Explore How Computer Vision Models Use AI to “See” and Interpret Data

One of the most challenging and important fields of artificial intelligence innovation is computer vision. Although machines have always been good at processing data and doing advanced computations, processing images and video is an entirely different world. 

When human beings look at a picture, complex brain functions enable them to assign labels and definitions to every object within that image and interpret what the image represents. This is very difficult for a computer to achieve. However, developments are being made in this direction. 

What are computer vision models? Computer vision (CV) is a field of computer science that explores how artificial intelligence algorithms can be used to “teach” machines how to “see” and interpret videos and imagery. The basis for this technological innovation is an architecture known as convolutional neural networks. 

These neural networks are used to analyze the colors within each pixel of an image to break the image down into a series of data sets. These data sets are then compared against known data for classification purposes. 

The network then rapidly disregards all known data sets that do not match it as it searches for a classification. With each pass, the possibilities for what the image represents are reduced until the computer, hopefully, arrive at an accurate definition of what is in the image or video. 

However, the ultimate goal is not simply to be able to have a computer tell us what is in an image. Different computer vision models can actually use this interpretation data to make decisions and automate tasks. 

For example, computer vision models enable smartphones of today to visually scan an image of your face and then automatically unlock your phone in a process known as facial recognition. Computer vision topics and use cases include self–driving cars, optical character recognition, cancer detection, defect inspection, and many more. 

Computer vision models examples

One of the most frequently cited computer vision models examples is autonomous vehicles. Self-driving cars rely on cameras that continuously scan the environment around them to detect and identify objects that may be around them. The system then uses this information to plan its course and direction. 

Just like a child who learns over time what objects are by looking at them and hearing their definition, computer vision deep learning models are based on repetitive analysis of images. Furthermore, the best computer vision examples are self-learning. This means the computer’s analysis continuously improves with additional use. 

If you’re wondering how to build a computer vision model, the first thing you’ll need is images. These images will need to be high quality and resemble, as closely as possible, the types of images your system needs to be able to accurately analyze. 

If you were designing a system for a self-driving car, you would want to use images of cars, trash cans, caution cones, and stop signs that were actually outside on the road, as they will be seen in real life. If you were designing a system that could read and analyze invoice documents, you would want representative images of real invoices instead of prototypes or templates. 

Then comes the annotation step. Here, you’ll provide definitions of what is in these images so that the machine can associate those objects with those definitions and make decisions based on that interpretation. 

After that, you’ll need to train your model with thousands of annotated images. Generally, when it comes to artificial intelligence, the more images, the better. 

However, it is important to also use high-quality, detailed images so that you are providing as much data as possible to the system. Although you may not want to build an entire computer vision model yourself, this short summary provides you with insights as to how this technology operates, generally. 

Computer vision models types

There are many different computer vision models types that are ideally suited for a variety of use cases. These include:

• Object Detection (locates objects in images and videos)
• Facial Recognition (matching a human face using a digital image or video)
• Image Segmentation (partitions images for easier analysis or interpretation)
• Edge Detection (identifies curves and edges in images)
• Image Classification (identifies and classifies objects within images and videos)
• Feature Matching (finds similar features in two images)

These are a few of the most popular computer vision models. Which computer vision algorithms are best is entirely dependent on your goals with the system. Furthermore, several of these computer vision deep learning models can be used in connection with each other to achieve a particular objective. 

For example, edge detection, image segmentation, and feature matching can all be used in a facial recognition system. Edge detection and image segmentation can both be used to separate the user’s face from the background. Feature matching detects the unique features of the user’s face and matches them with the image in the database, thereby verifying the user’s identity. 

This is just one of the many computer vision examples that are happening all around us. This technology is increasingly being implemented in a wide variety of industries. Suppose you would like to learn more about computer vision software or computer vision technology. In that case, there are several sites that will provide research papers where you can download a computer vision PDF. 

Best computer vision models

No two computer vision models are created equal. The specific algorithms and features of each model will vary depending on the use case. Ultimately, the best computer vision platforms for your organization may not meet the needs of another business. This makes finding the “best” model a fairly subjective goal. 

However, the best computer vision software applications do have several shared characteristics. For example, training a deep-learning machine can be complicated and time-consuming. This is why the best computer vision models come pre-trained. 

Some of the best pre-trained models for image classification include:

• VGG-16
• ResNet50
• EfficientNet

VGG-16 is one of the most popular pre-trained computer vision models. It was created by a team at the University of Oxford and was quickly adopted for its speed, accuracy, and ease of use. ResNet50 and EfficientNet are two other computer vision models that can be used for image classification. 

Speed, accuracy, and ease of use are the three most important characteristics of all computer vision tools and libraries. The best machine vision libraries receive high scores in all of these categories. 

What are deep learning computer vision models?

The key to deep learning computer vision models is neural networks. At a high level, neural networks are models or system architectures designed to mimic the behavior of the human brain. 

The specific goal is to enable machines to be able to “learn” by recognizing patterns in a similar fashion to how the neurons in the brain function. These neural networks are at the heart of all deep-learning computer vision projects. 

They operate using a series of nodes (artificial network neurons), each of which contains a number of layers. These nodes are all connected together and are activated by each other depending on a number of factors. When one node starts another, it passes its data on to the next. This system allows computers to learn to recognize patterns over time as input data is fed into them. 

Because of the many possibilities of truly accurate computer vision models, various projects and research is being poured into this space. This is demonstrated by the establishment of a deep learning computer vision Stanford research lab. 

There are also several resources online that provide information about computer vision, including deep learning for computer vision Jason Brownlee PDF file. Jason is both a software engineer and a research scientist with a wealth of experience in artificial intelligence. 

There are many other places for you to download deep learning for computer vision PDF files to learn more about the exciting developments in popular computer vision models. 

Deploying computer vision models

When it comes to deploying computer vision models, there are several best practices that organizations should keep in mind. One of them is edge deployment. Edge deployment refers to the practice of deploying models near where the data is originating. 

This eliminates the need for a long and complex computer vision pipeline that can allow data to become lost or corrupted. Repeated testing and computer vision model monitoring are also key to the successful deployment of computer vision projects. 

Another part of deploying computer vision models to be aware of is MLOps for computer vision. MLOps is the combination of machine learning and the continuous improvement processes associated with DevOps. MLOps involves a series of steps your organization can follow to ensure the successful rollout of any machine learning products. 

Different tools will rely on different languages and application frameworks. For instance, you may decide to deploy a Flask computer vision application, while another organization may opt for a different choice. 

Computer vision and its applications

One of the most exciting things about artificial intelligence and machine learning generally is computer vision and its applications. For more information about developments in this space, there are several computer vision books that explore the latest computer vision algorithms. 

The book Computer Vision: Models, Learning and Inference is one of the most well-known works on this subject. Although originally published in 2012, this book is still widely respected as one of the most established and reliable sources of information about computer vision. 

Here at Rossum, we also provide several different resources that provide information on computer vision and how it can be specifically applied to meet your organization’s document processing needs.