[Interview question for Computer vision] Part 2: Classical Computer Vision Interview Questions & Answers
Published:
Some interview questions and answers for the computer vision field. Part 2 shows some classical interview questions and answers.
Classical Computer Vision Interview Questions & Answers
What is the difference between Computer Vision and Image Processing?
Answer:
Computer Vision focuses on enabling machines to interpret and understand visual information from the world. It aims to replicate the human visual system to make decisions or perform tasks.
Image Processing involves manipulating or enhancing images for a specific purpose, such as improving the quality, extracting features, or applying filters.
Explain the concept of Image Thresholding.
Answer: Image Thresholding is a technique used to separate objects from the background in an image. It involves setting a threshold value, and pixels with intensity values above the threshold are classified as foreground (object), while those below are classified as background. It’s a crucial step in tasks like object segmentation.
What is the purpose of Edge Detection in Computer Vision?
Answer: Edge Detection is a fundamental process in Computer Vision used to identify the edges or boundaries in an image. It’s crucial because edges often correspond to important features in an image, such as object boundaries or textures.
Explain the concept of Hough Transform.
Answer: The Hough Transform is a technique used for detecting simple geometric shapes like lines, circles, or ellipses in an image. It converts points in an image to a parameter space, where the presence of a shape is detected as peaks in this space.
What is the Sobel Operator and what is its purpose?
Answer: The Sobel Operator is a popular edge detection filter used to approximate the gradient of the image intensity function. It highlights regions of rapid intensity change, which typically correspond to edges.
Explain the concept of Image Histogram.
Answer: An Image Histogram is a graphical representation of the distribution of pixel intensities in an image. It shows the number of pixels for each intensity level. Histograms are useful for tasks like contrast enhancement and thresholding.
What is Morphological Image Processing?
Answer: Morphological Image Processing involves operations on the shape or structure of an image. It includes operations like dilation (to expand regions), erosion (to shrink regions), opening (erosion followed by dilation), and closing (dilation followed by erosion). These operations are often used in tasks like noise reduction and object extraction.
Explain the concept of Image Segmentation.
Answer: Image Segmentation involves dividing an image into meaningful regions or segments based on the characteristics of the pixels, such as color, intensity, or texture. It’s used for tasks like object recognition and tracking.
What is Feature Matching in Computer Vision?
Answer: Feature Matching is the process of identifying and matching distinctive features (like corners, keypoints, or edges) between different images. This is used in tasks like object recognition, image stitching, and 3D reconstruction.
Explain the concept of Scale-Invariant Feature Transform (SIFT).
Answer: SIFT is a feature detection and description algorithm that identifies keypoints and extracts feature vectors which are invariant to scaling, rotation, and illumination changes. It’s widely used in tasks like object recognition and image stitching.