Computer vision, is the process of using software to perform operations that the human brain and vision can perform, such as object recognition, flaw detection or quality control. Various image processing and machine learning algorithms are used together to achieve computer vision.

What is Computer Vision?

A clear definition is suggested by IBM: “Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information.”

Computer vision aims computers to see and understand the images as well as humans. Computers have better tools to see but less developed tools to understand like humans. High-quality cameras, sensors, radar and thermal cameras enable computers to get more visual information from outside.  However, derivation of meaning from the visual input is a more complex issue and is an area of research under artificial intelligence.

Source: Techcrunch

Deep learning and neural network techniques make it easier for computer vision to make sense of what it sees and computer vision gets closer to the human visual cognitive system. In fact, computer vision surpasses human vision in many applications such as pattern recognition. For example, researchers suggest that AI gives better and faster results to identify neurological illnesses from brain scan images.

What is the difference between computer vision and machine vision?

Machine vision is the technology used to see errors in a production line or products that need to be categorized. It is mainly used in industrial processes. Computer vision is the whole software and hardware tools used for image acquisition and processing. While computer vision can be used alone without being part of a large system, machine vision is part of a system.

Why is it important now?

Use cases of computer vision are ranging from healthcare to automotive industries. Forbes expected the computer vision market to reach USD 49 billion by 2022.

Computer vision is one of the most important points in the autonomous vehicle race of the automotive industry. Computer vision enables cars to manage the relationship between the car and the environment.

Computer vision is highly dependent on the quality and quantity of the data, more data with better quality builds better deep learning models. Computer vision algorithms are fed by visual information flowing from smartphones every day. Therefore the computer vision systems will be better and smarter in the future.

How does computer vision work?

There are three main components of computer vision:

  • Acquisition of an image
  • Image Processing
  • Analysis

Acquisition of an image: A digital camera or sensor captures the image or data and it is stored as binary numbers; ones and zeroes. This is called raw data.

Image Processing: this process includes the methods used to extract the basic geometric elements that may give information about the image. The image processing also includes the preprocessing step. Preprocessing is necessary to get a more accurate analysis by getting rid of unwanted elements such as noise.

For more information about how image processing works and how it is used in image recognition, feel free to explore our research on the topic.

Analysis: In this step, the processed image is analyzed by using high-level algorithms. Trained neural networks can be used to identify the objects and make decisions.

What are the top computer vision techniques?

In the computer vision, a number of methods are used to evaluate the inputs and obtain the outputs. Techniques such as image classification, object detection, object tracking and image segmentation help create computer vision by combining them or separately.

Many different architectures have been created for Deep Learning, but the CNN architecture is most commonly used in the CV field. This technique has disadvantages such as large dataset requirements, the difficulty of optimization, and being black-box.

Image Classification:

Image classification aims to classify the content in the image according to its type. The most widely used deep learning technique is convolutional neural networks (CNN).

Pre-tagged images create a training data set. Each of the classes in which the images will be included has separate properties and these properties are represented by vectors. These vectors are trained using CNN and improvements are made with new data sets. If the quality of the classifier is not sufficient, more test sets or training sets can be added.

Object Detection:

The identification of the objects in an image has a different working principle than the image classification. In order to classify the objects in the image, those objects must be determined in the bounding boxes. In order to classify the objects in the image, those objects must be determined in the boxes. Although these boxes are of different sizes, they may contain images of the same class. Also, the detection of images containing a large number of objects also requires an increasing amount of computer power. Algorithms like R-CNN, Fast R-CNN, YOLO, Single Shot MultiBox Detector (SSD), and Region-Based Fully Convolutional Networks have been developed to find these occurrences fast.

Object Tracking:

Object tracking is the method that tracks the movement of the object in an image by finding the same object in the next image. Object Tracking techniques can be divided into three categories according to observation methods:

  • Generative techniques: In this technique, the tracking problem is formulated as searching for the image regions which are most similar to the target model. Principal component analysis(PCA), independent component analysis (ICA), non-negative matrix factorization (NMF) are examples of generative models that try to find a suitable representation of the original data.
  • Discriminative techniques: In discriminative methods, monitoring is considered as a binary classification problem, which aims to find a decision limit that best separates the target from the background. Unlike generative methods, both target and background information are used simultaneously. Examples of discriminative methods are stacked auto encoders (SAE), convolutional neural networks and support vector machines(SVM).
  • Hybrid techniques: these two techniques are used jointly and different techniques are adapted according to the problem.

Image Segmentation:

The process of dividing a digital image into image objects or pixel sets. The purpose of image segmentation is to simplify the representation of an image and to facilitate analysis.

Source: University of Toronto

Since there are many different approaches fr image segmentation, Mask R-CNN and Fully Convolutional Networks (FCN) can be used for dense predictions without any fully connected layers.

What are its use cases in the industry?

Computer vision is used in many industries from automotive to marketing, health to security. Computer vision and image processing are concepts that do not separate from each other with certain lines. You can read this research to get detailed information about the usage areas in the industry.

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