What is Computer Vision?

While visual information processing technology has existed for some time, much of the process required human intervention and was time consuming and error prone. For example, implementing a facial recognition system in the past required developers to manually tag thousands of images with key data points, such as the width of the nose bridge and the distance between the eyes. Automating these tasks required extensive computing power because image data is unstructured and complex for computers to organize. Vision applications were thus expensive and inaccessible to most organizations.

Today, progress in the field combined with a considerable increase in computational power has improved both the scale and accuracy of image data processing. Computer vision systems powered by cloud computing resources are now accessible to everyone. Any organization can use the technology for identity verification, content moderation, streaming video analysis, fault detection, and more.

Computer vision systems use artificial intelligence (AI) technology to mimic the capabilities of the human brain that are responsible for object recognition and object classification. Computer scientists train computers to recognize visual data by inputting vast amounts of information. Machine learning (ML) algorithms identify common patterns in these images or videos and apply that knowledge to identify unknown images accurately. For example, if computers process millions of images of cars, they will begin to build up identity patterns that can accurately detect a vehicle in an image. Computer vision uses technologies such as those given below.

Deep learning

Deep learning is a type of ML that uses neural networks. Deep learning neural networks are made of many layers of software modules called artificial neurons that work together inside the computer. They use mathematical calculations to automatically process different aspects of image data and gradually develop a combined understanding of the image.

Convolutional neural networks

Convolutional neural networks (CNNs) utilize a labeling system to categorize visual data and comprehend the whole image. They analyze images as pixels and give each pixel a label value. The value is inputted to perform a mathematical operation called convolution and make predictions about the picture. Like a human attempting to recognize an object at a distance, a CNN first identifies outlines and simple shapes before filling in additional details like color, internal forms, and texture. Finally, it repeats the prediction process over several iterations to improve accuracy.

Recurrent neural networks 

Recurrent neural networks (RNNs) are similar to CNNs, but can process a series of images to find links between them. While CNNs are used for single image analysis, RNNs can analyze videos and understand the relationships between images. 

Let’s look at some examples of computer vision tasks that organizations can implement below. 

Image classification

Image classification enables computers to see an image and accurately classify which class it falls under. Computer vision understands classes and labels them, for instance trees, planes, or buildings. One example is that a camera can recognize faces in a photograph and focus on them.

Object detection

Object detection is a computer vision task for detecting and localizing images. It uses classification to identify, sort, and organize images. Object detection is used in industrial and manufacturing processes to control autonomous applications and monitor production lines. Connected home camera manufacturers and service providers also rely on object detection to process live video streams from cameras to detect people and objects in real-time and provide actionable alerts to their end users.

Object tracking

Object tracking uses deep learning models to identify and track items belonging to categories. It has several real-world applications across multiple industries. The first element of object tracking is object detection; the object has a bounding box created around it, is given an object ID, and can be tracked through frames. For example, object tracking can be used for traffic monitoring in urban environments, human surveillance, and medical imaging.

Segmentation

Segmentation is a computer vision algorithm that identifies an object by dividing images of it into different regions based on the pixels seen. Segmentation also simplifies an image, such as placing a shape or outline of an item to determine what it is. By doing so, segmentation also recognizes if there is more than one object in an image or frame.

For example, if there is a cat and a dog in an image, segmentation can be used to recognize the two animals. Unlike object detection, which builds a box around an object, segmentation tracks pixels to determine the shape of an object, making it easier to analyze and label.

Content-based image retrieval

Content-based image retrieval is an application of computer vision techniques that can search for specific digital images in large databases. It analyzes metadata like tags, descriptions, labels, and keywords. Semantic retrieval uses commands such as 'find pictures of buildings' to retrieve appropriate content.