MCQ for DIP

Mewar University, Gangrar, Chaittorgarh Electronics and Communication Engineering Department Multiple Choice Question Bank Subject: DIP (7th Semester CS) Unit-1 1. DIP Stands: a) Digital image processing b) Digital information processing c) Digital induction process d) None of these 2. What is image? a) Picture b) Matrix of pixel c) Collection of pixel d) All of these 3. What is digital image? a) When x, y, and the amplitude values of f are all finite, discrete quantities, we call the imge a digital image. b) When x, y, and the amplitude values of f are all infinite, discrete quantities, we call the imge a digital image. c) a & b d) None of these 4. The field of digital image processing refers to processing digital images by means: a) Digital computer b) Super computer c) mini-computer d) None of these 5. What is pixel? a) Pixel is the elements of a digital image b) Pixel is the elements of a analog image

c) a & b d) none of these 6. First application of digital image was in the: a) News paper industry b) communication system c) a & b d) None of these 7. The process of extracting information from the image is called as a) Image enhancement b) Image restoration c) Image Analysis d) Image compression 8. Among the following image processing techniques which is fast, precise and flexible a) optical b) digital c) electronic d) photographic 9. An image is considered to be a function of a(x,y) where a represents a) height of image b) width of image c) amplitude of image d) resolution of image 10. Which is the image processing technique used to improve the quality of image for human viewing? a) compression b) enhancement c) restoration d) analysis 11. Which type of enhancement operations are used to modify pixel values according to the value of the pixel‗s neighbors? a) point operations b) local operations c) global operations d) mask operations

12. In which type of progressive coding technique,gery color is encoded first and then other colors are encoded? a) quality progressive b) resolution progressive c) component progressive d) region progressive 13. Which image processing technique is used to eliminate electronic noise by mathematical process? a) Frame averaging b) Image understanding c) Image compression d) none 14. The amount of noise decreases by ————— of number of frames averaged a) division b) square root c) linear d) none 15. Dilation-Morphological image operation technique is used to a) shrink brighter areas of the image b) diminishes intensity variation over the image c) expands brighter areas of the image d) scales pixel intensity uniformly 16. Image compression is a) making image look better b) sharpening the intensity-transition regions c) minimizing degradation over image d) reducing the redundancy of the image data 17. Which is a fundamental task in image processing used to match two or more pictures? a) registration b) segmentation c) computer vision d) image differencing 18. Which technique is used for the images of the same scene are acquired from different

viewpoints a) multiview analysis b) multitemporal analysis c) multisensory analysis d) image differencing 19. Which sensor is used for obtaining the video source in 3d face recognition system a) optical b) electronic c) 3d sensor d) 2d sensor 20. What algorithm is used in fingerprint technology a) Intensity based algorithm b) pattern based algorithm c) feature based algorithm d) Recognition algorithm 21. Which technique turns the unique lines, patterns, and spots apparent in a person‘s skin into a mathematical space a) registration b) segmentation c) skin texture analysis d) image differencing 22. In which technique which is used to determine changes between two images ? a) Image differencing b) segmentation c) skin texture analysis d) image differencing 23. Select one of the most appropriate application of Computer vision? a) medical computer imaging b) remote sensing c) geographical map d) medical diagnosis 24. What does SDR stands for? a) standard dynamic range b) Software defined radio

c) Session directory d) System design view 25. Which software has a built in face recoginition system ? a) Google‘s picasa digital image orgranizer b) Apple iphoto‘s organizer c) PMB d) Sony‘s photo organizer 26. Which device is used to capture the fingerprint pattern? a) Capture device b) Fingerprint sensor c) 2d sensor d) digital sensor 27. An image is converted to two-dimensional matrix of pixel values by a) pixel grabber b) bough transform c) masking d) none 28. _____ is the most reliable and accurate biometric identification technique. a) Computer vision b) Iris recognition c) Medical imaging d) Remote sensing 29. A biometrics may be a) fingerprint images b) satellite images c) computer vision d) none 30. The initial step in any image processing technique is a) segmentation b) masking c) image acquisition d) normalization 31. The identification technique using voice, keystroke, gait etc. are included in

