Selected Topics in Vision & Learning - Object Recognition

Course Description

Object detection and recognition; visual category recognition; dataset issues; feature detection and description; object models; statistical pattern recognition based methods.

Prerequisites

Linear algebra, calculus, probability and statistics. This course makes extensive use of Matlab. Click here for information on Matlab. Assignments should be prepared using LaTeX. If you are not familiar with LaTeX, click here.

Room & Time

MW 2-3:20pm, Room WLH 2113. Class section ID: #743536.

Course Requirements and Grading

• • Course Units: For those taking the class for 4 units, your grade will be determined by the assignments (2/3) and either a final exam or final project (1/3). For 2 units, the grade is based only on homework. For 1 unit, the grade is based only on class participation.

  • Homework: There will be approximately four assignments. Each assignment will have a “target date” for completion but the actual due date for turning in all of the assignments is Monday June 4. Thus you can work at your own pace, but it is a good idea to stick to the target dates and come by my office hours periodically to check your progress.

  • Collaboration: You are encouraged to work in groups of 2-3 on each assignment. Indicate the names of your collaborators at the top of each assignment and cite any references used (including articles, books, code, websites, and personal communications). You may submit just one writeup for the entire group. Remember not to plagiarize; all solutions must be written by members of the group.

  • Final exam: June 13 (Wednesday) 3:30-5:00pm in EBU3B 4217.