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Learner Reviews & Feedback for Visual Perception by Columbia University
30 ratings
About the Course
The ultimate goal of a computer vision system is to generate a detailed symbolic description of each image shown. This course focuses on the all-important problem of perception.
We first describe the problem of tracking objects in complex scenes. We look at two key challenges in this context. The first is the separation of an image into object and background using a technique called change detection. The second is the tracking of one or more objects in a video. Next, we examine the problem of segmenting an image into meaningful regions. In particular, we take a bottom-up approach where pixels with similar attributes are grouped together to obtain a region.
Finally, we tackle the problem of object recognition. We describe two approaches to the problem. The first directly recognize an object and its pose using the appearance of the object. This method is based on the concept of dimension reduction, which is achieved using principal component analysis. The second approach is to use a neural network to solve the recognition problem as one of learning a mapping from the input (image) to the output (object class, object identity, activity, etc.). We describe how a neural network is constructed and how it is trained using the backpropagation algorithm.
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1 - 6 of 6 Reviews for Visual Perception
By Ferenc J
•Nov 21, 2022
This course gives an excellent high-level overview of perception in computer vision. I wish there would be a supplemental lab course (for example in Python OpenCV) to try out some of these examples in practice.
By Krushi J
•Apr 28, 2022
Amazing course , Well explained and interesting assignments!!!
By SOHYUN C
•Jul 30, 2024
this is very professional also perfect lecture
By Marco M
•Jan 27, 2023
Excellent course. Thank you Professor Nayar.
By Ali K
•Sep 24, 2024
Everything is fine about this course
By Shahid R
•May 23, 2023
Amazing Course....