a) image enhancement b) behavioral biometrics c) face recognition d) physical biometrics 32. Which of the following steps are included in iris segmentation? a) image acquisition b) iris normalization c) iris localization d) code generation 33. Localization of iris, pupil, eyelids come under a) normalization b) masking c) extraction d) segmentation 34. Verification using genetic algorithm and back propagation is done in a) iris recognition b) face detection c) fingerprint identification d) none 35. The dominant application of imaging in the microwave band is: a) Radar b) satellite c) communication d) None 36. What‘s recognition? a) It‘s the process that assigns a label to an object based on its descriptors. b) it‘s process of search a image c) a & b d) None 37. Morphological processing deals: a. with tools for extracting image components that are useful in the representation and description of shape. b. with tools for changes in image components that are useful in the representation and description of shape.

c) a & b d) None 38. What is digitizer? a. it‘s a device for converting the output of the physical sensing device into digital form. b. it‘s a device for converting the output of the physical sensing device into analog form. c) a & b d) None 39. What is brightness adaption? a. For a given set of conditions, the current sensitivity level of the visual system. b. For a given set of conditions, the current sensitivity level of the in-visual system. c) a & b d) None 40. What is spatial resolution? a. it‘s the largest discernible detail in an image. b. it‘s the smallest discernible detail in an image. c) a & b d) None 41. The spatial domain refers: a. to the image plane itself, and approaches in this category are based on indirect manipulation of pixels in an image. b. to the image plane itself, and approaches in this category are based on direct manipulation of pixels in an image. c) a & b d) None 42. Frequency domain refers: a. its processing techniques are based on modifying the Fourier transform of an image. b. its processing techniques are based on modifying the laplace transform of an image. c) a & b d) None 43. Image negatives a gray level transformation is defined as: a. s=L-1-r b. s=L-r c. s=r-1-L d. none

44. the principal disadvantage of piecewise function is: a. that their specification requires considerably more user input. b. that their specification requires considerably more user output. c) a & b d) None 45. Smoothing spatial filters are used for: a. blurring b. noise reduction c) a & b d) None

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Unit-2 1. DFT stands as: a. Discrete Fourier transform b. digital function transform c. digital frequency transform d. none 2. Basic steps for filtering in the frequency domain: a. Fourier transform b. filter function c. Inverse Fourier transform d. all of these 3. A generalization of unsharp masking is: a. high boost filtering b. low boost filtering c. a & b d. none 4. Restoration is: a. attempts to reconstruct or recover an image that has been degraded by using a priori knowledge of the degradation phenomenon. b. attempts to reconstruct or recover an image that has been graded by using a priori knowledge of the gradation phenomenon. c. a & b d. None of above

5. Restoration technique: a. its oriented toward modeling the degradation and applying the inverse process in order to recover the original image. b. its oriented toward modeling the gradation and applying the inverse process in order to recover the original image. c. its oriented toward modeling the degradation and applying the process in order to recover the original image. d. none of above 6. An image transmitted using wireless network: a. corrupted as a result of lighting or other atmospheric disturbance. b. non-corrupted as a result of lighting or other atmospheric disturbance.

c. corrupted as a result of pixel disturbance. d. none of above 7. Fourier spectrum of noise is constant, the noise usually: a. white noise b. red noise c. green noise d. none of above 8. Periodic noise in an image arises due to: a. electrical or electromechanical interference during image compression b. electrical or electromechanical interference during image restoration c. electrical or electromechanical interference during image acquisition d. none of above 9. Bipolar impulse noise is also called a. salt and pepper noise b. white noise c. Gaussian noise d. none of above 10. spatial filtering is the method of choice in situation when only: a. additive noise is present. b. additive noise is absent. c. additive noise is doublet. d. none of above 11. A ……….achieves smoothing comparable to the arithmetic mean filter, but it tends to lose less image detail in the process. a. Arithmetic mean filter b. geometric mean filter c. spatial filter d. none of above 12. A geometric mean filter achieves smoothing comparable to the arithmetic mean filter, but it tends to …………….image detail in the process. a. lossy b. corrupted c. lose less

