Site icon T4Tutorials.com

Texture Analysis (GLCM, LBP, Gabor Filters) — MCQs | Digital Image Processing

1. Who introduced the concept of the Gray Level Co-occurrence Matrix (GLCM) for texture analysis?

(A) Haralick


(B) Otsu


(C) Marr


(D) Gonzalez



2. What does GLCM primarily measure in an image?

(A) Frequency of intensity values


(B) Spatial relationships between pixels


(C) Color histogram


(D) Edges in different directions



3. Which parameter of GLCM indicates how uniform the texture is?

(A) Contrast


(B) Entropy


(C) Energy


(D) Correlation



4. In texture analysis, what does the LBP (Local Binary Pattern) operator compare?

(A) Mean of the image


(B) Gradient magnitude


(C) Center pixel with its neighbors


(D) Color intensity variation



5. Which of the following is not a typical GLCM feature?

(A) Homogeneity


(B) Sharpness


(C) Energy


(D) Entropy



6. Which operator transforms local texture into a binary pattern code?

(A) GLCM


(B) Sobel


(C) LBP


(D) Laplacian



7. Gabor filters are mainly used in texture analysis to capture:

(A) Noise


(B) Motion blur


(C) Spatial frequency and orientation


(D) Histogram equalization



8. What is the size of the standard neighborhood in basic LBP?

(A) 3×3


(B) 5×5


(C) 7×7


(D) 9×9



9. Which of the following features measures randomness in GLCM?

(A) Energy


(B) Correlation


(C) Entropy


(D) Contrast



10. In LBP, a uniform pattern is one that has how many transitions from 0 to 1 or 1 to 0?

(A) 0–1 transitions


(B) At most 2 transitions


(C) Always 8 transitions


(D) Unlimited transitions



11. Which method is rotation invariant in LBP variants?

(A) Basic LBP


(B) Uniform LBP


(C) Rotation-invariant LBP


(D) Extended LBP



12. What does the contrast feature in GLCM represent?

(A) Average pixel intensity


(B) Local binary transitions


(C) Amount of local variations


(D) Pixel frequency



13. Which frequency domain filter is commonly used in texture segmentation?

(A) Butterworth filter


(B) Gabor filter


(C) Median filter


(D) Homomorphic filter



14. GLCM is constructed by counting pixel pairs with specific:

(A) Colors and sizes


(B) Intensities and distances


(C) Shapes and positions


(D) Contrast levels



15. Which of the following is true for Gabor filters?

(A) Only used in color images


(B) Are Gaussian-modulated sinusoids


(C) Work only in time domain


(D) Eliminate low-frequency texture



16. LBP histograms are useful for:

(A) Color segmentation


(B) Frequency enhancement


(C) Texture classification


(D) Edge detection



17. How many binary codes can be formed in basic LBP 3×3 window?

(A) 16


(B) 64


(C) 128


(D) 256



18. Which direction is not typically used in constructing a GLCM?

(A) 0°


(B) 90°


(C) 135°


(D) 270°



19. Which LBP variant uses circular neighborhoods?

(A) Uniform LBP


(B) Extended LBP


(C) Local variance LBP


(D) Center-symmetric LBP



20. In GLCM, what does a high correlation value suggest?

(A) Random texture


(B) High contrast


(C) Linear dependency between pixel pairs


(D) Low entropy



21. The Gabor filter bank is a set of filters with varying:

(A) Brightness and hue


(B) Shape and intensity


(C) Orientation and frequency


(D) Scale and threshold



22. Which parameter in Gabor filters controls orientation?

(A) Sigma


(B) Theta


(C) Gamma


(D) Lambda



23. LBP texture features are robust to:

(A) Lighting variations


(B) Noise


(C) Scale changes


(D) Rotation



24. What does the entropy feature of GLCM quantify?

(A) Uniformity


(B) Linearity


(C) Complexity


(D) Brightness



25. What is the typical output of Gabor filtering?

(A) Smoothened image


(B) Binary mask


(C) Complex image representing magnitude and phase


(D) Distance transform



26. In LBP, the thresholding step compares neighboring pixels to:

(A) Global histogram


(B) Median value


(C) Center pixel value


(D) Average local value



27. What kind of texture does GLCM handle well?

(A) Random noise


(B) Periodic texture


(C) Directional patterns


(D) Smooth gradients



28. Which of the following is a drawback of basic LBP?

(A) Too complex


