Computer vision is worth your time if you are interested in machine learning and artificial intelligence. It is a specific field of AI that allows computers to comprehend and process images from different sources, such as real-time feeds and digital inputs. It gives computers sight, allowing them to observe and understand different details.
This article will look at some questions you should expect in a computer vision interview to increase your chances of landing any related job. Make sure that you fully understand all the technical aspects of this branch of AI, and you will be good to go. Take a look at the following:
1. What Do You Understand By Computer Vision?
Computer Vision is one of the main branches of artificial intelligence. It allows the computer to see, observe, understand, and extract data from digital images, visual inputs, and videos. It also enables systems to act on the extracted data, complementing artificial intelligence, a discipline of Computer Science that allows computers and systems to think.
2. How Does Computer Vision Work?
The working mechanism of computer vision CA is similar to that of normal human sight. However, humans can differentiate objects and determine their direction and distance without much training. With computer vision, computers and systems are trained to tell objects apart and derive any other important information. It also uses data, cameras, and algorithms in relatively less duration. Computer vision enables computers to process several products and processes in a minute, identifying defects and issues that need to be solved, making it more detailed and powerful than human sight.
3. Can You Mention Some Of The Applications Of Computer Vision?
There are several applications of computer vision. However, its use in self-driving cars such as Tesla is the main and most common. Such vehicles depend on computer vision to understand and extract important information from their cameras and army of sensors. It helps identify other cars, road users, traffic signs, lane markers, and other important visual information. One of its main applications, therefore, is in the automotive industry.
4. Give Examples Of Computer Vision
One of the most common examples of computer vision is Google Translate. Through this function, Google allows different smartphone users to point their cameras at signs written in different languages and instantly get a translation in a language of their choice. Besides Google, IBM’s My Moment, widely used in the 2018 Masters Golf Tournament, is a classic example of computer vision. It enabled the viewing of hundreds of hours of Masters footage and identification of different sights and sounds. Fans accessed curated moments as personalized reels.
5. Why Are You Interested In This Field?
I have always been wowed by artificial intelligence. I have witnessed some of the mind-blowing applications of AI, given that I have been surrounded by software and data engineers throughout my life. I decided to pursue Ai to gain more insight and understanding of different aspects. I particularly developed an interest in computer vision after the 2018 Masters Golf Tournament when IBM used it to develop My Moment, a system that identified special moments which were then curated and shared with the public and fans as reels. The more I uncover the power of AI, the more my interest grows.
6. Can You Mention The Advantages Of Computer Vision?
Given that it is a special branch of Artificial Intelligence, computer vision comes with several benefits. It can automate multiple tasks without human intervention, thus treating organizations to faster and simpler processes. It can also conduct repetitive tasks at a relatively faster rate. It significantly reduces costs that would have otherwise been used in data manipulation. It further saves organizations from spending huge amounts on correcting faulty processes. Lastly, computer vision systems deliver high-quality products and services, given that they don’t have any room for mistakes.
7. Mention Some Of The Disadvantages Of Computer Vision
Despite the advantages we’ve just mentioned, this type of artificial intelligence also has its shortcomings. First, it requires regular monitoring since a technical glitch or breakdown can result in unimaginable losses. Organizations that use computer vision must always have a dedicated team on standby for monitoring and evaluation. Second, this field lacks specialists since, to be a computer vision expert, one must know and understand all the differences between AI, machine learning, and deep learning, which may not be as easy as it sounds.
8. How Can One Convert An Analog Image Into A Digital One?
Converting an analog image to a digital image is known as digitizing. It is not challenging, provided that the right steps are followed. In doing so, one should perform two operations: sampling and quantization. Sampling deals with coordinates, converting those in the analog images to digital images. On the other hand, quantization targets intensity or amplitude as it converts either of the two into digitized forms for digital images.
9. What Qualifications Do You Have In This Field?
I undertook a deep learning course certification training which also covered Keras and TensorFlow. It taught me all the important concepts of deep learning and how to use different models and frameworks for deep learning algorithms implementation. I deeply understand the differences between artificial intelligence, machine learning, and deep learning, with experience in all of them. I can train computer vision systems easily, thanks to all the learning I have obtained in this field.
