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.

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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.

What You Will Learn

- Design algorithms for detecting meaningful changes in a scene

- Develop methods for tracking objects in a video while the object undergoes changes in pose and illumination

- Learn several approaches to segmenting an image into meaningful regions

- Create an end-to-end pipeline for learning and recognizing objects based on their visual appearance

Course 5 of 5 in the First Principles of Computer Vision Specialization