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Master's Projects (MCOGSc) ideas

This version was saved 10 years, 9 months ago View current version     Page history
Saved by Jim Davies
on February 9, 2012 at 3:01:36 pm
 

Soon Carleton will be offering a master's in cognitive science (MCOGSc).

This page contains masters-sized project ideas.

 

Analogical transfer of motion between different 3D models

     This project, co-supervised with Dr. Ali Arya, will look at the case-based/analogical issues associated with transferring a motion-captured sequence (e.g., walking sadly) from one model (e.g., a person) to another (e.g., a dog). Other possibilities are transferring the dance style of one dancer to a different dance on another, or making any arbitrary 3D model walk similarly to any other walking 3D model given.

 

Machine Learning on Quanty Game Data to Improve Detectors

    This project is only possible after data has been collected from the Quanty Game, which, as of March 2010, is not yet live. The SOIL currently has implementations of spatial relation detectors (e.g., above/below, occlusion, close-to), based on computational, linguistic, and some psychological analyses. But they are not data-driven. This project involves making superior detectors based on data collected from human beings. It would involve using a portion of the Quanty Game data for training, and the rest for testing.

 

The next project ideas are based on the software Visuo, originally written by former lab member Jonathan Gagne. It takes in quantitative information (e.g., size, height) and outputs guesses for quantitative information for newly imagined things. It currently works with a database of tagged images. We have 50k images, many of the pixels of which are associated with labels (e.g., tree.)

 

Improving Visuo: Part-Whole Relationships (Kae Bagg's project)

 

Improving Visuo: In Contact

    Right now the distance between two labels in the 2D image is calculated as the distance between the average pixel location. This is not great, because they might overlap-- a smarter system would take into account the edges of the labeled area's outline. For example, how close are the closest points associated with the label? What things are adjacent to each other?

 

Improving Visuo: Coherence In Search Results

     Visuo uses the oracle of objects, which returns the top 10 labels that co-occur with some query label. For example, a "dog" might be associated with the labels "leash" and "fire hydrant." However, also in the top ten might be labels such as "sofa" and "dog bowl." But we know that a leash and a sofa are rarely going to be in the same image. This project's goal is to improve the search results so that the returned results are coherent-- that is, not only do they correlate with the query, but they correlate with each other to some degree. 

 

Improving Imagination Engine: Photo stitching

     Right now the imagination engine puts pixels from different images into a canvas, but there is a lot of blank space around the images. There are software techniques from computer graphics that "stitch" photos together so they look they all came from the same photo. This needs to be implemented for our imagination engine for a good demo.

 

Improving Visuo: Training Visuo

     Visuo needs to be trained on all labels. This takes forever and needs to be babysat. Someone needs to set up scripts and make it happen.

 

Improving Visuo: Optimization

     Training takes forever, and the code can probably be optimized. 

 

Improving Visuo: Clean up LabelMe Database

     LabelMe is a publicly available database 

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