ESP Biography

Computer vision is an exciting field that is changing the world we live in. It has numerous applications, from self-driving cars to facial recognition systems. In this course, we will explore non-machine learning forms of computer vision that are still widely used today. We will start by introducing the basics of image processing, including image transforms, image enhancements, and template matching. We will then move on to non-machine learning approaches to computer vision, such as edge detection using the Canny algorithm, and image filtering using techniques like Gaussian blur and median filtering. We will also cover image segmentation techniques, including thresholding and region growing. By the end of the course, students will have a solid understanding of non-machine learning forms of computer vision and will have developed the skills to apply these techniques to real-world problems. This course is ideal for students who have some programming experience, particularly in Python, but no prior knowledge of computer vision is required. Students should be comfortable with basic linear algebra and calculus concepts.