The manipulation of images is referred to as image processing. Those interested in computers and programming are likely to succeed in this industry. Therefore, if you are interviewing for a post in Image Processing, you should expect technical and experience questions. We have listed 25 common interview questions and answers related to image processing.
1. What Exactly Is Meant By “Image Processing”?
The manipulation of digital images is referred to as “image processing,” and it is typically done to enhance the images’ quality or add particular characteristics to them. This may entail anything as simple as color correction or the elimination of noise, or something as complex as detecting objects or stitching together images.
2. What Are The Primary Stages That Comprise The Image Processing Pipeline?
The following are the primary stages of an image processing pipeline:
- The first phase, called pre-processing, consists of various tasks, including image enhancement, noise reduction, and color correction.
- Segmentation: We will separate the image into parts using segmentation in this stage.
- Feature extraction: Pertinent picture features are extracted from various image regions during this process.
- Classification: During this stage, labels are applied to the various parts of the image.
- Post-processing: This step encompasses a variety of activities, including picture compression and output production.
3. Are You Able To Describe What A Convolutional Layer Is As Well As Its Function In The Context Of Image Processing?
In image processing, a specific kind of neural network layer known as a convolutional layer is frequently utilized. This layer is responsible for applying a convolutional filter to the image it is working on to extract features from the image it is working on. This filter is often comprised of a tiny matrix that is employed in the process of identifying particular visual elements. For instance, if you want to identify edges in a picture, you may use a convolutional filter 3 by 3.
4. How Exactly Do Fast Fourier Transforms Allow For The Computation Of A Convolution In An Effective Manner?
Using Fast Fourier Transforms, it is possible to efficiently compute a convolution by first doing the Fourier Transform on both the image and the kernel, then multiplying these two results together and performing the inverse Fourier Transform on the resulting product. Using the process known as the Fast Fourier Transform, one can accomplish this task in a relatively short amount of time.
5. In What Ways Are You Familiar With The Concept Of Gaussian Smoothing Filters?
To lessen noise’s impact on an image, smoothing filters based on the Gaussian distribution are utilized. To get this result, the image is convolved with a Gaussian kernel, a matrix employed in the process of blurring the image. Either the image’s quality can be improved with this technique, or it can be used to simplify the process of locating specific elements within an image.
6. Why Is The Extraction Of Features So Critical To The Image Processing Process?
One way to accomplish this is by feature extraction, which involves selectively discarding less crucial details from an image while keeping the relevant ones. This is crucial for the image processing process since it can help reduce the quantity of data that has to be processed, which in turn can assist speed up the process as a whole. In addition, the accuracy of image processing algorithms can be improved simply by identifying the most significant features of the overall picture.
7. In The Context Of Image Processing, What Exactly Does The Phrase “Normalization” Mean?
The process of altering the contrast of an image to get a more uniform distribution of the intensities of the image’s pixels is referred to as normalization. This may be accomplished by broadening or narrowing the range of intensity values, depending on personal preference. The visibility of a picture can be improved by the normalization process, which is also frequently employed as a pre-processing step for various additional image processing activities.
8. Could You Please Describe An Edge Detection Filter And How It Operates, Please?
One type of filter used in image processing is called an edge detection filter, and its purpose is to locate the boundaries of objects inside an image. This is accomplished by searching for spots that strongly contrast the individual pixel colors. The filter examines each pixel in an image and evaluates it in relation to the other pixels in the surrounding area. If there is a discernible change in hue or level of brightness from pixel to pixel, then the filter will identify that area as an edge.
9. What Sets Adaptive Thresholding Apart From Non-Adaptive Thresholding, And How Does It Work?
The primary distinction between adaptive thresholding and non-adaptive thresholding is that, with adaptive thresholding, the threshold value is calculated based on the pixels that are immediately adjacent to the area being analyzed, whereas, with non-adaptive thresholding, the threshold value is calculated based on the entirety of the image being analyzed. Since the threshold value will be variable for each pixel, adaptive thresholding can handle photos with varying lighting conditions more effectively than traditional thresholding methods.