(B) Not invariant to rotation


(C) Cannot work on grayscale images


(D) Requires color features



29. Which filter is preferred for multi-resolution texture analysis?

(A) GLCM


(B) Gabor


(C) Laplacian


(D) Prewitt



30. Which GLCM feature gives average difference in gray levels?

(A) Homogeneity


(B) Contrast


(C) Dissimilarity


(D) Correlation



31. In LBP, what does a value of 0 represent in a neighbor?

(A) Equal or higher than center


(B) Noise


(C) Lower than center


(D) Edge pixel



32. Which method is best for capturing small-scale texture patterns?

(A) GLCM


(B) LBP


(C) Gabor filter


(D) Histogram equalization



33. In texture classification, GLCM features are typically used as:

(A) Filter kernels


(B) Color indicators


(C) Feature vectors


(D) Threshold masks



34. LBP is highly sensitive to:

(A) Histogram changes


(B) Local illumination


(C) Noise


(D) Global contrast



35. Gabor filters are derived from:

(A) Wavelet theory


(B) Linear algebra


(C) Fourier transform


(D) Spatial histograms



36. The term “local” in LBP refers to:

(A) Image region


(B) Individual color


(C) Surrounding neighbors of a pixel


(D) Histogram blocks



37. Which GLCM feature is sensitive to changes in contrast?

(A) Energy


(B) Entropy


(C) Homogeneity


(D) Contrast



38. What does “rotation-invariance” mean in LBP?

(A) Same output under different lighting


(B) Same code despite image rotation


(C) Angle measurement


(D) Consistent with edge orientation



39. In Gabor filter, lambda denotes:

(A) Orientation


(B) Spatial aspect ratio


(C) Wavelength of sinusoidal factor


(D) Phase offset



40. A uniform LBP pattern contains how many bitwise transitions at most?

(A) 0


(B) 2


(C) 4


(D) 8



41. Which feature is commonly used in GLCM to measure texture smoothness?

(A) Energy


(B) Dissimilarity


(C) Entropy


(D) Correlation



42. GLCM requires how many parameters for its computation?

(A) 1


(B) 2


(C) 3 or more


(D) None



43. LBP is best used on:

(A) Binary images


(B) Edge-detected images


(C) Grayscale textures


(D) Color gradients



44. Which combination provides the most comprehensive texture analysis?

(A) LBP + Gabor


(B) Histogram + Sobel


(C) Canny + LBP


(D) Median + GLCM



45. What is the dimensionality of a typical LBP histogram for 8 neighbors?

(A) 8


(B) 16


(C) 64


(D) 256



46. Which GLCM feature decreases as texture becomes more uniform?

(A) Entropy


(B) Energy


(C) Contrast


(D) Dissimilarity



47. Gabor filters can act as:

(A) Feature detectors


(B) Histogram equalizers


(C) Color mappers


(D) Noise reducers



48. LBP can be extended to:

(A) 3D volumes


(B) Binary images only


(C) Histogram matching


(D) Fourier transforms



49. Which of the following is NOT a parameter of Gabor filter?

(A) Theta


(B) Sigma


(C) Lambda


(D) Gamma-ray



50. LBP is computationally:

(A) Intensive


(B) Expensive


(C) Simple


(D) Not feasible



51. In GLCM, increasing distance between pixel pairs affects:

(A) Orientation


(B) Feature stability


(C) Texture resolution


(D) Frequency filtering



More MCQs on Digital image Processing

  1. Introduction to DIP — MCQs | Digital Image Processing

  2. Human Visual System (HVS) — MCQs | Digital Image Processing

  3. Image Acquisition Devices — MCQs | Digital Image Processing

  4. Image Sampling & Quantization — MCQs | Digital Image Processing

  5. Image Resolution & Bit Depth — MCQs | Digital Image Processing

  6. Basic Image Operations (Negative, Log, Power-law) — MCQs | Digital Image Processing

  7. Histogram Equalization & Specification — MCQs | Digital Image Processing

  8. Contrast Stretching — MCQs | Digital Image Processing

  9. Image Arithmetic (Add, Subtract, Multiply, Divide) — MCQs | Digital Image Processing

  10. Bit-plane Slicing — MCQs | Digital Image Processing

  11. Smoothing Filters (Mean, Gaussian, Median) — MCQs | Digital Image Processing

  12. Sharpening Filters (Laplacian, Gradient) — MCQs | Digital Image Processing

  13. High-Boost Filtering — MCQs | Digital Image Processing

  14. Edge Detection (Sobel, Prewitt, Roberts, Canny, LoG) — MCQs | Digital Image Processing

