20 OpenCV Interview Questions and Answers

OpenCV is a powerful open-source library for computer vision and machine learning. If you’re applying for a position that involves working with OpenCV, you’re likely to encounter questions about the library during your interview. Knowing how to answer these questions can help you demonstrate your expertise and land the job. In this article, we discuss some of the most common OpenCV questions and provide tips on how to answer them.

OpenCV Interview Questions and Answers

Here are 20 commonly asked OpenCV interview questions and answers to prepare you for your interview:

1. What is OpenCV?

OpenCV is a computer vision and machine learning software library. It is open source and free to use.

2. Can you explain what the different components of OpenCV are?

The different components of OpenCV are:

– The core module, which contains the basic image processing functions
– The highgui module, which contains the graphical user interface functions
– The imgproc module, which contains the image processing functions
– The video module, which contains the video processing functions
– The ml module, which contains the machine learning algorithms
– The objdetect module, which contains the object detection functions
– The flann module, which contains the algorithms for finding nearest neighbors

3. How does OpenCV compare to other computer vision libraries like SimpleCV and SciKit Image?

OpenCV is a more comprehensive and powerful library than both SimpleCV and SciKit Image. However, it can be more difficult to use and has a steeper learning curve.

4. What programming languages can be used with OpenCV?

OpenCV can be used with C, C++, Python, and Java.

5. Why do you think OpenCV is considered to be an image processing library?

OpenCV is considered to be an image processing library because it provides a wide range of functions for image processing and computer vision tasks. These tasks include things like object detection, image stitching, and optical flow. OpenCV also provides a wide range of features for working with images, including support for a variety of image formats and image filters.

6. When would you use OpenCV over a standard image editing tool like GIMP or Photoshop?

OpenCV is a computer vision library, which means it is designed for tasks related to analyzing and manipulating digital images. This makes it a good choice for tasks like object detection, facial recognition, and motion tracking. If you need to perform any of these types of tasks on images, then OpenCV would be a better choice than an image editing tool like GIMP or Photoshop.

7. What are some real-world applications that make use of OpenCV?

OpenCV is used in a wide variety of applications, including:

– Object detection and tracking
– Facial recognition
– Gesture recognition
– Motion estimation
– Image stitching
– 3D reconstruction
– Augmented reality
– And more!

8. Is it possible to perform object detection using OpenCV? If yes, then how?

Yes, it is possible to perform object detection using OpenCV. The most common way to do this is by using the Haar Cascade classifier. This classifier is trained on a set of positive and negative images, and can then be used to detect objects in new images.

9. Which algorithm is best suited for performing face recognition in videos?

The best algorithm for performing face recognition in videos is the Viola-Jones algorithm. This algorithm is able to detect faces in videos by looking for certain patterns, such as the shape of the face, the size of the eyes, and the position of the mouth.

10. What’s the difference between feature detection and edge detection algorithms?

Feature detection algorithms are used to identify specific features in an image, such as corners or blobs. Edge detection algorithms are used to find boundaries in an image.

11. What are histograms? What are they good for?

A histogram is a graphical representation of the distribution of data. It is a useful tool for visualizing data sets and for identifying patterns in the data. Histograms can be used to compare data sets, to find outliers, and to determine the distribution of data.

12. What is thresholding in the context of OpenCV?

Thresholding is the process of converting an image into a binary image, which is an image where each pixel is either black or white. This is done by comparing each pixel in the image to a threshold value, and if the pixel is above the threshold, it is turned white, and if it is below the threshold, it is turned black.

13. Can you give me some examples of where color segmentation techniques might be applied?

Color segmentation is a process of identifying and isolating objects in an image based on color. This can be useful for a variety of applications, such as identifying specific objects in a scene for object recognition, or for tracking objects as they move through a frame.

14. Can you explain what augmented reality means? Is it possible to achieve augmented reality using OpenCV?

Augmented reality is the integration of digital information with the user’s environment in real time. It is often used in gaming applications, but has potential applications in other areas as well. It is possible to achieve augmented reality using OpenCV, but it would require a lot of work to develop the necessary algorithms.

15. How would you go about extracting features from an image using OpenCV?

In order to extract features from an image using OpenCV, you would first need to convert the image into a grayscale image. Once the image is in grayscale, you can then use the Canny edge detection algorithm to find the edges in the image. After the edges have been found, you can then use the Hough transform to find lines in the image. Finally, you can use the Harris corner detection algorithm to find corners in the image.

16. What are another words for “corner” in an image?

There are a few different terms that are used to describe corners in images, including interest points, keypoints, and salient points. Corners are typically defined as areas of an image that have a high degree of variability in pixel values in at least one direction, which makes them stand out from the rest of the image.

17. What is the significance of epipolar lines in stereo images?

Epipolar lines are lines in an image that correspond to the same point in another image. They are used to help match up corresponding points between two images, which is essential for stereo vision.

18. What is your opinion on pattern matching as an approach to detecting objects in an image?

Pattern matching is a powerful tool for detecting objects in images, but it can be computationally intensive. In my opinion, it is best used in conjunction with other methods, such as edge detection or color histogram analysis.

19. How would you detect if one image is just a rotation of another without changing its orientation?

You could use a technique called SIFT (Scale Invariant Feature Transform) to detect if one image is a rotation of another. SIFT works by finding keypoints in an image and then creating a descriptor for each keypoint. The descriptor is invariant to image rotation, meaning that it will be the same for an image that has been rotated as for the original image. By comparing the descriptors for two images, you can determine if they are a rotation of each other.

20. What are keypoints? What are they used for?

Keypoints are used in image processing to identify certain features in an image. For example, you could use keypoints to identify corners or edges in an image. Keypoints can also be used to track objects in a video.