d. none of above 13. The harmonic mean filter works well for………..but fails for pepper noise. a. salt and pepper noise b. salt noise c. pepper noise d. none of above 14. The harmonic mean filter works well for salt noise, but fails for ................... a. salt and pepper noise b. salt noise c. pepper noise d. none of above 15. The harmonic mean filter works well for............. a. salt and pepper noise b. Gaussian noise c. pepper noise d. none of above 16. Contra harmonic mean filter is well suited for reducing or virtually eliminating the effects of ................................ a. salt and pepper noise b. Gaussian noise c. pepper noise d. none of above 17. For ...................value of Q, the Contra harmonic mean filter eliminates pepper noise. a. positive b. negative c. equal d. none of above 18. for negative value of Q, the Contra harmonic mean filter eliminates salt noise. a. positive b. negative c. equal d. none of above 19. The Contra harmonic mean filter reduces to the arithmetic mean filter if......, and to the harmonic mean filter if Q=-1.

a. Q=0 b. Q=1 c. Q=-1 d. none of above 20. The arithmetic and geometric mean filters are well suited for random noise like Gaussian or uniform noise. a. random noise b. uniform noise c. Gaussian noise d. all of above 21. The ..................are well suited for random noise like Gaussian or uniform noise. a. arithmetic and geometric mean filters a. arithmetic mean filters a geometric mean filters d. all of these 22. The Contra harmonic mean filter is well suited for impulse noise, but it has the disadvantage that it must be known whether the noise is dark or light in order to select the proper sign for Q. a. random noise b. uniform noise c. impulse noise d. all of above 23. The best known order statistics filter is the median filter, which replaces the value of a pixel by the median of the gray levels in the neighborhood of that pixel. a. fˆ ( x, y) median{g(s, t )} ( s , t ) Sxy

b. fˆ ( x, y) c. fˆ ( x, y)

max {g(s, t )}

( s , t ) Sxy

min {g(s, t )}

( s , t ) Sxy

d. none of above

24. Using 100th percentile results in the so-called max filter, a. fˆ ( x, y) b. fˆ ( x, y) c. fˆ ( x, y)

median{g(s, t )} ( s , t ) Sxy

max {g(s, t )}

( s , t ) Sxy

min {g(s, t )}

( s , t ) Sxy

d. none of above

25. Using 0th percentile results in the so-called min filter, a. fˆ ( x, y)

median{g(s, t )} ( s , t ) Sxy

b. fˆ ( x, y) c. fˆ ( x, y)

max {g(s, t )}

( s , t ) Sxy

min {g(s, t )}

( s , t ) Sxy

d. none of above

26. Advantage of ................is finding the brightest points in an image. a. max filter b. min filter c. median filter d. none of above 27. Advantage of .............. is finding the darkest points in an image. a. max filter b. min filter c. median filter d. none of above 28. Which type of noise reduced by Max filter: a. salt and pepper noise b. salt noise c. pepper noise d. none of above 29. Which type of noise reduced by min filter: a. salt and pepper noise b. salt noise c. pepper noise d. none of above 30. Midpoint filter works best for..............

a. salt and pepper noise b. salt noise c. random noise d. none of above 31.

a. basics steps for filtering in the frequency domain b. basics steps for filtering in the spatial domain c. basics steps for filtering in the time domain d. none of above

32.

a. 2-D DFT

b. 1-D DFT c. 2-D FFT d. none of above 33. Below figures shows:

. Fig. (a)

Fig. (b)

a. Discrete function of M points, it‘s Fourier spectrum b. Discrete function of 2M points, it‘s Fourier spectrum c. Discrete function of 3M points, it‘s Fourier spectrum d. none of above 34. Below figures shows:

Fig. (a)

Fig. (b)

a. Discrete function with twice the number of nonzero, it‘s Fourier spectrum

b. Discrete function with the number of nonzero, it‘s Fourier spectrum c. Discrete function with fourth the number of nonzero, it‘s Fourier spectrum d. none of above 35. SEM image of a damaged integrated circuit

Fig. (a)

Fig. (b)

a. SEM image of a damaged integrated circuit, result of band-pass filtering b. SEM image of a damaged integrated circuit, result of high-pass filtering c. SEM image of a damaged integrated circuit, result of low-pass filtering d. none of above 36. SEM image of a damaged integrated circuit

Fig. (a)

Fig. (b)

a. SEM image of a damaged integrated circuit, result of band-pass filtering b. SEM image of a damaged integrated circuit, result of high-pass filtering

c. SEM image of a damaged integrated circuit, result of low-pass filtering d. none of above 37.

