part of the reason that vision trumps other senses in learning...Brain Rules)

 

So Far...

1. Barcodes...computer can read lines well (since 1950s)

2. Opitical Character Recognition (OCR)

3. Biometrics (fingerprints, faces)

...

*Image stitching

* Sports (lines on football field)

* Object Recognition (Google Goggles)

* Human Computer Interaction (object recognition, 3D reconstruction)

 

RECAP:

Vision is Hard!

Most apps are still "quirky"

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FAST Corner for iPhone on the iTunes App Store

Read reviews, get customer ratings, see screenshots, and learn more about FAST Corner on the App Store. Download FAST Corner and enjoy it on your iPhone, iPad, and iPod touch.
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Introduction to MATLAB, January IAP 2010

Introduction to MATLAB, January IAP 2010 | DHHpC12 @ICHASS | Scoop.it
This course provides an aggressively gentle introduction to MATLAB®. It is designed to give students fluency in MATLAB, including popular toolboxes. The course consists of interactive lectures with students doing sample MATLAB problems in real time.
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Matlab Handout: An Introduction to MATLABand the Control Systems toolbox

Matlab Handout: An Introduction to MATLABand the Control Systems toolbox | DHHpC12 @ICHASS | Scoop.it

ABSTRACT: MATLAB is essentially a programming interface that can be used for a variety of scientific calculations, programming and graphical visualization. Its basic data element isan array, and its computations are optimized for this data type, which makes it ideal for problems with matrix and vector formulations. MATLAB is also extendible by means of add-on script packages called toolboxes, which provide application-specific functions for use with MATLAB. For this course, we will mostly be using MATLAB’s basicmatrix/vector operations and graphing capabilities in conjunction with the control systemtoolbox.

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#dhhpc12 - Digital Humanities High-Performance Computing collaboratory 2012

#dhhpc12 - Digital Humanities High-Performance Computing collaboratory 2012 | DHHpC12 @ICHASS | Scoop.it

The official event hashtag!

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ImageMagick: Convert, Edit, Or Compose Bitmap Images

ImageMagick: Convert, Edit, Or Compose Bitmap Images | DHHpC12 @ICHASS | Scoop.it

Kenton McHenry suggested a an extensive command line image manipulation and conversion utility. 

FROM SITE: Use ImageMagick to convert, edit, or compose bitmap images in a variety of formats. In addition resize, rotate, shear, distort and transform images automagically.

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National Center for Supercomputing Applications at the University of Illinois

National Center for Supercomputing Applications at the University of Illinois | DHHpC12 @ICHASS | Scoop.it

NCSA is where we met for the first week of DHHpC.

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Framework for Handwriting Analysis

Freely available framework that could be downloaded to use for small archival projects. 

Pre-processing: spreadsheet segmentation, word spotting, indexing

GWT web interface

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Cultural Heritage Imaging | Reflectance Transformation Imaging (RTI)

Cultural Heritage Imaging | Reflectance Transformation Imaging (RTI) | DHHpC12 @ICHASS | Scoop.it

FIRST LINES: RTI is a computational photographic method that captures a subject’s surface shape and color and enables the interactive re-lighting of the subject from any direction. RTI also permits the mathematical enhancement of the subject’s surface shape and color attributes.

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Day 1: Sun. June 10, 2012; 2:30-5:10pm

Day 1: Sun. June 10, 2012; 2:30-5:10pm | DHHpC12 @ICHASS | Scoop.it

Reminders that the point is to understand what goes on under the hood of serious image analysis applications so we have some sense of what/how/why to have conversations with CS folks in the future (we're not going to be coding this stuff ourselves). 

 

What can you do?

Matching: match an object in two images based on similar features.

Tracking: Follow an object in a video by following its features.

Object Recognition: Find objects based on known features it will posses.

Segmentation: Break an image up into more meaningful regions based on the seen features. 

 

Feature Descriptors: Shapes, Curves, Color, Mean, Distribution, Texture, Filter banks, Size, Statistics, Neighbors

 

Machine Learning:

Supervised learning (human groups elements and teaches the computer w/a bunch of examples)

 

ISDA Project: digitizing (w/OCR) census data

 

Project: Outcome goal?, what exactly is the data?, how will we constrain material? 

 

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Computer Vision: Algorithms and Applications

FROM PREFACE: The seeds for this book were first planted in 2001 when Steve Seitz at the University of Washington invited me to co-teach a course called “Computer Vision for Computer Graphics”. At that time, computer vision techniques were increasingly being used in computer graphics to create image-based models of real-world objects, to create visual effects, and to merge real-world imagery using computational photography techniques. Our decision to focus the applications of computer vision on fun problems such as image stitching and photo-based 3D modeling from your own photos seemed to resonate well with our students.

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Kenton McHenry's Slides

Parent Directory -
1-Introduction.pptx 07-Jun-2012 22:15 8.1M
2-Features.pptx 10-Jun-2012 12:05 1.8M
3-Descriptors.pptx 07-Jun-2012 21:21 2.4M
4-MachineLearning.pptx 09-Jun-2012 23:47 3.1M
5-HandwrittenData.pptx 09-Jun-2012 23:40 23M
6-BasicHPC.pptx 10-Jun-2012 00:27 532K
7-Versus.pptx 10-Jun-2012 08:25 7.1M
8-Medici.pptx 10-Jun-2012 00:01 1.7M

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MathWorks Italia - MATLAB - The Language of Technical Computing

MathWorks Italia - MATLAB - The Language of Technical Computing | DHHpC12 @ICHASS | Scoop.it
MATLAB is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numerical computation.
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