Probability Engine · CSC413

Image Processing (BSc CSIT, CSC413): the questions likely to come

87 analyzed questions from 8 past papers (2074-2082), grouped by syllabus unit — each with its probability, how often it's been asked, and where to study the answer.

Papers analyzed

2074-2082

Analyzed questions

across 7 syllabus units

Very likely units

high-probability topics

Units = 80% of marks

study these first

Model answers for this subject are being written. Every question links to its original paper so you can study from the source meanwhile.

Pick a unit

U2 · Q1/23 · 208210 marks

Intensity Transformations and Spatial Filtering

Distinguish between high frequency emphasis filter and Laplacian filter. Find the output of the following image using histogram equalization, where number of possible gray levels is 8.

3 3 3 3 3

2 3 4 3 2

2 4 4 4 2

2 3 4 3 2

3 3 3 3 3

19%

Occasional to appearAppeared in 1 of the last 1 board papers

Seen in

How well do you know this?rating moves you on

MODEL ANSWERU2 · 10 marks

High-Frequency Emphasis Filter vs Laplacian Filter

Aspect	High-Frequency Emphasis Filter	Laplacian Filter
Domain	Mainly frequency domain	Mainly spatial domain (a mask)
Definition	$H_{hfe}(u,v)=a+b\,H_{hp}(u,v)$ where $H_{hp}$ is a high-pass filter, $a\ge 0$ , $b>a$	$\nabla^2 f=\dfrac{\partial^2 f}{\partial x^2}+\dfrac{\partial^2 f}{\partial y^2}$ , a second-order derivative
Idea	Adds back a fraction of the original so low frequencies (background) are not lost while high frequencies (edges) are boosted	Produces only the edge/detail map; must be added to the original for sharpening: $g=f-\nabla^2 f$
Effect	Sharpens and preserves overall tonality/brightness	Strongly highlights edges but darkens flat regions if used alone
Order	Built on a high-pass response	Pure second-derivative operator
Output	Enhanced image with detail emphasized	Edge image (zero in flat areas)

In short, the high-frequency emphasis filter is essentially a high-pass filter with an added offset so the result is not a bare edge map, whereas the Laplacian is an isotropic second-derivative operator whose raw output is an edge map that is normally combined with the original for sharpening.

Histogram Equalization ( $L=8$ )

Input image ( $5\times5$ ):

Total pixels $MN = 25$ , levels $L=8$ so the scaling factor is $L-1=7$ .

Step 1 – Histogram (count of each gray level):

$r_k$	$n_k$
2	6
3	14
4	5

(All other levels 0, 1, 5, 6, 7 have count 0.)

Step 2 – PDF, CDF and transformation $s_k=\text{round}\big[(L-1)\,\text{CDF}\big]$ :

$r_k$	$n_k$	$p_r=n_k/25$	CDF	$(L-1)\cdot$ CDF	$s_k$
2	6	0.24	0.24	1.68	2
3	14	0.56	0.80	5.60	6
4	5	0.20	1.00	7.00	7

Step 3 – Mapping: $2\to2,\; 3\to6,\; 4\to7$ .

Output (equalized) image:

The original values were clustered in $\{2,3,4\}$ ; after equalization they are spread across $\{2,6,7\}$ , increasing the global contrast.

AI-generated answer · unverifiedView in 2082 paper →

U2 · Question 1 of 23

Question Priority · U2ranked by appearance likelihood — study top-down

Intensity Transformations and Spatial Filtering

Analyzed next22%

★ TOP PICK

Distinguish between high frequency emphasis filter and Laplacian filter. Find the output of the following image using histogram equalization, where number of possible gray levels is 8.

3 3 3 3 3

2 3 4 3 2

2 4 4 4 2

2 3 4 3 2

3 3 3 3 3

10 marksSEEN IN

19%

What is histogram processing? Explain histogram equalization and histogram specification with mathematical formulation and an example.

10 marksSEEN IN

16%

Explain image enhancement techniques in the spatial and frequency domains with suitable examples and their comparative advantages.

10 marksSEEN IN

15%

What is histogram equalization? Perform histogram equalization on a given 3-bit image with the listed gray-level distribution and show all steps.

10 marksSEEN IN

13%

Define histogram of an image and explain its uses.

5 marksSEEN IN

17%

Explain the histogram equalization process briefly.

