Thanks to AI and machine learning, computer vision technology is getting upgraded with improved versions of visualizing making perception through machines reliable. Actually, this is completely related to computer-based visual processing of objects.
What is Computer Vision in Machine Learning and AI?
Computer vision is simply the process of perceiving the images and videos available in the digital formats. In Machine Learning (ML) and AI – Computer vision is used to train the model to recognize certain patterns and store the data into their artificial memory to utilize the same for predicting the results in real-life use.
The main purpose of using computer vision technology in ML and AI is to create a model that can work itself without human intervention. The whole process involves methods of acquiring the data, processing, analyzing and understanding the digital images to utilize the same in the real-world scenario.
How Does Computer Vision Work?
Computer vision in machine learning is used for deep learning to analyze the data sets through annotated images showing an object of interest in an image. It can recognize the patterns to understand the visual data feeding thousands or millions of images that have been labeled for supervised machine learning algorithms training.
This process depends subject to the use of various software techniques and algorithms, that are allowing the computers to recognize the patterns in all the elements that relate to those labels and make the predictions accurately in the future. Computer vision can be only utilized only with image processing through machine learning.
How Computer Vision is Different from Image Processing?
Both are part of the AI technology used while processing the data and creating a model. The difference between computer vision and image processing in computer vision helps to gain high-level understanding from images or videos.
For instance, object recognition, which is the process of identifying the type of objects in an image, is a computer vision problem. In computer vision, you receive an image as input and you can produce an image as output or some other type of information.
Whereas, image processing doesn’t need such a high level of understanding of image. In fact, it is the sub-field of signal processing but also applied to images. For example, if you have noisy or blurred images, then under image processing the deblurring or denoising is done to make the object in the image clearly visible to machines.
The image process task involves filtering, noise removal, edge detection, and color processing. In entire processing, you receive an image as input and produce another image as an output that can be used to train the machine through computer vision.
The main difference between computer vision and image processing are the goals (not the methods used). For example, if the goal is to enhance the image quality for later use, which is called image processing. If the goal is to visualize like humans, like object recognition, defect detection or automatic driving, then it is called computer vision.
Application and Role of Computer Vision in AI and ML
The applied science of computer vision is expanding into multiple fields. From AI development to machine learning, it is playing a significant role in helping the machines identify the different types of objects in their natural environment.
From simple home tasks to recognizing human faces, detecting the objects in autonomous vehicles, or combating with enemies in war, computer vision the only technology giving an edge to AI-enabled devices to work efficiently.
The application of computer vision in artificial intelligence is becoming unlimited and now expanded into emerging fields like automotive, healthcare, retail, robotics, agriculture, autonomous flying like drones and manufacturing, etc.
Actually, to create the computer vision-based model the labeled data is required for supervised machine learning. And image annotation is the data labeling technique used for creating such labeled images for computer vision.
Many companies providing the data annotation service for computer vision providing the image annotation solution for AI and machine learning.
Rendering the high-quality training data using the best tools and techniques allowing computer vision to help algorithms train the model to perform accurately in real-life use.