10 Computer Vision Interview Questions And Sample Answers

Updated 30 September 2022

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Computer vision is an interdisciplinary field of computer science that requires in-depth knowledge of artificial intelligence, deep learning and various programming languages. When interviewing for computer vision positions, interviewers are likely to ask you different types of questions to assess your level of proficiency. By understanding the types of technical questions you are likely to encounter at an interview for a computer vision position, you can prepare meaningful responses and improve your chances of getting the job.

In this article, we compile frequently asked computer vision interview questions and provide sample answers that you can refer to for your interview preparation.

Computer vision interview questions

The following computer vision interview questions and sample answers may help you succeed in your upcoming job interview:

1. What do you know about computer vision?

Many interviewers ask this question at the start of the interview to assess your fundamental understanding of computer vision. You can explain what computer vision is and its purpose in your response. You can elaborate by giving an example of its use.

Example: 'Computer vision is a field of computer science. It is concerned with using artificial intelligence and deep learning to build processes that mimic the human visual system. With computer vision, you can get computers to see, identify, process, analyse and interpret digital images, videos and other types of visual data. You can use it to automate various tasks. For example, you can use computer vision in self-driving cars. The cameras and sensors in such vehicles can gather and process environmental data to drive unaided.'

2. What is a digital image?

The interviewer may ask this question to test your basic understanding of the different components of computer vision. You can provide a brief explanation of a digital image. You can also mention how artificial intelligence uses digital images.

Example: 'A digital image is a digital version of a picture. It consists of many tiny parts, or pixels. A numerical component represents the specific colour code and intensity of each pixel. When an artificial intelligence system processes a digital image, it uses these numerical components to analyse and interpret the image. That's how it can 'see' the image and respond appropriately to it.'

3. What is greyscaling and what is its purpose?

Interviewers can ask this question to gauge your understanding of the use of digital images in computer vision technology. You can define greyscaling and briefly explain its purpose.

Example: 'Greyscale is the range of grey shades, from white at the lightest end of the scale to black at the darkest end. Greyscale consists of different shades of grey and no colours, which makes it monochromatic. The pixels in a greyscale contain only light intensity information, not colour information. Greyscaling is the process of converting a colour image into a greyscale image. It can simplify the image data to make it easier for computers to understand. Greyscaling facilitates image processing since the reduction in image dimensions also reduces the complexity of edge and contour detection functions.'

4. What is the HSI colour model and how is it useful for computer vision?

You can expect this question during a computer vision job interview due to the importance of the HSI colour model in computer vision. You can answer with a brief definition and explain its use in computer vision applications.

Example: 'HSI stands for hue, saturation and intensity. Hue refers to pure colours that have no tints or shades. Saturation is the amount of white light within a hue. The colours with high saturation contain little or no white, while low saturation colours contain a higher concentration of white light. Intensity refers to the brightness of colours and varies depending on the tint or shade.

The HSI colour model describes these colour attributes. In a colour image, it separates intensity from hue and saturation. It's useful in computer vision since its colour representation is similar to how the eye perceives colours.'

5. What programming languages are you proficient in?

You can expect the interviewer to ask you this question since it's necessary to know various programming languages to work with computer vision. In your answer, you can list the programming languages that you know. You can also mention any certifications you have in these languages.

Example: 'I'm proficient in Java, Python, C, C++, C#, MATLAB and Prolog. I have a Programming Language Certified Associate certification in C, a Certified Associate Programmer certification in C++ and a Microsoft Power Platform certification in C#. I also have an Oracle Certified Professional certification in Java. I learned Python, MATLAB and Prolog on my own and have used them in past projects, but I don't have certification for them yet. I'm currently learning LISP.'

6. What do you know about computer vision libraries?

When an interviewer asks you this question, they may want to test your understanding of how computer vision works. You can give a brief definition and explain the use of computer vision libraries.

Example: 'A computer vision library is a collection of pre-written code and data. There can be different computer vision libraries for different programming languages. Programmers can use the code and data in these libraries to build neural networks. Computers can then use these neural networks to identify and process image data.'

7. What are some of the skills you have developed as a computer vision engineer?

An interviewer may ask you this question to assess your understanding of the requirements of the available position. It is advisable to reread the job advertisement before the interview. In your response, align your existing skills to the employer's requirements.

Example: 'I have a strong knowledge of computer vision libraries and dataflow programming. I know how to create image analysis algorithms and build deep learning architectures. I also know how to design solutions for image processing and visualisations.'

8. What can a computer vision neural network detect?

This is a question that interviewers often ask during computer vision job interviews. Your response can demonstrate your knowledge of neural networks and their capabilities. It's advisable to monitor new developments in this field and adapt your answer to these changes.

Example: 'A neural network consists of several algorithms or rule sets that computers follow for performing calculations or undertaking other problem-solving operations. Computer vision neural networks work in a similar way to the human brain and can process data sets to detect underlying relationships within them. Computer vision neural networks can detect human faces, vehicles, landscapes and various other items. They can recognise colours, shapes, patterns and sizes. They can then assimilate these features and use them to develop logical rules. The computer can then use these rules to process data and identify images.'

9. What is OpenCV and how can you use the algorithms in it?

Interviewers may ask you this question to learn about your experience with OpenCV. You can respond by explaining what OpenCV is and then mentioning how you can use the machine learning algorithms in it.

Example: 'OpenCV stands for Open-Source Computer Vision. It's an open-source library containing thousands of optimised computer vision and machine learning algorithms. You can use these algorithms to build computer vision applications and improve machine learning. Among other things, the algorithms can help applications with facial detection and recognition, the classification of physical actions and the identification of objects.

You can use the computer vision and machine learning algorithms to track eye movements, camera movements and moving objects. They can help you search for similar images in databases. They can recognise and overlay scenery in augmented reality. They can also create high-resolution scenes by stitching images together.'

10. What is the Mach band effect?

When working in a computer vision role, you may face some common visualisation problems. Interviewers may ask this question to know how you might resolve them. You can answer by providing a definition of the Mach band effect and explaining how you could approach the issue.

Example: 'The Mach band effect is an optical illusion that the Austrian physicist Ernst Mach first described in 1865. You can see it in greyscale, where it creates an exaggerated contrast between the edges of the different shades of grey. Instead of seeing the sharp edge differences that exist between the dark and light grey shades, you see dark bands on the edge of the dark grey shades and light bands on the end of the light grey shades.

The Mach band optical illusion occurs when the light receptors in the retina capture an image and the eye subjects it to spatial high-boost filtering. That causes the lateral inhibition phenomenon. It means the eye sees more contrast in the adjacent grey shades than actually exists. That can cause incorrect calculations in computer vision. To mitigate the Mach band effect, you can try adjusting the image smoothness levels. That can reduce the banding at the edges and enable the computer to detect images more accurately.'