a. low pass filtering in spatial domain b. high pass filtering in spatial domain c. band pass filtering in spatial domain d. none of above 38.

a. high pass filtering by DFT windows b. low pass filtering by DFT windows c. band pass filtering by DFT windows d. none of above 39. ..............can be thought of as one low-pass filtered image minus another low pass filtered image. a. The low pass filtered image b. The high pass filtered image c. The band pass filtered image d. none of above 40. below figure shows

a. homo-morphic filtering approach for image enhancement b. homo-morphic filtering approach for image compression c. homo-morphic filtering approach for image restoration d. none of above 41. below figure shows

a. computation of 2D Fourier transform as a series of 1D Fourier transform b. computation of 2D fast Fourier transform as a series of 1D Fourier transform c. computation of 2D DIT-FFT as a series of 1D Fourier transform d. none of above 42. below figure shows:

a. homo-morphic filtering approach for image enhancement b. homo-morphic filtering approach for image restoration c. homo-morphic filtering approach for image compression d. none of above 43. below figure shows:

a. model of image degradation/restoration process b. model of image enhancement process c. model of image compression process d. none of above 44. Given: observation y(m,n) and blurring function h(m,n); Design: g(m,n), such that the distortion between x(m,n) and

is minimized

a. Non-blind deblurring/deconvolution b. Blind deblurring/deconvolution c. Non-blind blurring/convolution d. none of above 45. Given: observation y(m,n); Design: g(m,n), such that the distortion between x(m,n) and is minimized a. Non-blind deblurring/deconvolution b. Blind deblurring/deconvolution

c. Non-blind blurring/convolution d. none of above Answers-Key Unit-2: 1

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Unit-3 1. what is color? a. it‘s an attribute of objects (feature) b. it‘s an attribute of objects (shape) c. it‘s an attribute of objects (smoothness) d. all of above 2. color depends on: a. spectral characteristics of the light sources illuminating the objects b. spectral characteristics of objects c. spectral characteristics of the sensors of the imaging device d. all of above 3. Tri-chromatic color mixing theory is a. any color can be obtained by mixing of three secondary colors with a right proportion. b. any color can be obtained by mixing of three primary colors with a right proportion. c. a & b d. none of above 4. Primary colors for illuminating sources: a. Red, Green, Blue b. cyan, magenta, Yellow c. a & b d. none of above 5. Additive rule a. cyan+ magenta + Yellow = white

b. Red + Green + Blue = white c. a & b d. none of above 6. Color monitor works by exciting a. red, green, blue phosphors using separate electronics guns b. cyan, green, blue phosphors using separate electronics guns c. red, magenta, blue phosphors using separate electronics guns d. none of above 7. Primary colors for reflecting sources: a. Red, Green, Blue b. cyan, magenta, Yellow c. a & b d. none of above 8. Subtractive rule a. Red + Green + Blue = Black b. cyan+ magenta + Yellow = black c. Red + Green + Blue = white d. none of above 9. Color printer works by using a. cyan, magenta, Yellow and black dynes a. red, magenta, Yellow and black dynes a. cyan, blue, Yellow and black dynes d. none of above

10. Primary colors for reflecting sources a. secondary colors b. red, green, blue c. a & b d. none of above 11. Primary colors of pigments a. cyan, magenta, Yellow b. red, green, blue c. a & b d. none of above 12. If a surface coated with ........is illuminated with white light, no red light is reflected form the surface. a. cyan b. yellow c. magenta d. none of above 13. ....... subtracts red light from white light which contains amounts of red, green and blue light. a. cyan b. yellow c. magenta d. none of above 14. CMYK stands as a. CMY + K where K stands for brown a. CMY + K where K stands for blue

a. CMY + K where K stands for black d. none of above 15. Four colors printing as a. CMYK b. RGBK c. RGYK d. none of above 16.