10. Can You Mention The Different Computer Vision Algorithms?
Computer vision algorithms provide means of understanding objects in given digital inputs such as images or videos. They obtain high-dimensional data and convert them to numeric or symbolic information. Most of these algorithms aid in object identification in photographs. They include object identification which identifies the type of object found in the photograph; object classification, which identifies the main category of a given object in a photograph and object recognition, which identifies the object present in digital input and their location.
11. Can You Mention The Uses Of Object Landmark Detention, Object Verification, Object Segmentation, And Object Classification?
Those are computer vision algorithms that understand the objects found in a digital image or output. Object Landmark Detention identifies the key points of an object in a photograph, while object verification tells whether the object that needs to be identified is present in the photo or not, making it the first algorithm to be relied on. On the other hand, object segmentation identifies the pixels that belong to an object in the digital input. Lastly, the object classification algorithm identifies the main category of the object found in a particular photograph.
12. What Do You Understand By A Digital Image?
Computer Vision extracts information from digital images. Digital images generally comprise picture elements, widely known as pixels. These pixels are often organized in a rectangular fashion, meaning that a digital image’s size is highly dependent on the dimensions of the pixel array. Additionally, the image width is directly associated with the number of columns in the pixel array, and the height determines the number of rows in the pixel. A digital image represents a real image as a set of numbers, making it possible to be stored and computed by a digital computer.
13. What Do You Understand By OpenCV?
OpenCV is a huge library of programming functions used in real-time computer vision. It comes in handy in machine learning and image processing. It mainly processes images and videos, identifying faces, objects, and even human handwriting. It can also be integrated with several libraries, allowing Python to process the OpenCV array structure, which aids in analysis. Identification of image patterns and features is then done through vector space and a number of mathematical operations. It is also worth mentioning that this collection of libraries has C++, Python, Java, and C interfaces and supports several operating systems such as Windows, Linus, Mac, iOS, and Android, which is an added advantage.
14. What Are The Features Of OpenCV?
OpenCV has a number of features responsible for its wide application and usage. First, it is open-source, meaning that it can be easily accessed by users who don’t have to pay for it. This is, in fact, one of its advantages. It also comes with C, C++, Python, and Java Interfaces, capturing all the major programming languages. Additionally, it can support Linux, iOS, Android, Mac OS, and Windows operating systems. Lastly, OpenCV enjoys wide usage with over 7.5 million recorded downloads. Owing to the number of downloads, you can be sure of accessing a number of related resources.
15. Mention The Applications Of OpenCV
There are several applications of OpenCV, given that it is used in image processing. They include interactive art installation, face recognition, TV adverts recognition, recording the number of vehicles on highways and their speed, street view image stitching, counting the number of people, medical image analysis, object recognition, 3D structuring of movies from motion, image search, and retrieval, defect detection during manufacturing and automated surveillance and inspection.
16. What Do You Understand By Image Processing?
As the name implies, image processing is the performance of different operations on an image. It allows computer systems to get an enhanced image or extract insightful and useful information from it. It can also be defined as the process of analyzing and manipulating digital images to improve their quality and identify different objects. It is conducted in three basic steps. First, the image is imported, after which it is analyzed and manipulated. The output is then obtained, which can either be an altered image or a report of the image analysis.
17. How Is Opencv Applied In Egomotion Estimation And Gesture Recognition?
Gesture Recognition, a sub-branch of computer vision, identifies and interprets human gestures via mathematical algorithms. These features are normally obtained from bodily motion and state, mostly targeting the face and hand. In Egomotion estimation, the focus is on the 3D motion of a camera within a given environment. Here, a camera’s motion relative to a rigid scene is measured or estimated. It comes in handy in self-driving cars whose moving positions can be estimated with respect to road signs to prevent accidents. This goes to show just how powerful computer vision is.