5 marksSEEN IN

15%

Explain power-law (gamma) transformation.

5 marksSEEN IN

22%

Explain image enhancement in the spatial domain. Discuss point processing techniques (negative, log, power-law) and histogram processing.

10 marksSEEN IN

11%

Define clipping and contrast stretching. Compute Hadamard transform of the data sequence {1, 2, 0, 3}.

5 marksSEEN IN

19%

Explain log transformation and its use.

5 marksSEEN IN

16%

What is convolution in spatial filtering?

5 marksSEEN IN

16%

What is the difference between brightness and contrast?

5 marksSEEN IN

13%

Explain bit-plane slicing.

5 marksSEEN IN

13%

Explain the different types of filters used in image processing.

5 marksSEEN IN

12%

What is the difference between smoothing and sharpening?

5 marksSEEN IN

12%

Explain image negatives and their application.

5 marksSEEN IN

12%

What is image sharpening? Which filters are used?

5 marksSEEN IN

11%

Explain contrast stretching.

5 marksSEEN IN

What is histogram specification (matching)?

5 marksSEEN IN

Differentiate between mean filter and median filter.

5 marksSEEN IN

What is image enhancement? Differentiate between spatial and frequency domain enhancement.

5 marksSEEN IN

Explain the concept of a histogram of an image.

5 marksSEEN IN

What is a low-pass filter? How is it used for smoothing?

5 marksSEEN IN

03The mock

Sit a probable paper

A full mock exam built from the most likely questions, mirroring the real paper's structure. Every slot is a real past question.

Most Probable Paper

Mirrors the real structure · 60 marks · based on 8 past papers

Section A: Long Answer QuestionsAttempt any TWO questions.

1.
What is image segmentation? Explain adaptive thresholding and the region split-and-merge technique with examples.
[10 marks]
Image SegmentationVery likelyfrom 2078 paper →
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Image Segmentation) appears in 100% of years.
2.
Distinguish between high frequency emphasis filter and Laplacian filter. Find the output of the following image using histogram equalization, where number of possible gray levels is 8.

3 3 3 3 3

2 3 4 3 2

2 4 4 4 2

2 3 4 3 2

3 3 3 3 3
[10 marks]
Intensity Transformations and Spatial FilteringVery likelyfrom 2082 paper →
Asked once (2082); so far only in internal assessments, not the board; and its topic (Intensity Transformations and Spatial Filtering) appears in 100% of years.
3.
What is histogram processing? Explain histogram equalization and histogram specification with mathematical formulation and an example.
[10 marks]
Intensity Transformations and Spatial FilteringVery likelyfrom 2081 paper →
Asked once (2081); so far only in internal assessments, not the board; and its topic (Intensity Transformations and Spatial Filtering) appears in 100% of years.

Section B: Short Answer QuestionsAttempt any EIGHT questions.

1.
Explain the properties of the 2D DFT.
[5 marks]
Filtering in the Frequency DomainVery likelyfrom 2080 paper →
This question has recurred in 4 of 8 years; so far only in internal assessments, not the board; and its topic (Filtering in the Frequency Domain) appears in 100% of years.
2.
Define histogram of an image and explain its uses.
[5 marks]
Intensity Transformations and Spatial FilteringVery likelyfrom 2078 paper →
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Intensity Transformations and Spatial Filtering) appears in 100% of years.
3.
Explain the histogram equalization process briefly.
[5 marks]
Intensity Transformations and Spatial FilteringVery likelyfrom 2077 paper →
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Intensity Transformations and Spatial Filtering) appears in 100% of years.
4.
Explain power-law (gamma) transformation.
[5 marks]
Intensity Transformations and Spatial FilteringVery likelyfrom 2080 paper →
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Intensity Transformations and Spatial Filtering) appears in 100% of years.
5.
Explain dilation and erosion with examples.
[5 marks]
Image SegmentationVery likelyfrom 2081 paper →
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Image Segmentation) appears in 100% of years.
6.
Distinguish between forward and inverse transform. Define pixel coding, run length and bit plane.
[5 marks]
Image CompressionVery likelyfrom 2082 paper →
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Image Compression) appears in 100% of years.
7.
Define clipping and contrast stretching. Compute Hadamard transform of the data sequence {1, 2, 0, 3}.
[5 marks]
Intensity Transformations and Spatial FilteringVery likelyfrom 2082 paper →
Asked once (2082); so far only in internal assessments, not the board; and its topic (Intensity Transformations and Spatial Filtering) appears in 100% of years.
8.
Explain log transformation and its use.
[5 marks]
Intensity Transformations and Spatial FilteringVery likelyfrom 2081 paper →
Asked once (2081); so far only in internal assessments, not the board; and its topic (Intensity Transformations and Spatial Filtering) appears in 100% of years.
9.
What is convolution in spatial filtering?
[5 marks]
Intensity Transformations and Spatial FilteringVery likelyfrom 2081 paper →
Asked once (2081); so far only in internal assessments, not the board; and its topic (Intensity Transformations and Spatial Filtering) appears in 100% of years.

