Chapter Questions
What is the main aim of image pre-processing?
Give examples of situations in which brightness transformations, geometric transformations, smoothing, edge detection, and/or image restorations are typically applied.
What is the main difference between brightness correction and gray-scale transformation?
Explain the rationale of histogram equalization.
Explain why the histogram of a discrete image is not flat after histogram equalization.
Consider the image given in Figure 5.3a. After histogram equalization (Figure 5.3b), much more detail is visible. Does histogram equalization increase the amount of information contained in image data? Explain.
What are the two main steps of geometric transforms?
What is the minimum number of corresponding pixel pairs that must be determined if the following transforms are used to perform a geometric correction?(a) Bilinear transform(b) Affine transform
Give a geometric transformation equation for(a) Rotation(b) Change of scale(c) Skewing by an angle
Consider brightness interpolation-explain why it is better to perform brightness interpolation using brightness values of neighboring points in the input image than interpolating in the output image.
Explain the principles of nearest-neighbor interpolation, linear interpolation, and bicubic interpolation.
Explain why smoothing and edge detection have conflicting aims.
Explain why Gaussian filtering is often the preferred averaging method.
Explain why smoothing typically blurs image edges.
Name several smoothing methods that try to avoid image blurring. Explain their main principles.
Explain why median filtering performs well in images corrupted by impulse noise.
Give convolution masks for the following edge detectors:(a) Roberts(b) Laplace(c) Prewitt(d) Sobel(e) KirschWhich ones can serve as compass operators? List several applications in which determining edge direction is important.
Explain why subtraction of a second derivative of the image function from the original image results in the visual effect of image sharpening.
What are LoG and DoG? How do you compute them? How are they used?
Propose a robust way of detecting significant image edges using zero-crossings.
Explain why LoG is a better edge detector than Laplace edge detector.
Explain the notion of scale in image processing.
Explain the importance of hysteresis thresholding and non-maximal suppression in the Canny edge detection process. How do these two concepts influence the resulting edge image?
Explain the principles of noise suppression, histogram modification, and contrast enhancement performed in adaptive neighborhoods.
What is the aperture problem? How does it affect finding correspondence for line features and corner features? Add a simple sketch to your answer demonstrating the concept of aperture and the consequences for correspondence of lines and corners.
Explain the principles of image restoration based on(a) Inverse convolution(b) Inverse filtration(c) Whener filtrationList the main differences among the above methods.
Give image distortion functions for(a) Relative camera motion(b) Out-of-focus lens(c) Atmospheric turbulence