Computer Vision for Scientific Measurement

Project Description:

Counting in large image collections (e.g., how many cars passed a traffic sign, or how many animals live in a region) is challenging, and many modern applications use computer vision to detect and count objects in massive image collections. However, when the detection task is very difficult the counts may be inaccurate even with significant investments in training data and model development. The project will build on our ongoing work called DISCOUNT (Perez et al., AAAI 2024) on combining computer vision and human screening to obtain accurate counts. The students will learn about how computer vision can be used to classify images based on modern deep learning approaches, and evaluate statistical estimation approaches for estimating counts for some applications motivated by Ecology and other domains.

Learning Objectives:

• Learn about modern computer vision tools for image classification based on deep learning

• Develop a working knowledge on how to deploy these systems

• Learn about statistical estimation techniques

• Experiment and evaluate the performance of various approaches

• Solve a large scale counting problem

Skills to learn:

• Programming: Python (especially numpy)

• Math: Calculus, Probability