COMSATS University Islamabad (Lahore Campus)
□Sessional-1 □ Sessional-II √ Terminal Examination
Course Title:
Computer Vision
Course Instructor/s: Dr. Zeeshan Gillani
Semester:
6th
Batch: FA18
Section:
Time Allowed:
90 Minutes
Student’s Name: SHAHROZE NADEEM
Important Instructions / Guidelines:
a. Start with prayer.
b. 50 Mins to solve the paper+10 Mins to Upload = 60 Mins
c. Attempt your own exam
– Spring 2020
Course Code: CSC462 Credit 3(2,1)
Hours:
Programme BCS
Name:
C
Date:
40
Maximum Marks:
Reg. FA18-BCS-147-C
No.
Question no 1: Feature selection is integral part in image classification problem. You are required to
extract feature descriptor using chain codes for the following example using 4 directional chain codes
in anti-clockwise direction.
.
[10]
Question no 2: Perceptron is basic neural network algorithm. Design an architecture of perceptron to
solve image classification problem by dividing images into two groups and sample input shown in
figure below. If number of brighter pixels are greater than darker pixels than image is classified as
brighter image otherwise darker image. [10]





Identify number of class
Write an activation function
Define input feature and size
Suitable learning rate
Draw a perceptron architecture
Question no 3: You are required to calculate the total learnable parameters for following CNN
network. In order to improve this network, suggest possible advantages and disadvantages if we opt
for more deeper CNN.
[10]
INPUT
CONV1(f=6, s=1, Nc=10)
POOL1(f=2, s=2)
CONV1(f=6, s=1, Nc=20)
POOL2(f=2, s=2)
FC3 (200)
FC4 (100)
Softmax(10)
Activation shape
32,32,3
30,30,10
15,15,10
12,12,20
6,6,20
200,1
100,1
10,1
Activation Size
3072
9000
2250
1440
720
120
100
10
Parameters
0
(3*3*3+1)*10=280
0
(3*3*10+1)*20=1820
0
720*120+120=86520
120*100+100=12100
100*10+10=1010
Question no 4: One hot encoded technique was used to transform images into 1 dimension vector and
pass to SVM and KNN classifiers for image classification task. Discuss drawback of this technique
and suggest more suitable method with example.
[5]
In one hot encoded technique we face difficulties because of feature space blow up in no time. So that
the representation size grows with the corpus. Each vector in one hot encoded is equidistant from
every vectors. It is not suitable for tasks such as entity recognition. On contrary a distributed
representation is quite much less in size than original input. It captures salient features among
different elements of input that’s why one hot encoded is not recommended
Question no 5: Describe how CNN uses feature sharing and spare connections to solve complex
problem in computer vision and compare them to traditional feature extraction techniques
[5]
For image recognition, a convolutional neural network is used. It's divided into two sections:
convolution and pooling. It's a little easier to train than traditional image recognition methods. It
solves complex problems by reducing the number of connections and using feature sharing and spare
connections to improve generalization. It's also possible to improve problem solving by replacing the
linear filter with a non-linear function.