AI is an extremely vast concept, and the only way to develop a good understanding of this mammoth subject is by taking it step by step.
We all know AI is the simulation of human intelligence and thinking by computer systems, but to truly understand what lies beyond this basic definition, we need to dive into the various AI applications individually. Apart from being prominent in global industries, these applications play a huge role in our daily life, and we don't even notice them.
One such prominent application is Vision AI, or computer vision, which in its right is no less of a technological revolution. From retail, security, and healthcare to automotive, manufacturing, logistics, and even agriculture, Vision AI plays a huge role in these industries.
But what is Vision AI?
Vision AI helps a computer simulate human vision and its data processing abilities. The core principle of Vision AI is centered on machines and computing devices making intelligent decisions after the interpretation of visual data. Vision AI applications can be further broken down into branches like:
- Object classification
- Object detection
- Object tracking
- OCR (optical character recognition)
We know, we know it all sounds too technical! Hi, I am Harsh Murari, CTO at Visionify.ai, a Denver-based computer vision consulting company. And I will help break down these seemingly technical and confusing concepts of Vision AI into a simple and easy-to-understand format for you.
Vision AI basically answers the question: what if we replace human eyes and thinking abilities with intelligent cameras and sensors to do the same job? This AI application uses cameras instead of humans to see, think, and analyze.
It started with exploring the possibilities of smart cameras, whether they could look at images, videos, or live footage and understand what is happening in those frames. To enable these cameras to tell night from day and a moving vehicle from a stationary one.
With Vision AI, a camera can classify by looking at a picture whether it depicts a dog or a cat. This category of Vision AI is called object classification, and the deep learning models used for it are referred to as classification models.
Classifying images is the process of organizing them into categories on the basis of their features or characteristics. Classification is the process of identifying which classes (objects) are present in the visual data. It’s useful on a yes/no level for finding out whether an image contains an object/anomaly or not.
Object detection is a technology that uses machine vision to understand, identify, and detect different objects in a particular area/frame. Example: cars on a road, lampposts on a sidewalk.
Widely used in factories with object detection, manufacturers can detect the presence of unwanted objects on their factories' belts. It’s also quite prominent amongst food manufacturers who use this solution to identify and filter out unwanted debris, human hair, and micromachine parts, and protect their food products from contamination.
Object tracking is all about the movement or motion of a targeted object within a frame. It can be used to monitor the movements of customers in a shop or the speed of moving vehicles.
More advanced versions of this solution include pose-based tracking, which can be used for many things, such as sports analytics or a factory environment, to ensure all workers wear safety gear. You can also program pose-based tracking to focus specifically on particular body parts of a person.
With the help of object tracking, cameras can identify objects in a video and interpret them as a set of trajectories with high accuracy. Objects in the video are tracked frame by frame. People tracking is one of the biggest applications of this solution with widespread applications in almost every industry.
Optical Character Recognition
Can a camera look at a text and be able to read it? That's how simple it was when it started. We slowly progressed from being able to read a single line to being able to read an entire paragraph. Now we can convert non-linear texts, diagrams, and tables into digital formats and machine-readable data.
OCR can capture data from different sections of a document, segregate it, and organize it accordingly into a sanitized digital format.
If we consider the example of a passport, OCR scanning can extract information from every column like the name, address, last issued date, etc., and arrange it accordingly in a digital format. The biggest everyday use of this Vision AI application can be witnessed at banks that use this OCR to process cheques and passbooks.
What about the backbone - machine learning
Machine learning is quite similar to a human neural network. Deep learning networks closely replicate the human brain and neurons. The neuron equivalent in AI is called nodes and the connections formed between different nodes are called edges.
ML models use a vast network of data to imitate the thinking and analytical capabilities of a human brain. For example, if we want a model to identify an image ‘A’ from a set of images A, B, C, and D, we need to train the model to learn and understand what separates these images. It needs to understand what makes those images unique.
What makes image ‘A’, ‘A’? It learns this concept from the set of training images, pixel by pixel, to understand the classification. The more training images you use in your neural network, the more developed and vast it becomes. Now when you ask the model to identify a particular image, it can easily compare the interpreted visual data with information already stored in its network and arrive at a conclusion.
Computer vision is a great starting point for understanding the basic concepts of AI. Stay tuned for more as we carry this conversation forward by diving deep into the industry applications of Computer vision and AI.
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