Question

How machine learning is used in computer vision. Please explain briefly with examples.

Answer 1

Computer Vision:

Computer vision allows computers to gain an understanding of digital images and videos. It seeks to automate tasks that human vision can achieve. This involves methods of acquiring, processing, analyzing and understanding digital images, and extraction of data from the real world to produce information. It allows computers to understand digital images and videos.

Machine Learning:

Machine learning is the study of algorithms and statistical models. Systems use it to perform a task without explicit instructions, and instead rely on patterns and inference. Because of this, it can be applied to computer vision and pattern recognition.

Machine learning and computer vision are two fields that have become closely related to one another. Machine learning has improved computer vision with regards to recognition and tracking. Computer vision, in turn, has broadened the scope of machine learning.

Examples:

1. Hand Movement using OpenCV:

To perform video tracking an algorithm analyzes sequential video frames and outputs the movement of targets between the frames. There are a variety of algorithms, each having strengths and weaknesses. Considering the intended use is important when choosing which algorithm to use. There are two major components of a visual tracking system: target representation and localization, as well as filtering and data association.

Video tracking is the process of locating a moving object (or multiple objects) over time using a camera. It has a variety of uses, some of which are: human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging and video editing.

2. Drowsiness detection OpenCV:

This can be used by riders who tend to drive for a longer period of time that may lead to accidents. This code can detect your eyes and alert when the user is drowsy.

3. Digit recognition using softmax regression:

Softmax Regression (synonyms: Multinomial Logistic, Maximum Entropy Classifier, or just Multi-class Logistic Regression) is a generalization of logistic regression that we can use for multi-class classification (under the assumption that the classes are mutually exclusive).