a. RGB to CMY conversion b. CMY to RGB conversion c. RGB to RGB conversion d. none of above 17. HSI color Model is a. Height, Saturation, Intensity b. Hue, symmetric, Intensity c. Hue, Saturation, Intensity d. none of above 18. Hue represents

a. Dominant color as perceived by an observer. It‘s an attribute with the dominant wavelength b. non-Dominant color as perceived by an observer. It‘s an attribute with the dominant wavelength c. a & b d. none of above 19. Saturation refers in HS color model as a. To the relative purity or the amount of white light mixed with a hue. The pure spectrum colors are semi-fully saturated. b. To the relative purity or the amount of white light mixed with a hue. The pure spectrum colors are partially saturated. c. To the relative purity or the amount of white light mixed with a hue. The pure spectrum colors are fully saturated. d. none of above 20. Pink and Lavender are....... a. less saturated b. more saturated c. better saturated d. none of above 21. Intensity reflects in HIS color model as a. The brightness b. The contrast c. a & b d. none of above 22. Total amount of energy that flow from the light source, measured in watts (W) a. Radiance

b. Luminance c. a & b d. none of above 23. Amount of energy an observer perceives from a light source a. Radiance b. Luminance c. a & b d. none of above 24. Luminance measured in........ a. lumens b. km c. mm d. none of above 25. Far infrared light: a. high radiance, but 0 luminance b. low radiance, but 0 luminance c. high radiance, but 1 luminance d. none of above 26. Subjective descriptor that is hard to measure, similar to the achromatic notion of intensity a. Radiance b. Brightness c. a & b d. none of above

27. Principal sensing categories in eyes a. Red light 65%, green light 33%, and blue light 2% b. Yellow light 65%, green light 33%, and blue light 2% c. Red light 65%, green light 33%, and cyan light 2% d. none of above 28. Secondary colors: a. G+Y=Cyan, R+G=Yellow, R+B=Magenta b. G+B=Cyan, R+G=Yellow, R+B=Magenta c. G+B=Cyan, R+G=Yellow, R+B=Magenta d. none of above 29. below figure shows:

a. additive primaries b. subtractive primaries c. a & b d. none of above

30. below figure shows

a. additive primaries b. subtractive primaries c. a & b d. none of above 31. below figure shows

a. Color TV b. Picture c. image d. none of above

32. Suitable for hardware or applications a. RGB model b. CYM model c. CYMK model d. all of above 33. HSI model a. match the human description b. match the animal description c. match the cat description d. none of above 34. The number of bits used to represent each pixel in RGB space. a. Pixel depth b. no. of pixel c. pixel size d. none of above 35. Used to generate hardcopy output a. CMY color model b. RGB color model c. HIS color model d. none of above 36. Morphological Image Processing a. used to compact image components that are useful in the representation and description of region shape.

b. used to extract image components that are useful in the representation and description of region shape. c. used to extract image components that are useful in the compression of region shape. d. none of above 37. The element of the set is the coordinates (x,y) of pixel belong to the object  Z2 a. Binary image (0 = white, 1 = black) b. gray-scaled image c. a & b d. none of above 38. The element of the set is the coordinates (x,y) of pixel belong to the object and the gray levels  Z3 a. Binary image (0 = white, 1 = black) b. gray-scaled image c. a & b d. none of above 39. Erosion a. Erosion of a set A by structuring element B: all z in A such that B is in A when origin of B=z b.

A B {z|(B)z

A}

c. Shrink the object d. all of above 40. Dilution a. Dilation of a set A by structuring element B: all z in A such that B hits A when origin of B=z b.

A

ˆ) B {z|(B z

c. Grow the object

A

Φ}

d. all of above 41. Erosion a. Removal of structures of certain shape and size, given by SE b.

A B {z|(B)z

A}

c. Shrink the object d. all of above 42. Dilation a. Filling of holes of certain shape and size, given by SE b. A

ˆ) B {z|(B z

Φ}

A

c. Grow the object d. all of above 43. Wanted: Remove structures / fill holes and without affecting remaining parts a. Solution: Combine erosion and dilation b. Solution: dilation c. Solution: erosion d. none of above 44. Opening: a. Erosion followed by dilation, denoted ∘ b. A  B

(A

B)

B

c. Eliminates protrusions d. all of above 45. Closing: a. Dilation followed by erosion, denoted •

b. A

B

(A

B)

