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Bewertung und Feedback des Lernenden für Visual Perception von Columbia University

11 Bewertungen

Über den Kurs

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 - 3 von 3 Bewertungen für Visual Perception

von Ferenc J

21. Nov. 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.

von Krushi J

28. Apr. 2022

Amazing course , Well explained and interesting assignments!!!

von Marco M

27. Jan. 2023

Excellent course. Thank you Professor Nayar.