Computer Vision Job Description

If you have had to authenticate your identification lately, you have probably noticed passwords are becoming a thing of the past, with facial recognition becoming the replacement. This is an example of computer vision, which is gaining popularity in a range of areas, including the automotive, agriculture, and health care industries. 

A computer vision professional works with visual data, such as video feeds and analog images that a computer digitizes. They collect and transform a lot of data through machine learning systems to perform tasks typically done by humans. Their responsibilities include finding unique ways to incorporate artificial intelligence (AI) into different areas, plus helping teach computers to recognize and understand people and objects in images and photos. 

What is computer vision?

Computer vision is a form of AI that uses machine learning algorithms to teach computers how to recognize patterns and features in visual data. After training, the computer uses its capabilities to identify and classify objects in images and videos. Computer vision can also incorporate other techniques, such as deep learning, which mimics the way humans recognize patterns and features in large amounts of data. Computer vision uses a range of pragmatic applications to help with various computer innovations and solutions by interpreting visual data in a manner similar to human perception. 

Types of computer vision roles

Computer vision combines computer science, machine learning, image recognition, and applied mathematics to create hardware and software that uses visual data to perform a function or fix issues. This requires the collaboration of various roles. The backbone of computer vision is a computer vision engineer, whose main role is researching information and using the results in a machine learning system to build client solutions. 

This process requires the input of other professionals, including:

• Machine learning engineers create and train models that can handle a lot of visual data.

• Image processing specialists contribute by manipulating and enhancing images through filtering, smoothing, and other techniques to improve the data’s quality.

• Research scientists test new algorithms and work on the theoretical side of machine perception. 

Computer vision duties and responsibilities

Computer vision engineers typically have experience in various computer-related systems, such as AI, image recognition, machine and deep learning, advanced computing, data science, and image and video segmentation. A computer vision professional’s job responsibilities vary depending on their specific area of focus and objectives. Some general responsibilities include:

• Creating and optimizing computer algorithms and systems that process and interpret visual data

• Applying techniques to identify patterns, shapes, and features in images and videos

• Working with other teams to integrate computer vision technology into different systems

• Developing automated vision algorithms

• Using analytics from computer vision algorithms to improve their performance

• Creating and maintaining documentation to help users understand the system and how to use it

Essential computer vision skills

Computer vision is a type of AI, using techniques such as machine learning, neural networks, and deep learning. Therefore, having a strong familiarity with mathematics, programming, and machine learning is beneficial. Some skills you should possess include:

• Proficiency in computer languages, such as Python, C++, and Java

• Competency in mathematics and statistics

• Working knowledge of machine learning and deep learning while being familiar with libraries such as TensorFlow and PyTorch

• Experience using computer vision projects and applications

• Familiarity with preprocessing, which enhances the quality and collects relevant information from digital images before being processed by computer vision 

Qualifications and experience requirements

Computer vision professionals need experience in different areas, including image recognition, computer science, computer science, and mathematics. Because the job is so technical, you must have a strong educational background. At a minimum, you should hold a bachelor’s degree in computer science, electrical engineering, or another related field in information technology (IT). As many concepts in computer vision involve mathematics, it’s important to have experience with or a strong understanding of algebra, calculus, statistics, and probability. A master’s degree or a PhD in a relevant field can serve as an advantage to employers. 

In addition to education, having hands-on experience when looking to start a career in computer vision is crucial. An internship that deals with computer vision will give you a better sense of how it works, and it should also impress employers. You can also gain experience by working on personal projects or contributing to open-source projects, which will help you build your portfolio.

Key competencies for success

In addition to technical skills, other important skills that will help in the computer vision field include:

• Analytical skills: These skills are necessary to help you identify problems, determine how they happened, and find ways to fix them. 

• Problem-solving skills: Being able to break down large problems into more manageable ones will help when dealing with challenges that arise with new technology.

• Communication skills: Because working in computer vision requires collaboration with other teams, you need to communicate clearly to make sure everyone understands the requirements of their roles. This keeps everybody on board with the progress of projects. 

Industry variations in the computer vision role

Many industries are using computer vision to help improve their technology. Depending on the field, the role of computer vision differs to fit the necessary needs. Types of computer vision applications in particular industries include:

• Health care: Analyzing images and scans takes time, but properly trained computer vision can process and analyze ultrasound images, CT and MRI scans, and X-ray images quickly and with more accuracy.

• Robotics: Computer vision teaches machines how to identify and interact with physical objects.

• Security: Video surveillance is much more advanced because of computer vision AI models, which can rapidly process images in many computer feeds in different locations simultaneously.

• Manufacturing: Automation is enabled by computer vision applications, which help produce quickly and effectively without the risk of human error. It also detects any defects or problems with the system. It alerts the technical team and saves time and money by checking quality and packaging during each step of production.

• Automotive: Improved safety and awareness are possible for drivers because of computer vision applications. These systems allow vehicles to perform tasks such as 3D representation of the driver’s surroundings with object recognition and Radar. This enhances visibility even in low-light conditions. It is beneficial in developing fully automated self-driving automobiles by collecting relevant information about what is going on around the vehicle and routing it properly. 

Career path and growth opportunities

Once you have experience working in computer vision, you may choose to specialize in a particular niche in the computer vision realm. This allows you to concentrate on a specific area and hone your expertise in it, offering career opportunities for employers looking for a particular skill set. Some areas to pursue include augmented reality, facial recognition, and motion capture. Alternatively, you could choose an industry that interests you, such as health care or robotics, or another industry where computer vision is making an impact.

Other advancement opportunities include working in research and development to create new algorithms, product management to oversee computer vision products from conception to completion, or technical leadership to manage a team in your area of expertise.

Learning more about computer vision with Coursera

Computer vision combines basic knowledge of different areas of AI, including algorithms, machine learning, and data processing. If you are looking for a career in computer vision but would like to gain more insight and skills, check out The University of Buffalo’s Computer Vision Basics on Coursera, where you will learn what computer vision is and its goals. You could also consider the First Principles of Computer Vision Specialization from Columbia University in the City of New York, a five-course series focusing on mathematical and physical algorithms used for computer vision.