Computer vision is the field of study concerned with the application of computers to human visual perception, especially in situations that need the knowledge of the object and context for accurate machine behavior.
Are you interviewing for a new role in this field? Computer vision interviews can be tricky. You will face questions that test your field knowledge and problem-solving ability. Having a basic understanding of the field will help you answer questions as well as help you pass the computer vision interview. In this article, you will find a list of frequently asked questions with answers you can use as a practice guide. Questions like:
1. Explain The Intersection Over Union Method Used To Evaluate An Object Localization Model?
Intersection over union is a method used to evaluate an object localization model by comparing it with ground truth. In other words, the model is compared with a photo of the object taken from a camera.
This comparison is made by using a computer vision algorithm to find the object’s location in the image and then comparing it with its actual location on the photo. The algorithm will then return an error value that indicates how close the two match.
If there is a need to evaluate more than two locations, there would be another step where all of them are compared and averaged out to evaluate one point at a time.
2. Mention The Applications Of OpenCV?
OpenCV is an open-source computer vision library written in C++ and released under the MIT License. It provides a wide variety of functions for real-time computer vision applications, such as face detection and tracking, optical flow, object recognition, and monitoring, etc.
Many commercial products, such as Google Glass and Microsoft Kinect, use OpenCV.
The applications of OpenCV include:
- Real-time face recognition
- Video conferencing, speech recognition, and translation
- Automatic car navigation systems and self-driving cars
3. What Are The Features Of OpenCV?
OpenCV is one of the best open-source computer vision libraries. It contains several algorithms for object detection, tracking, and segmentation. OpenCV also has a library for face recognition.
The following are the features of OpenCV:
- Object detection: OpenCV provides various algorithms for object detection. The most popular ones are Haar cascade, SIFT, and SURF.
- Tracking: OpenCV provides various tracking algorithms such as HOG, K-SVD, GIST, and DFT (Dense Feature Table) for object tracking.
- Face detection: detect faces in images
- Motion detection: track moving objects in images;
- Stereo matching: create 3D models from 2D images.
- Segmentation.
4. In Computer Vision, How Can One Convert An Analog Image Into A Digital One?
Analog-to-digital conversion is converting an analog signal into a digital form. It is achieved by sampling the signal and then quantizing it at a certain number of discrete values (1 through 8). This process is called digitization.
The most common example is an image sensor that converts light into digital numbers. The sensor may have many more pixels than the actual physical pixels in the camera lens or sensor itself. Each pixel on the sensor has its transistor and capacitor connected to a reference voltage Vref.
5. What Are The Uses Of Object Landmark Detention, Object Verification, Object Segmentation, And Object Classification?
Object landmark detention, object verification, object segmentation, and object classification are the four main tasks of an image processing system. Object landmark detention is the first step in an image processing system to identify an object in a scene of a given size.
The object verification task follows after the detection of an object. This process determines if an object exists in an image or not. If there is no object in the picture, it classifies as noise or is non-existent. If there are two or more objects present in the image, the images are verified to decide which one is real and which two are not real but just some kind of noise.
Image segmentation divides an image into smaller parts based on its characteristics, such as shape, size, etc. In contrast, classification refers to assigning values (classes) to each element based on attributes like color, texture, etc.
6. When Do You Use Anchor Boxes?
Anchor boxes help determine the best location for a particular image. An anchor box often determines which part of the image is important or valuable.
Anchor boxes are also often used when creating a model for computer vision applications. For example, if you want to create an application that uses facial recognition, you will need to create a model that can recognize specific vital points on a face.
These points may include the eyes, nose, and mouth area. Recognizing these points requires that you create a model that provides for these areas and then use it as part of your program.
7. What Is A Computer Vision Neural Network?
A computer vision neural network is an artificial neural network that uses layers of neurons to mimic the behavior of the human brain. The idea behind computer vision neural networks is to make them easier to train and use in applications like self-driving cars.