10. What Are Some Of The Things That Come To Mind When You Hear The Word “Image Segmentation”?
The process of breaking up a picture into its parts, sometimes known as segments or regions, is known as image segmentation. Picture segmentation aims to reduce the complexity of or otherwise transform an image’s representation into something more straightforward, meaningful, and less difficult to evaluate.
11. What Are The Two Primary Categories Of Picture Segmentation Techniques That Are Available?
Methods based on regions of an image and methods based on edges are the two primary categories of image segmentation methods. Methods based on areas work by combining adjacent pixels into distinct regions. In contrast, methods based on edges look for abrupt changes in intensity or color to locate the boundaries between different areas.
12. Which color spaces are there, and how are they utilized to depict different kinds of images?
The RGB color space, the CMYK color space, and the Lab color space are the three color spaces utilized most frequently in the process of representing images. Since RGB is the color space that is used by computer monitors as well as digital cameras, it is the color space that is used for the majority of digital photographs. If you want to print an image, you’ll need to convert it to the CMYK color space, which printers use. The Lab colour space is a device-independent color space, which may represent colors on any device, including a monitor or a printer.
13. What Exactly Are The Advantages Of Using Image Processing?
Various image processing methods have produced significant change across different technological companies. Regardless of the industry in which it is used, image processing offers several features that are incredibly beneficial, including the following:
- The digital photograph can be rendered accessible in any format that may be preferred (improved image, X-Ray, photo negative, etc.)
- It contributes to the enhancement of images for human understanding.
- Images are capable of having information processed and taken from them for interpretation by machines.
- The density and contrast of the image can be adjusted to the user’s liking by manipulating the pixels.
- It is simple to save and retrieve digital photographs.
- It makes the process of sending photographs electronically to third-party sources quite simple.
14. What Exactly Is An Image?
The height and width of a picture are numerical representations of the number of pixels that make up the image. If an image’s width and height are 500 by 400, then the total number of pixels in the image is 200000. A pixel is a single point in a picture that has been assigned a certain brightness, hue, and/or color. Most commonly, it takes the following forms:
- Each pixel in a grayscale image has a numeric value between zero and one hundred fifty-five (0 is completely black, and 255 is entirely white).
- In RGB, a pixel is represented by three numbers between zero and 255. (the integers represent the intensity of red, green, and blue).
- To express the degree to which a picture is transparent, an alpha field was added to the RGB colour space, creating the RGBA colour space.
To process an image, conducting a series of operations on each pixel is necessary. The image processor carries out the first series of manipulations on the image. After this is finished, it will go on to the next task, and so on. Any image pixel can serve as an input for one of these computations and return an output value.
15. So, What Exactly Is An Image Processing Histogram?
Histograms of pixel intensities are the most common type of histogram used in image processing. The number of pixels in an image at various intensities is displayed graphically in this histogram. Since there are 256 levels of intensity in an 8-bit grayscale image, the histogram will graphically represent those 256 values to illustrate how evenly they are distributed. Even color images can have their histograms broken down into the individual channels of red, green, and blue, or a three-dimensional histogram created where the red, green, and blue axes each represent the number of pixels in that color, and the brightness of each point represents the number of pixels in that channel. The operation’s final result can be anything from a simple image of the desired histogram in a suitable image format to a data file containing the histogram’s statistics, depending on the implementation.
16. Is There A Connection Between The Two Fields Of Study—Image Processing And Computer Vision? If So, Then In What Way?
Image processing falls under the umbrella of computer vision as a subfield. A computer vision system will make use of image processing algorithms in an attempt to simulate human eyesight on a human-sized scale. Image processing could be said to be taking place if, for instance, the intention is to improve the image for use at a later time.