B

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Unit-4 1. Registration Goals: I2(x,y)=g(I1(f(x,y)) a. f() – 2D spatial transformation b. g() – 1D intensity transformation c. a & b d. none of above 2. Spatial Transformations a. Rigid b. Affine c. Projective d. all of above 3. Rigid Transformation a. Rotation(R) b. Translation (t) c. Similarity (scale) d. all of above 4. Affine Transformation a. Rotation b. Translation c. Scale d. all of above 5. Flat plane tilted with respect to the camera requires a. Projective Transformation b. affine Transformation c. rigid transformation

d. none of above 6. Methods of Registration a. Correlation b. Fourier c. Point Mapping d. all of above 7. ..........must be normalized to avoid contributions from local image intensities. a. Correlation b. Convolution c. circular convolution d. none of above 8. Given a two images T & I, 2D normalized ..............function measures the similarity for each translation in an image patch

C(u, v)

x

y x

T ( x , y ) I( x u , y v ) y

I 2 ( x u , y v)

a. Correlation b. Convolution c. circular convolution d. none of above 9. Correlation Theorem a. Fourier transform of the correlation of two images is the product of the Fourier transform of one image and the Fourier transform of the other. b. Fourier transform of the correlation of two images is the product of the Fourier transform of one image and the inverse of the Fourier transform of the other. c. Fourier transform of the correlation of two images is the product of the Fourier transform of one image and the complex conjugate of the Fourier transform of the other. d. none of above

10. Fourier Transform Based Methods a. Phase-Correlation b. Cross power spectrum c. Power cepstrum d. all of above 11. All Fourier based methods are very efficient, only only work in cases of rigid transformation. a. Projective Transformation b. affine Transformation c. rigid transformation d. none of above 12. Point Mapping Registration a. Control Points b. Point Mapping with Feedback c. Global Polynomial d. all of above 13. Point mapping with Feedback a. Clustering b. determine the optimal spatial transformation between images by an evaluation of all possible pairs of feature matches. c. a & b d. none of above 14. Characteristics of Registration Methods a. Feature Space b. Similarity Metrics c. Search Strategy

d. all of above 15. Same Modality Camera Sensors  enough correlated structure at................ a. all resolution levels b. high resolution levels c. a & b d. none of above 16. Different Modality Camera Sensors  primary correlation only in.................. a. all resolution levels b. high resolution levels c. a & b d. none of above 17. Goal: Suppress non-common information & capture the common scene details a. Solution: low pass energy images b. Solution: High pass energy images c. Solution: band pass energy images d. none of above 18. Apply the Laplacian high pass filter to the original images a. Square the results  NO contrast reversal b. Square the results  contrast reversal c. Square the results  high contrast reversal d. none of above 19. Alignment Algorithm a. Do not assume global correlation, use only local correlation information b. Use Normalized Correlation as a similarity measure c. Thus, no assumptions about the original data

d. all of above 20. ............is to subdivide an image into its component regions or objects. a. Segmentation b. fragment c. addition d. none of above 21. ........should stop when the objects of interest in an application have been isolated. a. Segmentation b. fragment c. addition d. none of above 22. Segmentation algorithms generally are based on one of 2 basis properties of intensity values a. discontinuity : to partition an image based on sharp changes in intensity (such as edges) b. Similarity: to partition an image into regions that are similar according to a set of predefined criteria. c. a & b d. none of above 23. Detect the three basic types of gray-level discontinuities a. points b. lines c. edges d. all of above 24. Point Detection a. a point has been detected at the location on which the mark is centered if |R| b. T is a nonnegative threshold

T

c. R is the sum of products of the coefficients with the gray levels contained in the region encompassed by the mark. d. all of above 25. This is a:

a. Point Detection mask b. Line detection mask (Horizontal) c. Line detection mask (vertical) d. none of above 26. This is a:

a. Point Detection mask b. Line detection mask (Horizontal) c. Line detection mask (vertical) d. none of above 27. This is a:

a. Point Detection mask b. Line detection mask (+450) c. Line detection mask (vertical) d. none of above 28. This is a:

a. Point Detection mask b. Line detection mask (+450) c. Line detection mask (vertical) d. none of above 29. This is a:

a. Point Detection mask

b. Line detection mask (-450) c. Line detection mask (vertical) d. none of above 30. ............will result with max response when a line passed through the middle row of the mask with a constant background. a. Horizon mask b. Horizontal mask c. vertical mask d. none of above 31. if we are interested in detecting all lines in an image in the direction defined by a given mask, we simply run the mask through the image and threshold the absolute value of the result. a. Line Detection b. edge detection c. point detection d. none of above 32. Blurred edges tend to be ....... and sharp edges tend to be......: a. thick, thin b. thick, thick c. thin, thin d. none of above 33. An imaginary straight line joining the extreme positive and negative values of the second derivative would cross zero near the midpoint of the edge. a. two-crossing property b. one-crossing property c. Zero-crossing property d. none of above

