Ementorhub

What you'll 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

Skills you'll gain

Computer Vision

Deep Learning

Image Analysis

Feature Engineering

Graph Theory

Artificial Neural Networks

Dimensionality Reduction

Machine Learning

Machine Learning Algorithms

Unsupervised Learning

There are 5 modules in this course

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.

Object Tracking

Image Segmentation

Appearance Matching

Neural Networks