17. What Are Some Of The More Common Algorithms Used For Detecting Objects?
Object detection is used in computer vision to identify specific items inside an image or video. Machine learning and deep learning are frequently used in object detection techniques. Convolutional neural networks (R-CNN, which stands for Region-Based Convolutional Neural Networks), Fast R-CNN, and YOLO are three famous examples of algorithms that can be used for object detection (You Only Look Once). The R-CNN’s belong to the family of R-CNNs, whereas the single-shot detectors that YOLO belongs to belong to the family of single-shot detectors.
18. What Exactly Do You Comprehend When I Say The Phrase “Edge Enhancement”?
Edge enhancement is an image processing filter that aims to increase an image or video’s acuity by increasing the contrast along the image’s or video’s edges (apparent sharpness). The filter works by locating sharp edge boundaries in the image, such as the edge between a subject and a background of a color that contrasts with it, and then increasing the image contrast in the area immediately surrounding the edge. For example, the filter may identify the edge between a subject and a background of a color that contrasts with it. This creates faint, bright, and dark highlights on either side of any edges in the image, which are respectively referred to as overshoot and undershoot. This causes the edge to appear more defined when viewed from a standard distance.
19. What Is The Purpose Of Using Interpolation When Processing Images?
The process of moving a picture from one resolution to another without degrading the image’s quality is called interpolation. In image processing, picture interpolation is an essential function that may be used for zooming, enhancing images, resizing images, and many other operations.
20. What Exactly Is Chromatic Adoption?
Chromatic adaptation is the capacity of the human visual system to disregard the colour of the illumination and to maintain, to a certain extent, the appearance of an item in different lighting conditions. Examining a white object under various lighting conditions, such as natural daylight and artificial incandescence, will allow one to see the chromatic adaptation.
21. What Exactly Does “Dynamic Range” Mean When It Comes To Image Processing?
The amount of tonal variance inside an image from its lightest light to its darkest dark is referred to as the image’s dynamic range. The dynamic range is not always proportional to the number of tones that may be reproduced; nonetheless, the number of potential shades that can be represented increases in direct proportion to the dynamic range. For instance, microfilm with high contrast can capture a wide dynamic range but only a limited number of tones. The capability of a digital system to reproduce tonal information is another aspect described by dynamic range. This feature is most significant for continuous-tone texts that exhibit smoothly fluctuating tones. It is likely the one factor that contributes the most to the overall image quality for images.
22. How Does The Huffman Algorithm Work?
One of the fundamental compression techniques shown to be influential in developing image and video compression standards is Huffman coding. When the Huffman encoding technique is used on an image, the source symbols can either be the pixel intensities of the image itself or the result of an intensity mapping function. Both of these options are valid.
23. Simply Put, What Is A Pixel?
On a computer display or in a computer image, the fundamental unit of programmable colour is called a pixel, a word invented from the phrase “picture element.” Instead of thinking of it as a physical unit, think of it as a logical one. Pixels are the smallest unit of a digital display. An image or video played on a device’s screen can be composed of up to millions of individual pixels. Each pixel is composed of a subpixel that produces a red, green, and blue (RGB) colour at varying intensities. These colours are displayed on the screen. The spectrum of colours seen on a screen or computer monitor is composed of three primary hues: red, green, and blue, or RGB. Numbers such as 1920 x 1080 refer to the total number of pixels in an image when discussing the resolution of a display.
24. What Exactly Is A Greyscale Image?
The brightness of a pixel can be determined by looking at its grey level, also known as its grey value. The level of grey that is least visible is 0. The image’s level of digitization determines the maximum grey level that can be achieved. For an 8-bit-deep image, it is 255. A pixel in a binary image can only have the value 0 or the value 255 associated with it at any given time.
25. In Image Processing, What Does The Term “Filter” Refer To?
Image editing and enhancement can be accomplished by using a process known as filtering. You could, for instance, filter an image in such a way as to highlight certain aspects of it while downplaying others. Image processing procedures such as smoothing, sharpening, and edge enhancement can all be achieved by applying to filter.
Conclusion
Our list ends here. I hope you’ve grasped the topics better by reading these questions and answers. Apart from the questions, you should also study the company you’re going to be interviewed for and incorporate as much experience that will relate to the company.