Hominids@home

This is a design document for a distributed thinking project. Don't edit this page unless you're involved in this project.

Image collection

A given rig session will yield

• A number of cameras with ~1000 images. Image are hi-res (~2Kx3K pixels). Each image has a timestamp. File names may not be unique across cameras.
• A GPS log file with a sequence of timestamped waypoints.

We'll need to develop an image collector program (python/Linux?). This will first read in the GPS log file. Then you'll connect the various cameras, one at a time, to USB, and it will download their images. This will produce a "batch" of images, consisting of:

• A directory of the images, with filenames that are unique within the batch.
• A 'batch description' file, JSON format. Includes
  • Batch name (descriptive)
  • And for each image:
    • time
    • lat/long of center of image (estimated from image timestamp and GPS log)
    • filename
    • x/y size, pixels
    • estimated size factor (meters/pixel)

We'll develop a script load_images that takes the above info and does the following:

• Create a Bossa "batch" record
• Copy the images to a directory whose name is the batch ID
• Create medium-res (~1024x768) versions of images.
• Creates a Bossa job record for each image.

Each run of this script produces a batch of jobs that can be managed as a unit (see below). Initially the state of the batch is "pending", meaning that it is not available to volunteers.

Volunteer levels

There will be three levels of participation:

Beginning

Identify bones, no classification. Intended for elementary school kids.

Intermediate

Distinguish Primate/Nonprimate?, and Tooth/Skull/Other?.

Advanced

Distinguish ~10 taxa and ~5 bone types.

Training course

We'll need to develop training courses (probably using Bolt) for each volunteer level. A given course will contain:

• several examples (different ages, lighting conditions) of each type of object to be identified, some negatives, and some images that look like positives but are negative.
• a test consisting of some number of positive and negative examples

Volunteer experience

Each image will initially be shown in a medium-res (1024x768) form. It will have a control area that remains fixed in the UL corner, even if the image scrolls. The control area includes:

• a "Done" button
• a "Magnify" or "Shrink" button. This toggles between the medium-res and hi-res (3Kx2K) image.
• a "Rotate" button that rotates the image 90 deg.

To identify a feature, the user clicks on the image. This pops up a window in which they select (from menus) that taxa and bone type, and can type an optional comment.

After every N images (N=1?) the volunteer is shown a feedback page (see below).

Feedback page

This page is intended to give volunteers feedback on their efforts, and to encourage them to continue. Possible contents:

• thumbnails of recent jobs, with links so that they can see how other people annotated the same image.
• links to message boards
• "who's online now", with ability to send instant messages
• this users's message inbox

Calibration images

We may want to use "calibration images", i.e. images for which the correct annotation is known, for two reasons:

• to increase the fraction of images that contain something
• to improve our assessment of volunteer error rates

Scientist interfaces

Scientists will interact through a web-based interface. The top-level page will show a list of batches, with their times, names, spatial extent, and status (Pending, In Progress, Completed). For a given batch, the scientist can:

• Change the status of the batch
• View a map in which the images appear as small rectangles, with color coding showing which have been processed by volunteers, and how many features were found. Clicking on a rectangle goes to a page showing the instances of the job, allowing the scientist to view and compare the results, to select a "best" answer, and to add their own annotations.
• View a textual list of images, ordered by decreasing number of features found.