OpenCV Test

The OpenCV Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

• Implementing image processing algorithms
• Applying computer vision techniques
• Utilizing OpenCV functions and libraries
• Performing feature detection on images
• Recognizing objects in images
• Applying filters to enhance images
• Segmenting images for analysis
• Calibrating cameras for accurate measurements
• Stitching multiple images together
• Analyzing image quality and enhancing it

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

• Image Processing

  Image processing involves manipulating or altering digital images to improve their quality or extract meaningful information from them. This skill is measured in the test to assess the candidate's ability to apply various techniques and algorithms to process images effectively.

• Computer Vision

  Computer vision is the field of study that focuses on enabling computers to understand and interpret visual information from digital images or videos. Measuring this skill in the test helps evaluate the candidate's understanding of computer vision concepts and their proficiency in applying computer vision algorithms to analyze visual data.

• Feature Detection

  Feature detection refers to the process of identifying and locating specific features or patterns within an image. This skill is measured to gauge the candidate's ability to identify and extract relevant features from images, which is crucial for tasks like image recognition, tracking, and matching.

• Object Recognition

  Object recognition involves recognizing and classifying objects or specific patterns within an image or video. Measuring this skill in the test helps assess the candidate's knowledge of object recognition algorithms and their ability to utilize such techniques for tasks like object detection, classification, and localization.

• Image Filtering

  Image filtering pertains to the process of enhancing or altering images by applying various filters or convolutional operations. This skill is measured to evaluate the candidate's proficiency in performing image filtering operations using different filters like blur, sharpen, edge detection, etc., and understanding their impact on image quality and appearance.

• Image Enhancement

  Image enhancement involves improving the visual quality or information content of an image to make it more visually appealing or suitable for further analysis. Measuring this skill in the test helps ascertain the candidate's ability to apply appropriate enhancement techniques, such as contrast adjustment, noise reduction, and histogram equalization, to enhance image details and overall quality.

• Image Segmentation

  Image segmentation refers to the process of partitioning or dividing an image into meaningful regions or segments based on specific criteria or image properties. Measuring this skill in the test allows the assessment of the candidate's capability to apply algorithms and techniques for image segmentation, which is essential for tasks like object recognition, scene understanding, and image editing.

• Camera Calibration

  Camera calibration involves estimating the camera parameters that enable mapping of 3D points to their corresponding 2D image coordinates. This skill is measured in the test to evaluate the candidate's understanding of camera calibration techniques and their ability to calibrate cameras for tasks like 3D reconstruction, pose estimation, and augmented reality.

• Image Stitching

  Image stitching is the process of combining multiple overlapping images to create a single panoramic image. Measuring this skill in the test helps assess the candidate's knowledge of image stitching algorithms and their ability to accurately align and blend multiple images to produce seamless panoramas.

• Full list of covered topics

  The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for OpenCV Test to be based on.

  Image acquisition

  Image resizing

  Image rotation

  Image translation

  Image cropping

  Image histogram equalization

  Image thresholding

  Image blurring

  Image sharpening

  Image edge detection

  Image gradient calculation

  Image contour detection

  Image shape analysis

  Image morphological operations

  Image noise reduction

  Image artifact removal

  Image smoothing

  Image segmentation techniques

  Camera calibration

  Camera distortion correction

  Feature detection

  Feature extraction

  Object recognition

  Template matching

  Histogram of Oriented Gradients (HOG)

  Scale-Invariant Feature Transform (SIFT)

  Speeded-Up Robust Features (SURF)

  Local Binary Patterns (LBP)

  Corner detection

  Edge detection

  Image filtering

  Mean filter

  Median filter

  Gaussian filter

  Bilateral filter

  Non-local Means filter

  Histogram equalization

  Contrast stretching

  Gamma correction

  Color space conversion

  Histogram manipulation

  Image enhancement

  Histogram matching

  Brightness adjustment

  Contrast adjustment

  Sharpness adjustment

  Image segmentation

  Thresholding techniques

  Region-growing

  Watershed transformation

  Connected component labeling

  Contours and boundary extraction

  Image stitching

  Feature point detection

  Feature point matching

  Image alignment

  Bundle adjustment

  Perspective rectification

  Homography estimation

• Computer Vision Engineer
• Image Processing Engineer
• Data Scientist
• Machine Learning Engineer
• Software Developer
• Research Scientist

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

• Implementing pattern matching algorithms
• Applying geometric transformations to images
• Working with depth maps and 3D reconstruction
• Utilizing machine learning for image analysis
• Understanding image histogram and equalization
• Optimizing image processing algorithms
• Handling image noise and artifacts
• Working with image compression algorithms
• Utilizing parallel computing for faster processing
• Developing custom image processing techniques
• Implementing real-time image processing systems