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. Intended for adults from general public.

Advanced

Distinguish ~10 taxa and ~5 bone types. Intended for experts.

The level is stored both in user records and in job instances.

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 the medium-res (1024x768) form. It will have a control area that remains fixed at the top of the window, 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.
Menus for feature type, and a comment field.

Other ideas:

a "Rotate" button that rotates the image 90 deg.

To identify a feature, the user selects a feature type and clicks on the image.

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.

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