Image Processing & Computer Vision Concepts
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Image Processing & Computer Vision Concepts

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Questions and Answers

What is one of the primary roles of the essential matrix in stereo vision?

  • Quantifying the fundamental matrix
  • Representing the 3D transformation between stereo cameras (correct)
  • Describing the camera's internal parameters
  • Correcting lens distortion in images

    • What significant challenge is NOT typically addressed by the Lucas-Kanade method in motion analysis?

  • Object recognition (correct)
  • Illumination changes
  • Large displacements
  • Camera calibration

    • Which effect does radial distortion have on camera calibration?

  • It has no impact on camera calibration.
  • It reduces the computational complexity of calibration algorithms.
  • It introduces errors in the mapping between 3D and 2D coordinates. (correct)
  • It improves depth estimation accuracy.

    • In the context of computer vision, what does image registration primarily involve?

    <p>Aligning images from different sources or time points</p> Signup and view all the answers

    What is one of the challenges addressed by the Laplacian of Gaussian (LoG) edge detector?

    <p>Noise sensitivity</p> Signup and view all the answers

    What is the process of moving a filter mask over an image and computing the sum of products at each location called?

    <p>Convolution</p> Signup and view all the answers

    Which operation can be used for edge localization to enhance the detection of edges in an image?

    <p>Laplacian filtering</p> Signup and view all the answers

    Which of the following could potentially alter a camera's intrinsic parameters?

    <p>Irregular pixel shapes</p> Signup and view all the answers

    The theory of mathematical morphology is primarily based on which concept?

    <p>Set theory</p> Signup and view all the answers

    What effect does a low-pass filter have on an image?

    <p>Reduces noise and blurs edges</p> Signup and view all the answers

    What is the purpose of the Hough transform in image processing?

    <p>Fitting shapes as lines</p> Signup and view all the answers

    Which of the following statements about affine transformation is true?

    <p>It can include rotation, translation, and scaling</p> Signup and view all the answers

    Which technique uses shape templates to detect specific object patterns in an image?

    <p>Hit-or-miss transformation</p> Signup and view all the answers

    Smoothing an image can be effectively achieved by convolving which type of kernel?

    <p>Gaussian kernel</p> Signup and view all the answers

    What is a key feature of mathematical morphology?

    <p>It manipulates shapes within an image</p> Signup and view all the answers

    In feature extraction, which methods are commonly utilized to detect keypoints in images?

    <p>SIFT</p> Signup and view all the answers

    Which of the following is primarily concerned with the intrinsic parameters of a camera?

    <p>Focal length and sensor size</p> Signup and view all the answers

    Which characteristics are true for the Harris Corner Detector?

    <p>Rotation invariance</p> Signup and view all the answers

    Which feature extraction method focuses on identifying corners and edges within an image?

    <p>Edge-based feature extraction</p> Signup and view all the answers

    What is the relationship between disparity and depth in binocular vision?

    <p>Disparity is inversely proportional to depth</p> Signup and view all the answers

    Which interpolation method uses the intensity values of the nearest pixels to determine a new pixel's intensity?

    <p>Nearest neighbor interpolation</p> Signup and view all the answers

    Study Notes

    Image Processing & Computer Vision

    • Smoothing in the frequency domain of an image is achieved by suppressing high-frequency components.
    • High-pass filters sharpen details, while low-pass filters reduce sharpness.
    • Gaussian low-pass filters are a type of low-pass filter that smooths images.
    • The second derivative of an image has a zero response at the onset of a gray level step and flat segments, but a non-zero response during ramps.
    • Bilinear interpolation uses the intensity of the four neighboring pixels to determine the intensity at a new location.
    • Hue describes a pure color.
    • Hit-or-miss transformations are used for shape detection.
    • Gradient vectors are perpendicular to the contour lines, pointing towards the direction of higher intensity.
    • Gaussian filters are used in edge detection to reduce noise and blur the image.
    • Affine transformations include translation, 2D in-plane rotation, uniform scaling, and shearing.
    • Derivative operations can be applied for edge localization.
    • Baseline distance in binocular vision only affects the focal length.
    • The essential matrix plays a role in stereo vision: it represents the 3D transformation between stereo cameras.
    • The Lucas-Kanade method addresses challenges in motion analysis, specifically large displacements and illumination changes.
    • Radial distortion in camera calibration introduces errors in the mapping between 3D and 2D coordinates.
    • Image registration aligns images from different sources or time points.
    • The Laplacian of Gaussian (LoG) edge detector addresses challenges such as noise sensitivity and smoothing artifacts.
    • Camera calibration for a single camera yields the intrinsic matrix, extrinsic matrix, radial or tangential distortions.

    Feature Extraction

    • Harris Corner Detector, SIFT (Scale-Invariant Feature Transform), and the Hough Transform are techniques used to detect interest points or keypoints in an image.
    • Harris Corner Detector characteristics include rotation invariance and corner response, but it's not scale invariant.
    • In binocular vision, depth is inversely proportional to disparity and disparity is proportional to baseline; larger baselines improve depth map resolution.

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    Description

    Test your knowledge on the fundamental concepts of image processing and computer vision. This quiz covers various techniques including filtering, interpolation, transformations, and edge detection. Perfect for students and enthusiasts looking to validate their understanding of this dynamic field.

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