The first layer in a computer vision neural network is the input layer, which contains the input data that goes into the network. The second layer of a computer neural network is the hidden layer, which represents information that the previous layers have not yet processed. The third and final layer in a computer vision neural network is called the output layer, which describes how well your model learned from each input image.
8. What Is Dynamic Range?
A dynamic range is a difference in brightness between the darkest and lightest tones in an image. The dynamic range is one of the ways that describe how much contrast there is between different values in a picture. A low dynamic range image will have very little difference between bright and dark areas compared with a high-dynamic-range image
For example, a dynamic range of 10 is equivalent to comparing a black-and-white sheet of paper to a sheet of white paper. In contrast, a dynamic range of 100 would be equivalent to comparing the difference between an image that is entirely black and completely white.
9. What Are The Main Steps In A Typical Computer Vision Pipeline?
In computer vision, a typical computer vision pipeline consists of four steps: pre-processing, feature extraction, feature selection, and classification.
10. What Are The Languages Supported By Computer Vision?
The languages supported by computer vision include OpenCV, Python, Java, MATLAB, and C++. These languages provide various ways of creating a program for the camera.
11. Is It Possible To Use Machine Learning Algorithms In OpenCV?
Machine learning algorithms are often used to classify objects in an image. This process is usually called object detection and recognition.
Before using machine learning algorithms in OpenCV, we must first create a model that looks like the objects. This following process is done by using a trained set of images carrying labels of their classification. We then use this trained model to make predictions on new images.
The idea behind using a trained neural network is that it learns from past examples and generalizes these examples to new ones. So if you’re trying to detect cars in an image, the network would know that cars are generally black and white, and there should be one behind every corner.
12. What Is The Purpose Of Gray Scaling?
Grayscaling is the process of converting color content in an image to grayscale. For example, when you look at a photograph, the colors are represented by red, green, and blue. The grayscale representation uses only black and white.
The primary purpose of grayscaling is to reduce the amount of data that needs processing during image processing. Grayscaling increases the efficiency of image processing algorithms by reducing the number of bits required for each channel (red, green, and blue).
13. What Is The Difference Between Feature Detection And Feature Extraction?
Feature Detection is the first step in object detection. It detects objects in an image by looking for local features, such as corners, lines, or colors.
Feature Extraction is the second stage of computer vision. It extracts meaningful information about an object from its surroundings and outputs it to other models (e.g., learning neural networks).
14. Can You Explain What A Color Model Means?
The color model describes the numerical representation of colors and how these colors are used to represent images. In the RGB model, red, green, and blue values represent the intensity of each pixel. In the HSB color model, hue, saturation, and brightness represent a pixel.
We use color models for many different reasons. One reason is that there may not be an existing standard for a particular application. Another reason is that there may be exceptional performance or quality requirements the color model needs to meet.
15. Can You Explain What You Understand By Computer Vision?
Computer vision is a field of artificial intelligence that trains computers to interpret and comprehend the visual world.
It leverages artificial intelligence (AI) to allow computers to obtain meaningful data from visual inputs such as photos and videos. The insights gained are then used to take automated actions. Like AI gives computers the ability to ‘think,’ the insights gained from computer vision give an automated action, i.e., it gives computers the ability to see, understand, and extract data.
16. Can You Explain Some Of The Applications Of Computer Vision And How It Works?
Computer vision is used in transportation and the autonomous industry. In the automotive industry, computer vision is used to detect and classify objects and create 3D maps or motion estimations.
It plays a very vital role in this industry. Such as in self-driving cars, which collect data from their environments and give an automated response.
Computer Vision works almost like the human brain; the only difference is that humans can recognize visual information without much training.
17. What Are Some Of The Advantages Of Computer Vision?
Some of the advantages of computer vision include:
- The systems can perform repetitive tasks faster without human intervention, making them more efficient than humans.
