Computer Vision is associated with a branch of computer science concerned with processing digital images, specifically the identification and extraction of information from these images. A computer vision task can be anything from identifying objects in an image to rendering them.
Given this vision, it is also essential to understand how computer vision differs from machine vision. It is also a branch of artificial intelligence that concerns itself with the automated extraction, analysis, and understanding of useful information from a single image or video frame. In contrast, machine vision refers to video in industrial process control, robotics, and quality inspection. Machine vision uses digital images only to input a more extensive system that makes decisions based on image content.
Computer vision has many different algorithms. Each algorithm attempts to solve a specific task, such as object classification or action recognition. To evaluate each algorithm, researchers have developed a variety of evaluation measures. These measures fall under two main categories: high-level tasks and low-level tasks. As you read further, you’ll know about how Computer vision vs machine vision works.
High-level tasks assess the algorithm’s ability to extract and understand high-level features of an image. These features could be color, object boundaries, or edge detection. If successfully extracted and understood by the algorithm, one can use these features to make predictions about other images. For example, a person detecting a face in an image and then asking another person which direction they are facing would use high-level features such as skin color or facial expression to make a prediction. By using high-level features, we can still use a single metric to compare the performance of many different algorithms.
Low-level tasks do not make use of higher-level representations. Instead, these tasks are concerned with the exact pixel-level features of an image. These tasks ask questions such as, What is the color of this 20 x 20-pixel region? Or is there an object at this specific pixel? And so on. Low-level tasks are concerned with what input comes in, such as color and edge information.
- Pattern Recognition
In pattern recognition, you are concerned with recognizing patterns in your input. A computer vision expert could go through an image and decide whether each pixel belongs to a particular object. The most common algorithm used in this task is the nearest neighbor classifier. This algorithm predicts its label by simply looking at the labels of its neighbors. But, if the image contains features that differ from its neighbors.
- 3D Reconstruction
In three-dimensional reconstruction, you are concerned with finding an image of a 3D object that you have no access to. This image is reconstructed by checking all pixels in the input image. And by finding the pixel close to the known object. If you only compare pixels directly adjacent to each other. However, if you compare multiple positions on the same side of a known object, this algorithm is called an affine transform.
- Computer Vision Systems
You do not just compare an image to a single known object in computer vision systems. Instead, we often compare an image to multiple objects. It is possible to take advantage of low-level features by using a voting system because the visual cortex in humans performs a voting system on the input – in which neurons are active at any given time.
There are multiple ways to use computer vision, but the most common is to capture images. You can use these images for many different tasks. They can help people form a three-dimensional model of a room, or they can be used as input for machine learning algorithms.
Another everyday use of computer vision is to render images. The analysis and rendering of textures, light sources, materials, and other graphical objects is a prevalent task for computer vision researchers. For example, rendering a digital film or game that uses a game engine such as Unreal Engine 4 or Unity 3D is standard for computer vision researchers.
Computer vision is an exciting field of research because it allows us to overcome problems that most people encounter daily. When you design computer vision algorithms, you can ask the questions like, how would you like these problems to be solved?
You can ask this question because computer vision algorithms enable us to do things we currently cannot do. Researchers continue to work on improving our existing methods and designing new ones. Computer vision is thus a field of research with a very high return on investment.