34. ..............should be serious consideration prior to the use of derivatives in applications where noise is likely to be present. a. Image smoothing b. image compression c. image enhancement d. none of above 35. This is a:

a. Prewitt edge detection gradient mask b. Sobel edge detection gradient mask c. Roberts edge detection gradient mask d. none of above 36. This is a:

a. Prewitt edge detection gradient mask b. Sobel edge detection gradient mask c. Roberts edge detection gradient mask d. none of above 37. This is a:

a. Prewitt edge detection gradient mask b. Sobel edge detection gradient mask c. Roberts edge detection gradient mask d. none of above 38. Laplacian combined with smoothing to find edges via............. a. two-crossing b. one-crossing c. zero-crossing d. none of above 39. The threshold used for each pixel depends on the location of the pixel in terms of the subimages, this type of thresholding is........... a. adaptive b. static c. modern d. none of above 40. ................contributes significantly to algorithms for feature detection, segmentation, and motion analysis. a. point detection b. line detection c. Edge detection

d. none of above 41. An ........a place where there is a rapid change in the brightness (or other property) of an image. a. edge b. point c. line d. none of above 42. region-based segmentation (seeded region growing) a. advantage: With edge connectivity a. advantage: With loose connectivity a. advantage: With good connectivity d. none of above 43. region-based segmentation (seeded region growing) a. Disadvantage: Initial seed-points: different sets of initial seed-point cause different segmented result b. Disadvantage: Time-consuming problem c. a & b d. none of above 44. region-based segmentation (Unseeded region growing) a. Advantage: easy to use b. Advantage: can readily incorporate high level knowledge of the image composition through region threshold c. a & b d. none of above 45. region-based segmentation (Unseeded region growing) a. Disadvantage: slow speed b. Disadvantage: high speed c. Disadvantage: slow capture d. none of above

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Unit-5 1. Objective of Image Representation and Description? a. To represent and describe information embedded in an image in other forms that are less suitable than the image itself. b. a. To represent and describe information embedded in an image in other forms that are more suitable than the image itself. c. a & b d. none of above 2. Benefits of Image Representation and Description: a. Easier to understand b. Require less memory, faster to be processed c. More ―ready to be used‖ d. all of above 3. What kind of information we can use for Image Representation and Description a. Boundary, shape b. Region c. Texture

d. all of above 4. Why we focus on a boundary? a. The boundary is a good representation of an object shape and also requires a few memory. b. The boundary is a poor representation of an object shape and also requires a few memory. c. The boundary is a good representation of an object shape and also requires a high memory. d. none of above 5. .......represent an object boundary by a connected sequence of straight line segments of specified length and direction.

a. hex codes b. Chain codes c. binary codes d. none of above 6. Problem of a chain code: a chain code sequence depends on a starting point. a. Solution: treat a chain code as a rectangular sequence and redefine the starting point so that the resulting sequence of numbers forms an integer of minimum magnitude. b. Solution: treat a chain code as a circular sequence and redefine the starting point so that the resulting sequence of numbers forms an integer of minimum magnitude. c. Solution: treat a chain code as a circular sequence and redefine the ending point so that the resulting sequence of numbers forms an integer of maximum magnitude. d. none of above 7. .................Counting the number of direction change (in counterclockwise) between 2 adjacent elements of the code. a. The second difference of a chain code b. The third difference of a chain code c. The first difference of a chain code d. none of above 8. A chain code: 10103322 – a. The first difference = 3133030 a. The first difference = 3130030 a. The first difference = 3100030 d. none of above 9. The first difference is............. a. circular invariant b. rotational variant c. rotational invariant

