Getting started with Computer Vision

d. Deep learning
Deep learning is a special subset of machine learning and has
revolutionised computer vision. Many problems that once
seemed improbable to be solved are solved to the point
where machines are getting better results than humans.
Deep learning introduced the concept of end-to-end learning
where the machine is just given a dataset of images which
have been annotated with what class of object is present in
each image.
e. Choosing machine learning or deep learning
Classic computer vision analysis excels at measurements,
finding defects or matching patterns. It is the ideal solution
for repeatable dimension measurements of an object in a
controlled environment, such as examining machined parts or
printed circuit boards. Traditional techniques work very well in
constrained environments. However they don’t handle novel
situations very well.
In comparison, machine learning is trainable, and as it gains
access to a wider data set, it’s able to locate, identify and
segment a wider number of objects or faults with more
variable appearance or perspective, such as identifying and
counting foods such as broccoli on a conveyor belt.
Breakthroughs in the field of artificial neural networks in recent
years have driven companies across industries to implement
deep learning solutions, from chatbots in customer service
to image and object recognition in retail, and many more.
Deep learning has unlocked a myriad of sophisticated new AI
applications.
The performance of deep learning algorithms with complex
tasks have made it particularly appealing as a solution.
However, it is not always the best approach to computer
vision and machine learning related problems. Deep learning
methods are ideal for replacing human eyes for object
classification problems, or to emulate expertise by interpreting
images such as medical x-rays. Deep learning algorithms take
a long time to train however, requiring a lot of code compared
to relatively few lines of classic computer vision code.
While each use case is unique and will depend on business
objectives, AI maturity, timescale, data and resources, among
other things, are general considerations to take into account
before deciding whether or not to use deep learning to solve
a given problem.
f. Image processing libraries
OpenCV
The Open Source Computer Vision Library (OpenCV)
is one of the most popular computer vision libraries that
provides many algorithms and functions. It includes
modules such as image processing, object detection and
deep learning to name just a few.
The library is written in C++ and supports C++, Java, Python
and MATLAB interfaces.
Scilab An open source software similar to MATLAB,
with a computer vision and image processing module.
Octave An open source software also similar to MATLAB,
with a computer vision and image processing module.
R An open source data analysis library with packages for
image processing.
13