*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)
Data, filter, map to geometry, viewing attributes, render, display, record, loop to appropriate step...
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.
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)
Neighborhoods of Pixels...so how do we do something with this!
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.
Changes in intensity: albedo, orientation, distance
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.
We have large collections of image data...what/how do we do with it?
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.)
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.
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.
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.
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.
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.
Sharing your scoops to your social media accounts is a must to distribute your curated content. Not only will it drive traffic and leads through your content, but it will help show your expertise with your followers.
How to integrate my topics' content to my website?
Integrating your curated content to your website or blog will allow you to increase your website visitors’ engagement, boost SEO and acquire new visitors. By redirecting your social media traffic to your website, Scoop.it will also help you generate more qualified traffic and leads from your curation work.
Distributing your curated content through a newsletter is a great way to nurture and engage your email subscribers will developing your traffic and visibility.
Creating engaging newsletters with your curated content is really easy.