d. none of above 10. ....................Partitioning an object boundary by using vertices of a convex hull. a. Boundary Segments Concept a. Boundary distribute Concept a. edge Segments Concept d. none of above 11. Thinning Algorithm Concept: a. Do not remove end points b. Do not break connectivity c. Do not cause excessive erosion. d. all of above 12. Thinning Algorithm Apply only to contour pixels: a. pixels “1” having at least one of its 8 neighbor pixels valued “0” b. pixels “2” having at least one of its 8 neighbor pixels valued “0” c. pixels “1” having at least one of its 8 neighbor pixels valued “1” d. none of above Shape number of the boundary: a. the first difference of smallest magnitude b. the second difference of smallest magnitude c. the first difference of largest magnitude d. none of above 14. The order n of the shape number: a. the number of nibbles in the sequence a. the number of bits in the sequence a. the number of digits in the sequence

d. none of above 15. Chain code: 0 3 2 1 for below structure:

a. difference: 3 3 3 3 b. shape no.: 3 3 3 3 c. a & b d. none of above

16. Chain code: 0 0 3 2 2 1 for below structure:

a. difference: 3 0 3 3 0 3 b. shape no.: 0 3 3 0 3 3 c. a & b d. none of above 17. ..................view a coordinate (x,y) as a complex number (x = real part and y = imaginary part) then apply the Fourier transform to a sequence of boundary points. a. Fourier descriptor b. Laplace descriptor c. Regional descriptor

d. none of above 18. ..................Purpose: to describe regions or “areas” a. Fourier descriptor b. Laplace descriptor c. Regional Descriptors d. none of above 19. a circle is the most compact shape with a. C = 1/pi b. C = 1/4pi c. C = 1/8pi d. none of above 20. ..............Use to describe holes and connected components of the region a. Topological Descriptors b. Laplace descriptor c. Regional Descriptors d. none of above 21.

E C H C = the number of connected components; H = the number of holes

a. Euler number b. Euler formula c. Euler value d. none of above 22. V Q F C H of faces a. Euler number b. Euler formula c. Euler value

E

V = the number of vertices; Q = the number of edges; F = the number

d. none of above 23. below figure shows:

a. Euler number (E): E=0 b. Euler number (E): E=1 c. Euler number (E): E=-1 d. none of above 24. below figure shows:

a. Euler number (E): E=0 b. Euler number (E): E=1 c. Euler number (E): E=-1 d. none of above

25. below figure shows:

a. Euler number (E): E=0 b. Euler number (E): E=1 c. Euler number (E): E=-2 d. none of above26. Texture Descriptors Purpose: to describe “texture” of the region. 27. The 2nd moment = variance measure a. smoothness b. skewness c. flatness d. none of above 28. The 3rd moment  measure a. smoothness b. skewness c. flatness d. none of above 29. The 4th moment  measure a. smoothness

b. skewness c. flatness d. none of above 30. Fourier Approach for ..............Concept: convert 2D spectrum into 1D graphs a. Texture Descriptor b. regional Descriptor c. topological Descriptor d. none of above 31. Principal Components for ............Purpose: to reduce dimensionality of a vector image while maintaining information as much as possible. a. Description b. registration c. observation d. none of above 32. region-based segmentation fast scanning Advantage: a. The speed is very fast b. The result of segmentation will be intact with good connectivity c. a & b d. none of above 33. region-based segmentation fast scanning Disadvantage: a. The matching of physical object is not good: It can be improved by morphology and geometric mathematic b. The result of segmentation will be intact with good connectivity c. a & b d. none of above 34. a ............... is a family of patterns that share some common properties.

a. Pattern classes b. Pattern recognition c. Pattern checker d. none of above 35. .................to assign patterns to their respective classes. a. Pattern classes b. Pattern recognition c. Pattern checker d. none of above 36. Three common pattern arrangements used in practices are a. Vectors b. Strings c. Trees d. all of above 37. ..........................is more powerful than string ones. a. Tree descriptions b. regional descriptions c. space descriptions d. none of above 38. ...............to recognition are based on the use decision functions. a. semi-theoretic approaches b. Final-theoretic approaches c. Decision-theoretic approaches d. none of above 39. The distance between two shapes a and b defined as: (Degree of similarity k)

a. D(a , b)

1 k

b. D(a , b)

1 2k

c. D(a, b)

2 k

d. none of above 40. A simple measure of similarity between a and b is the ratio a.

R

max( a , b )

b. Represent the number of matches between the two strings, where a match occurs in the kth position if ak = bk. c. The number of symbols that do not match is d. all of above

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