In the first two weeks, we’ll learn about how our eyes and brain work together to make sense of what we see. We’ll also explore ways to make computer programs better at understanding images by changing them in different ways. We’ll try out some of these changes ourselves using a computer program called Python with a special tool called OpenCV.
Moving on, we’ll dive into how we can tell how far away things are and make cool illusions like the ones you might have seen in magic shows. We’ll also try to recreate some of these illusions using Python and OpenCV.
After that, we’ll talk about how bright or dark things are and how we can figure out what color something really is. We’ll also learn about another illusion that tricks our brains into seeing things differently, and we’ll try to make it happen on our own using Python and OpenCV.
Next up, we’ll explore how things can look different depending on how they’re shaped or positioned. We’ll look at a famous illusion involving a room that plays tricks on our eyes, and we’ll try to recreate it with moving objects using Python and OpenCV.
Then, we’ll talk about how things can seem like they’re moving even when they’re not. We’ll learn about different kinds of movement illusions and try to make some of our own with Python and OpenCV.
In the final weeks, we’ll learn how to get images ready for computer programs to understand them better. We’ll practice drawing lines and shapes on images and learn how to describe what’s in them. We’ll also show off the projects we’ve been working on and talk about how we can make them even better.
Learning Outcomes:
By the end of this class, students will:
Understand Human Perception: Gain insight into how humans perceive visual information, including illusions and phenomena that affect perception.
Apply Data Augmentation Techniques: Learn practical methods to enhance and manipulate visual data to improve machine learning models' performance.
Implement Depth Perception Algorithms: Develop proficiency in implementing stereo vision depth calculation algorithms, essential for tasks such as 3D reconstruction.
Analyze Brightness and Albedo: Acquire the skills to estimate brightness and albedo in images, crucial for tasks such as material recognition and scene understanding.
Address Distortion in Visual Data: Explore techniques to recognize and mitigate distortions in visual data, enhancing the accuracy of computer vision systems.
Understand Motion Perception: Gain insight into how motion illusions influence our interpretation of dynamic visual stimuli and implement motion perception algorithms.
Master Image Preparation and Annotation: Develop proficiency in preparing images for computer vision tasks and annotating them to generate perception.
Apply Python and OpenCV: Gain hands-on experience in implementing algorithms and illusions using Python programming language and OpenCV library.
Engage in Project Development: Collaborate on projects that integrate concepts learned throughout the course, showcasing practical applications of computer vision and perception.
Present and Review Projects: Demonstrate and discuss project outcomes, providing and receiving feedback to improve code quality and implementation techniques.
Delivery Method: Hybrid
Prerequisites:
Basic Programming Knowledge: Students should have a foundational understanding of programming concepts such as variables, loops, conditional statements, and functions.
Familiarity with Python: Prior experience with Python programming language is essential as the course heavily relies on Python for implementing algorithms and conducting practical exercises.
Understanding of Image Processing Basics: Some familiarity with image processing concepts such as grayscale conversion, image filtering, and basic manipulation will be beneficial.
Mathematics Fundamentals: A basic understanding of mathematics concepts such as algebra, geometry, and calculus is recommended, particularly for understanding algorithms and calculations involved in depth perception and image processing.
Interest in Computer Vision: While not mandatory, an interest in computer vision, image processing, and perceptual psychology will greatly enhance students' engagement and comprehension of the course material.
Course Level: 4000-level
Credits: 4
M/W/Th 8:30AM - 9:50AM (Full-term)
Maximum Enrollment: 8
Course Frequency: One time only
Categories: 4000 , All courses , Canceled Courses , Computer Science , Four Credit , Hybrid , Updates
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