CVB Polimago vs Deep Learning
“Deep learning is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised.”
How does deep learning work with machine vision?
To answer that, we need to consider the ways decisions are made by machine vision systems and how they could be improved by introducing automated learning.
The settings would work well with saloons, and the parameters would need to be adjusted to encompass the other types of cars available.

Once cars were done, you would have to add even more exceptions for all the other types of vehicles.

However, this would only be for the profile view, at this scale. You would need to Include all other possible angles of view/scale that the vehicle could be presented in.
Learning by example
Compare the traditional machine vision training to how a child would learn what a car is.
Machine learning tries to replicate this, learning by example instead of relying on explicit instructions from an operator. The machine learning algorithms use “training sets” which is simply data gathered from the real-world.
Neural networks
Because there isn’t a linear relationship between the inputs and outputs in these applications, complex decision making processes have to be made in between the inputs and the outputs.
Perceptrons (neurons) were originally developed in the late 1950s by Frank Rosenblatt, an American psychologist recognised for his work in the field of Artificial intelligence.
Image Source: https://en.wikipedia.org/wiki/File:Rosenblatt_21.jpg
Neurons (perceptrons) determine a single output from several inputs. Each of these inputs has an associated weight (ie how important it is). Their sum and a threshold value (for the total) is what determines the result.

The interconnectivity of neurons is what makes up a neural network, layers of decision making that increase in complexity as it progresses through the structure to produce an output. Neural networks are typically made up of three types of layers:

Without the intermediate hidden layers, the system can manage only relatively simple (but possibly parallelised) problems. The number hidden layers of neurons is a critical part of the structure, as data can be combined in these to allow complex decision-making.
However, to be able to do this, it requires some sort of feedback from the result so that adjustments can be made to fine tune it towards the desired output. This requires data, a lot of it.
Convolutional Neural Networks
A convolutional neural network (CNN) is a sub-class of neural networks, containing at least one layer that is made up of convolutional units that extract data from the image.
Supervised learning?
Neural networks are not the only learning algorithms, while they are the most popular they make up a tiny subset of the multiple approaches when it comes to machine learning.
Supervised learning methods use labelled input images, which the specific algorithm uses to approximate a function that correctly classifies these images. What none of these methods can tell you is the correct inputs or variables to use in designing the algorithm!
"An important feature of a learning machine is that its teacher will often be very largely ignorant of quite what is going on inside, although he may still be able to some extent to predict his pupil's behaviour"
(Alan Turing, 1950 in "Computing Machinery and Intelligence". Mind, 59, 433-460)
Data, data, even more data...is it needed?
For a machine learning tool to work, it needs feedback about right and wrong decisions. The labelled training data is all that the machine learning tool knows. This means that the variation in the data informs the tool how a class can look and where the limits lie. Training edge-cases is an important part of the process.
Because of this, it is critical that the training data is representative of the types of variation that the system will see at runtime. This might mean using a large amount of data so that outliers are not too influential.
CNNs & CVB Polimago: Pros and Cons
The possible applications overlap massively, but some applications are currently only served by CNNs: unsupervised learning (training on unlabelled data) and the segmentation of an image (for example by textural features). By contrast, CVB Polimago has a much smaller requirement for training data, typically 50 images per class instead of 500 to 1000 for CNNs. The smaller training sets make CVB Polimago applicable to many industrial applications, such as variable defect detection, where there simply may not be thousands of images of defects.
The assumptions about data in CVB Polimago have a further advantage, far fewer training parameters compared to CNNs, meaning that new users may find CVB Polimago easier than a CNN.
Head over to our Common Vision Blox Website to find out more about CVB Polimago and how it works