18. What Are Some Of The Advantages Of OpenCV?
OpenCV comes with a number of advantages worth exploring. They also explain its use in computer vision. For beginners, it is easy to use and learn, making it quite popular. Another significant advantage that companies tend to enjoy is that it is free to use, given that it is open source. You don’t need any subscription or regular fees. OpenCV is also compatible with several leading programming languages such as Python, C++, and Java. Lastly, there are several tutorials on OpenCV, widening the number of resources that can be used in learning.
19. When Do We Use Anchor Boxes?
Anchor Boxes are important tools in computer vision testing. They are normally used for object detection. A computer vision technician or expert uses them to isolate different image elements after image processing, such as the image size, location, and shape, which are then given values. Anchor boxes come in handy when an image to be visualized has several variables.
20. You Mentioned That You Have Vast Experience In Machine Learning. Can You Tell Us Whether It Is Possible To Use Machine Learning Algorithms In OpenCV?
Given OpenCV’s compatibility, it is possible to use an array of machine learning algorithms in OpenCV. I have used several techniques, including decision tree learning, k-nearest neighbor, gradient boosting trees, and convolutional neural frameworks. I have also used naive boxes, which define class labels with easily returnable values after an evaluation to resolve defects and bugs. All in all, the machine learning algorithm used on OpenCV is normally dependent on the function one is targeting.
21. What Do You Understand By The Match Band Effect?
The match band effect is a visualization issue where an optical illusion occurs when the edges of similar images have the same shade of grey. Due to that, the eye automatically adjusts, identifying and interpreting a higher contrast than what is factual. It is a defect in computer vision that can result in wrong calculations. Therefore, it is imperative that technicians make necessary adjustments to smoothen the images, reducing the detected banding effect and making image processing more accurate.
22. What Is A Computer Vision Neural Network?
A Computer Vision Neural network is a type of artificial neural network used in machine learning. Generally, these neural networks mimic how a human brain functions. Computer Vision Neural networks, therefore, mimic how the brain manipulates and processes images. It detects a number of images such as landscapes, vehicles, human faces, and different household items. Owing to the significant improvements that have been made to AI over the years, this network can also detect several features such as color, shape, patterns, and sizes. However, they are yet to detect dimensions and distinctive features.
23. What Do You Understand By Face Recognition Algorithms?
As the name implies, face recognition algorithms are used to track, detect, identify and verify the faces of human beings from images or videos obtained from digital cameras. It is a computer application mostly used in identifying crime suspects or for monitoring and surveillance. Some popular face recognition algorithms include fisher faces, Eigen’s faces, SURF, fully known as Speed Up Robust Features, and SIFT, or Scale Invariant Feature Transform. People also use PCA, Principal Component Analysis, the k-NN algorithm, and LBPH, or local binary patterns histograms.
24. Define Dynamic Range And Mention The Uses Of Sampling And Quantization
Dynamic range is used in sounds, light, photography, and signals. It can be defined as the ratio of small and large values that a given quantity assumes. In photography, the dynamic range is the ratio of the maximum and minimum measure of light intensity or the darkest or most exposed regions. Dynamic range, in this case, can mean color contrast. To answer the second part of the question, sampling and quantization come in handy in converting analog images to digital images. Sampling refers to the digitization of image coordinates. It converts these coordinates from analog to digital. Quantization refers to the digitization of amplitude or intensity. The two must be met for an image to be successfully digitized.
25. Mention The Common Established Computer Vision Tasks
There are four main established computer vision tasks: image classification, object detection, object tracking, and content-based image retrieval. In image classification, a system accurately predicts the class of a given image, while in object detection, an object is identified after image classification has identified the right image class, given that these two tasks go hand in hand. In object tracking, an object is followed after detection. It is mostly done using sequenced images or real-time video feeds. Lastly, content-based image retrieval applies computer vision to search, retrieve and browse images from data warehouses.
Conclusion
These top 25 computer vision interview questions and answers sum up most of the interview areas in computer vision interviews. We urge you to prepare adequately for your interview to have the upper hand. We wish you all the best in your upcoming interview!