04The receipts

Behind the numbers

The raw evidence the predictions are computed from: marks per unit per year, syllabus weights, trends, and coverage.

Show the heatmap, topic table and coverage analysis

The receipt: marks per unit, per year

Each row is a syllabus unit, each column an exam year, each cell the marks that unit earned that year. Click any cell to see the actual questions behind it.

Marks:nonefew → many

2074

2075

2077

2078

2079

2080

2081

2082

Total

U2Intensity Transformations and Spatial Filtering

165

U7Image Segmentation

145

U3Filtering in the Frequency Domain

120

U6Image Compression

U1Introduction

U4Image Restoration and Reconstruction

U5Color Image Processing

#	Syllabus unit	Probability	Appeared	Avg marks	Syllabus weight	Exam vs syllabus	Trend	Questions
1	U2Intensity Transformations and Spatial Filtering	Very likely100%	2074 2075 2077 2078 2079 2080 2081 2082	20.6	20%9 lecture hrs	Over-examinedexam 28% · syllabus 20%	Fading	3 recurring23 total
2	U7Image Segmentation	Very likely100%	2074 2075 2077 2078 2079 2080 2081 2082	18.1	9%4 lecture hrs	Over-examinedexam 24% · syllabus 9%	Rising	2 recurring23 total
3	U3Filtering in the Frequency Domain	Very likely100%	2074 2075 2077 2078 2079 2080 2081 2082	15	20%9 lecture hrs	Balancedexam 20% · syllabus 20%	Steady	1 recurring13 total
4	U6Image Compression	Very likely100%	2074 2075 2077 2078 2079 2080 2081 2082	8.1	11%5 lecture hrs	Balancedexam 11% · syllabus 11%	Fading	1 recurring11 total
5	U1Introduction	Very likely88%	2074 2075 2077 2079 2080 2081 2082	8.6	11%5 lecture hrs	Balancedexam 10% · syllabus 11%	Steady	none repeat9 total
6	U4Image Restoration and Reconstruction	Possible50%	2074 2075 2080 2082	7.5	18%8 lecture hrs	Under-examinedexam 5% · syllabus 18%	Steady	none repeat5 total
7	U5Color Image Processing	Possible38%	2074 2079 2081	5	11%5 lecture hrs	Under-examinedexam 2% · syllabus 11%	Steady	none repeat3 total

Study smart, not hard

Drag the slider: studying the top 4 units in priority order covers ~82% of all observed marks.

Units to study4/7

~80% line

Lecture time vs exam marks

Where the exam pays more than the curriculum spends: ● lectures vs ● exam marks, as a share of the whole course. A long teal-leading bar = high-yield unit.

U2Intensity Transformations and Spatial Filtering

20% of lectures → 28% of markshigh yield

U7Image Segmentation

9% of lectures → 24% of markshigh yield

U3Filtering in the Frequency Domain

20% of lectures → 20% of marks

U6Image Compression

11% of lectures → 11% of marks

U1Introduction

11% of lectures → 10% of marks

U4Image Restoration and Reconstruction

18% of lectures → 5% of markslow yield

U5Color Image Processing

11% of lectures → 2% of markslow yield

Image Processing (BSc CSIT, CSC413): the questions likely to come

High-Frequency Emphasis Filter vs Laplacian Filter

Histogram Equalization ( $L=8$ )

Intensity Transformations and Spatial Filtering

Sit a probable paper

Most Probable Paper

Behind the numbers

The receipt: marks per unit, per year

Study smart, not hard

Lecture time vs exam marks

2082 B.S.