Visual Perception

Columbia University via Coursera

Overview

Discover the intricacies of computer vision systems in "Visual Perception," a comprehensive course offered by Columbia University through Coursera. This enlightening program delves into the essence of perception, showcasing the essential techniques and challenges involved in understanding and interpreting images through advanced technology.

Embark on a journey through the fundamentals, starting with the problem of object tracking within complex scenes. Learn about change detection, a pivotal technique for differentiating objects from their backgrounds, and explore the nuances of tracking multiple objects in dynamic video sequences.

Progress into the realm of image segmentation, where the course introduces a bottom-up strategy for clustering pixels based on similar attributes to demarcate meaningful regions. This segment provides a solid foundation for understanding how images are dissected into components that machines can comprehend.

Building upon this knowledge, the course then ventures into the domain of object recognition. Two innovative approaches are presented: one utilizing principal component analysis for direct recognition of objects and their positions, and the other leveraging neural networks to learn the correlation between images and their classifications. Gain insights into neural network construction and training methodologies, including the backpropagation algorithm.

Designed for scholars and professionals intrigued by the intersections of Artificial Intelligence, Computer Vision, Neural Networks, and Image Segmentation, this course stands as a beacon of knowledge in the ever-evolving landscape of computer vision studies.