  15. Fourier Transform (DFT, FFT) — MCQs | Digital Image Processing

  16. Frequency Domain Filtering — MCQs | Digital Image Processing

  17. Low-pass & High-pass Filters — MCQs | Digital Image Processing

  18. Homomorphic Filtering — MCQs | Digital Image Processing

  19. Noise Models (Gaussian, Salt & Pepper, Speckle) — MCQs | Digital Image Processing

  20. Adaptive Filtering — MCQs | Digital Image Processing

  21. Inverse & Wiener Filtering — MCQs | Digital Image Processing

  22. Pseudo-color & True-color Processing — MCQs | Digital Image Processing

  23. Color Space Conversion (RGB ↔ HSV, HSI, YCbCr) — MCQs | Digital Image Processing

  24. Color Image Enhancement — MCQs | Digital Image Processing

  25. Image Segmentation (Thresholding, Otsu, K-means, Region Growing) — MCQs | Digital Image Processing

  26. Edge-based Segmentation — MCQs | Digital Image Processing

  27. Region Splitting and Merging — MCQs | Digital Image Processing

  28. Watershed Algorithm — MCQs | Digital Image Processing

  29. Morphological Operations (Erosion, Dilation, Opening, Closing) — MCQs | Digital Image Processing

  30. Boundary Extraction — MCQs | Digital Image Processing

  31. Skeletonization — MCQs | Digital Image Processing

  32. Connected Components Labeling — MCQs | Digital Image Processing

  33. Texture Analysis (GLCM, LBP, Gabor Filters) — MCQs | Digital Image Processing

  34. Shape Descriptors (Perimeter, Area, Compactness, Eccentricity) — MCQs | Digital Image Processing

  35. Statistical Features (Mean, Variance, Skewness) — MCQs | Digital Image Processing

  36. Principal Component Analysis (PCA) — MCQs | Digital Image Processing

  37. Linear Discriminant Analysis (LDA) — MCQs | Digital Image Processing

  38. Feature Matching (SIFT, SURF, ORB) — MCQs | Digital Image Processing

  39. Image Registration — MCQs | Digital Image Processing

  40. Image Stitching — MCQs | Digital Image Processing

  41. Motion Detection & Optical Flow — MCQs | Digital Image Processing

  42. Background Subtraction — MCQs | Digital Image Processing

  43. Object Detection & Tracking — MCQs | Digital Image Processing

  44. Template Matching — MCQs | Digital Image Processing

  45. Pattern Recognition (KNN, SVM, ANN) — MCQs | Digital Image Processing

  46. Image Classification — MCQs | Digital Image Processing

  47. Image Clustering — MCQs | Digital Image Processing

  48. Image Compression (RLE, Huffman, LZW, JPEG, JPEG2000) — MCQs | Digital Image Processing

  49. Video Compression (MPEG, H.264) — MCQs | Digital Image Processing

  50. Image Fusion (Pixel, Feature, Decision Level) — MCQs | Digital Image Processing

  51. Image Watermarking — MCQs | Digital Image Processing

  52. Steganography — MCQs | Digital Image Processing

  53. Face Detection & Recognition — MCQs | Digital Image Processing

  54. Gesture Recognition — MCQs | Digital Image Processing

  55. 3D Image Processing — MCQs | Digital Image Processing

  56. Stereo Vision & Depth Estimation — MCQs | Digital Image Processing

  57. Medical Image Analysis (CT, MRI, Ultrasound) — MCQs | Digital Image Processing

  58. Remote Sensing Image Processing — MCQs | Digital Image Processing

  59. Satellite Image Enhancement — MCQs | Digital Image Processing

  60. Deep Learning for Image Processing (CNN, GANs, Autoencoders) — MCQs | Digital Image Processing

  61. Image Captioning — MCQs | Digital Image Processing

  62. Semantic & Instance Segmentation (Mask R-CNN, U-Net) — MCQs | Digital Image Processing

  63. Super Resolution (SRCNN, ESRGAN) — MCQs | Digital Image Processing

  64. Image Inpainting — MCQs | Digital Image Processing

  65. Image Style Transfer — MCQs | Digital Image Processing

  66. Real-Time Image Processing — MCQs | Digital Image Processing

  67. Augmented Reality (AR) & Virtual Reality (VR) — MCQs | Digital Image Processing

  68. DIP using MATLAB/OpenCV/Python — MCQs | Digital Image Processing

  69. DIP in IoT & Embedded Systems — MCQs | Digital Image Processing

  70. Ethics & Privacy in Image Processing — MCQs | Digital Image Processing

Computer Science Repeated MCQs Book Download

Exit mobile version