Top 5 Computer Vision Platforms in 2022

Computer vision allows computers and their systems to extract useful information from digitally available photos, videos, and other visual input, such as live streams, and to take actions or make recommendations in response to that information. Computer vision gives the ability to perceive, observe and understand machines, just like artificial intelligence gives them the capacity to think.

Because it has been around longer, human vision has an advantage over computer vision. Just like human vision, it has the advantage of learning to distinguish objects, determining their distance from the viewer, determining if they are moving, and determining whether an image is valid for life.

Computer vision teaches computers to perform similar tasks in less time using cameras, data, and algorithms instead of retinas, optic nerves, and the visual cortex. Because it can examine thousands of products or processes per minute while detecting imperceptible flaws or problems, a system trained to inspect items or monitor a production asset can quickly outperform humans.

Computer vision is used in the energy, utilities, manufacturing, and automobile industries, and the market is still growing.

Now that we understand computer vision let's take a closer look at what a computer vision platform is.

What is Computer Vision Platform?

Computer vision platforms help companies and enterprises produce computer vision projects significantly faster and at a lower cost. No-code and low-code tools greatly accelerate computer vision development. Managed and integrated infrastructure enables businesses in retail, manufacturing, logistics, healthcare, transportation, and other industries to deliver computer vision.

Managed and integrated infrastructure helps businesses deliver computer vision in other industries, such as retail, manufacturing, logistics, healthcare, and transportation. Cameralyze, industry leaders, our software platform is used to integrate and scale computer vision efficiently. You can click here to try it.

Computer Vision Platforms / Tools

1. TensorFlow (Software Library for ML)

TensorFlow is a popular end-to-end open-source machine learning platform that includes a comprehensive set of tools, resources, and libraries. TensorFlow is beneficial for developing and deploying machine learning-powered computer vision applications.

TensorFlow is one of the most user-friendly computer vision tools, allowing users to create machine learning models for tasks such as facial recognition, image classification, object detection, and more. TensorFlow, like OpenCV, supports various programming languages, including Python, C, C++, Java, and JavaScript.

TensorFlow Lite is a lightweight implementation for on-device machine learning with edge devices that is suitable for real-world computer vision projects. TF Lite, as part of TensorFlow, greatly accelerates edge ML implementations with reduced model size and high accuracy at much higher efficiency, allowing ML to be run anywhere.

Advantages: 

• It is an open-source platform. 
• The platform supports a variety of languages. 
• It is constantly updated with new features and improvements. 
• Powerful features and excellent performance 
  

Disadvantage s: It is a resource-intensive toolkit.

2. OpenCV ( Real-Time Computer Vision Library)

OpenCV is a free and open-source software library for machine learning and computer vision. OpenCV, which was designed to provide a common infrastructure for computer vision applications, provides access to over 2,500 classic and cutting-edge algorithms.

Face identification and recognition, red-eye removal, object recognition, extraction of 3D models of objects, tracking moving objects, and stitching different images together into a high-resolution image are all possible with these algorithms.

Advantages: 

• The de facto standard image processing tool.
• It is open-source and free to use. 
• There is widespread community support. 
• Access to over 2,500 algorithms is available. 
• Allows you to modify the code to suit your needs. 
  

Disadvantages: 

• It is not as simple to use as other tools such as MATLAB. 
• A fairly steep learning curve
  

3. Cameralyze ( No Code Computer Vision Platform)

Cameralyze provides a no-code computer vision platform for individual users, businesses, and startups to build, deploy and maintain end-to-end Computer Vision applications. Cameralyze also offers partnerships for Data Scientists & Software Developers.

Cameralyze offers visual programming to intuitively create Computer Vision applications with prebuilt applications on the no-code platform. The platform combines industry-leading computer vision with the power of no-code development and delivers in a single platform for the entire lifecycle of computer vision solutions. It enables businesses, individuals, or the community to build, deploy, and deploy complete AI-based applications.

Advantages: 

• Provides end-to-end platform support to build and deliver solutions for all computer vision applications from a single platform.
  

• It offers its users a no-code platform to create computer vision pipelines much faster.
  

• Easily Scalable: No matter how many transactions you request, your transaction will be answered within milliseconds.
  

• You can connect your live stream with one click and start analyzing.
  

• It provides enterprise-level communication and complete data security and privacy.

Disadvantages: 

• For Enterprises, the image processing (frame capacity) right given as a trial is slightly less.
  

4. CUDA ( Parallel Computing and Programming)‍

‍NVIDIA's CUDA (compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model. It enables developers to use the power of GPUs (Graphics Processing Units) to accelerate processing-intensive applications.

The NVIDIA Performance Primitives (NPP) library is included in the toolkit, and it provides GPU-accelerated image, video, and signal processing functions for a variety of domains, including computer vision. Furthermore, the CUDA architecture is useful for a wide range of tasks such as face recognition, image manipulation, 3D graphics rendering, and others. Edge AI implementations support real-time image processing with Nvidia CUDA to run on-device AI inference on edge devices such as the Jetson TX2.

Advantages: 

• The NPP library contains over 5000 primitives for image and signal processing. 
• It also supports multiple languages. It is quick and efficient. 
• Effective, high-performance video analysis 
  

Disadvantage: 

• It has a high power consumption. 
• Flexibility across platforms is limited.
  

5. MATLAB – Programming Platform for Engineers and Scientists

MATLAB is a programming platform that can be used for a variety of applications including machine learning, deep learning, image, video, and signal processing. It includes a computer vision toolbox with a variety of functions, apps, and algorithms to assist you in designing solutions for computer vision tasks. 

Advantages: 

• It is simple to use and learn; there are numerous free resources available on MATLAB. 
• It is easier to write code because it is a programming language. 
• MATLAB allows for extremely fast coding and is ideal for rapid prototyping. 
• It has an easy-to-use automatic debugging process. 
• Considered to be the most effective research tool All of the functions are thoroughly documented.
  

Disadvantages: 

• The application is not free to use. 
• For many tasks, it is rather slow. 
• It is difficult to integrate with third-party tools.
  

Key Takeaways

As we mentioned above, computer vision is a diverse branch based on image and video data, and there are many platforms.

It helps us automate mundane tasks, achieve higher diagnostic accuracy, increase crop yields, and ensure safety.

Considering all the tools/platforms mentioned above, you will be convinced that Cameralyze is a must-try computer vision platform. You can click HERE to try it and start using it right away.