Computer Vision interview Question Part-1
1 – What are machine learning algorithms available in OpenCV?
The following are some of the machine learning algorithms available in OpenCV:
2 – How many types of image filters are there in OpenCV?
OpenCV is a library of programming functions for real time computer vision. It has more than 2500 functions, including image processing and analysis, machine learning, and video capture.
There are many types of image filters available in OpenCV, some of the most popular are: – Contrast and brightness, Bilateral Filter, Blur, Box Filter, Filter 2D, Gaussian blur, Median filter, Sharpen, Sobel filter, Deriv and gabor kernels and Laplacian.
3 – What are face recognition algorithms?
Face recognition algorithms are computer vision techniques that are used to identify or verify a person from a digital image or a video frame from a video source.
Face recognition is one of the most popular applications of computer vision. It has been used in law enforcement, military and other government applications, as well as in commercial applications like photo libraries and social media sites.
The most popular face recognition algorithm is called “OpenCV.” OpenCV can be used for facial detection, facial landmark detection, head pose estimation, emotion analysis and many other tasks.
Some other popular face recognition algorithms are:
1 – KNN (nearest neighbors) algorithm
2 – Eigen’s faces
3 – Fisher faces
4 – SIFT (Scale invariant feature transform) 5 – SURF (Speed up robust feature)
4 – What is “digital image?”
A digital image is a representation of a physical object that can be captured in the form of data and stored for later retrieval.
Digital images are often called “pictures” because they are made up of pixels, which are tiny dots that create an image when arranged in a grid. This is why digital images are also called “computer graphics.”
The purpose of digital imaging is to store and display pictures, such as photographs or illustrations, on computers. Digital imaging has many applications including medical imaging, satellite imagery, and movie making.
5 – What is the purpose of Gray scaling?
Grayscaling is a technique that is used to convert an image from color to black and white. It’s also called thresholding. Grayscale images are often used in medical imaging, satellite imagery, and other areas where the color of the object does not matter. The purpose of grayscaling is to convert images from color to black and white. This can be done by using a threshold value or by converting the RGB values into grayscale values.