Policies

Description:
Analysis, implementation, and comparison of sketch recognition algorithms, including feature-based, vision-based, geometry-based, and timing-based recognition algorithms; examination of methods to combine results from various algorithms to improve recognition using AI techniques, such as graphical models.

Prerequisites:
Students need to be proficient at object oriented programming in either Java or C++. Students should have taken some higher level computer science courses. Possible courses include artificial intelligence, user interfaces, or software engineering.

Topics:
Each topic will take 1-2 weeks. Each topic will include reading of related papers; students will write a summary and discussion of and/or comment on each paper on their blog. Programmatic assignments will accompany each topic. Students will develop a final project as part of the course. Students will have to write up results in journal formal and provide videos for certain homeworks and final project.

Criteria:
  • Summary and Discussion Criteria
  • Paper Criteria
Grading:
  • Homeworks: 60%
  • Project: 30%
  • Class Participation: 10%
Mini quizzes may be randomly given as part of homework and class participation (testing that reading was done and ready to be discussed in class).

Project:
Students will be expected to do a term project. The possible topics vary widely. Possible topics include:
  1. Building a sketch system for a new application using existing techniques
  2. Creating a new sketch algorithm by merging techniques, or developing a new technique
Students will write a description of their project expressing why the techniques used were appropriate and why. Students should also test their projects by having five other people testing their algorithm.

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