- It reduces costs.
- Natural language processing.
- It delivers high-quality products.
18. Why Are You Interested In This Field, And What Improvement Can You Make?
Computer vision is a sub-field of artificial intelligence. I have always been amazed by the wonders of artificial intelligence. I have even witnessed some of the mind-blowing applications of AI. My curiosity has turned into a passion; I want to fully invest my time in all the innovative experiences this field has to offer.
19. What Skills Do You Possess?
I believe I have the skillsets to help me succeed in this field. My Technical skills aid my proficiency in tools and computer science concepts. Also, I have analytical and problem-solving skills. I can break down large and complex problems into simple, manageable ones.
My communication skills help me relate well with a team in analyzing problems and arriving at solutions.
20. What Are The Different Computer Vision Algorithms?
There are different computer vision algorithms or techniques. Some have standard patterns but specific planning and considerations.
Some examples of these algorithms are:
- Open Detection: defines the objects in an image, labels them, and outputs bounding boxes.
- Image Classification: it is a subdomain that categorizes and labels groups of vectors within an image.
- Object tracking: this is the ability to estimate or predict the position of a target object.
- Image reconstruction: it is the process of capturing the shape and appearance of natural objects.
- Instance segmentation: it is the technique of detecting, segmenting, and classifying every individual object in an image.
- Semantic segmentation: this enables us to differentiate different objects in an image.
21. What Do You Understand About Digital Images?
A digital image represents an actual image as a set of numbers. The digital image translates this image to numbers by dividing it into small areas called pixels, i.e., picture elements.
The computer memory stores these pixels as a raster image or raster map, a two-dimensional array of small integers.
22. What Do You Understand By Image Processing?
Image processing is the field of enhancing images by tuning many parameters and features of the pictures. It helps to find various patterns and aspects in images, just as the name implies.
Standard image processing methods include image enhancement, restoration, compression, and encoding. These processes enable transformations on the image to give the desired output image.
23. What Are The Limitations Or Disadvantages Of Computer Vision?
Computer vision has not only advantages but also limitations. One such is that it needs more highly trained professionals with deep knowledge of AI. This field lacks experts who know all the differences between AI, machine learning, and deep learning.
Also, it requires regular monitoring since a technical glitch or breakdown can result in unimaginable losses for organizations. Organizations that use computer vision must have a team dedicated to monitoring and evaluation.
24. What Is The Difference Between Semantic Segmentation And Instance Segmentation In Computer Vision?
Semantic segmentation treats multiple objects within a single category as one entity, while instance segmentation treats various objects of the same class as distinct individual instances.
25. What Can You Say About Linear And Non-Linear Filters?
A linear filter is a mathematical operation that takes one input value and outputs another value proportional to the input value. The output value of the linear filter is called the predictor or coefficient, which describes a constant or slope. Linear filters are helpful in analog to digital conversion.
A non-linear filter has an adjustable parameter or function that can change the output value of the filter based on different input values. Non-linear filters are used to adjust pixel values based on pixel values from surrounding pixels. For example, if you want to modify an image based on what is around it, you would use a non-linear filter such as a blur filter or gaussian blur filter.
Conclusion
If you’re worried about how you’ll do in your interview, don’t be. Follow these tips instead.
- Find out as much information as you can about the company. The more you learn about them, the better you understand what makes them tick. It will help you give the answers the interviewers want to hear.
- Prepare for the interview and read all the information you can about the job, then develop a plan of action that will help you get hired. Anticipate and prepare for a variety of situations.
- Know what makes you different from everyone else who applied for the job, and portray that uniqueness during the interview.
- Practice answering technical and behavioral questions. Do not limit yourself to a few questions. The computer vision field is very vast, intensive, and practical.
- Write down all your thoughts, feelings, and experiences, making them easy to find when needed.
- Arrive early for the interview. Settle in, take in the environment, and get set for the discussion. Good luck!