Faculty of ICT
Department of Intelligent Computer Systems
CSA3803 (Advanced Project)
Proposal Form
Title:
Object localization and recognition
Project Supervisor:
Dr George Azzopardi
Project Co-supervisor:
(if applicable)
Main Subject Area:
/
Brain-inspired computer vision
Object recognition is an important topic in the field of computer vision.
Recently, a novel method called COSFIRE has been found very effective in
various applications including, traffic sign recognition, bifurcation detection in
and retinal images, among others. This approach is based on time consuming
orientation-selective (Gabor) filters. In this project, we will investigate the using
SIFT keypoint descriptors as input to the COSFIRE approach. It is expected to
Detailed Description of Task (c.
achieve better effectiveness and better efficiency in the localization and
200 words):
recognition of patterns of interest in images.
We will evaluate the method on the benchmark data set Flickr logos which can
be accessed from http://www.multimedia-computing.de/flickrlogos/
The Matlab implementation of the COSFIRE approach can be downloaded
from Matlab script: http://tinyurl.com/p5gklaw
Resources Available:
Computer
G. Azzopardi and N. Petkov, “Trainable COSFIRE filters for keypoint detection
Recommended Reading (at
and pattern recognition”, IEEE Transactions on Pattern Analysis and Machine
least one title):
Intelligence, vol. 35 (2), pp. 490-503, 2013.
•
Prerequisite Knowledge
Required:
Download: http://tinyurl.com/mdb6gmk
Interest in computer vision
Indication of any Ethical Issues
and How these will be Tackled
N/A
(if applicable):
_____________________________
________________________
Signature (Supervisor)
Date
_____________________________
________________________
Signature (Student)
Date
Faculty of ICT
Department of Intelligent Computer Systems
CSA3803 (Advanced Project)
Proposal Form
Title:
TensorFlow for websites
Project Supervisor:
Prof. Alexiei Dingli
Project Co-supervisor:
(if applicable)
Main Subject Area:
The student will make use of a new Machine Learning library, which has just
been released by Google.
TensorFlow is a powerful library for doing large-scale numerical computation.
One of the tasks at which it excels is implementing and training deep neural
networks, a new breed of machine learning techniques. It proved to be
extremely successful on the MNIST database which consists of images of
handwritten digits like these:
Detailed Description of Task (c.
It also includes labels for each image, telling us which digit it is. For example,
200 words):
the labels for the above images are 5, 0, 4, and 1.
For this project, you are requested to perform a similar task but with websites.
So you will:
1. Make use of TensorFlow (as per tutorial) to create a system to
categorize images.
2. Make use of Phantom JS to take snapshots of websites.
3. Make use of website rankings such as (https://www.woorank.com or
http://www.alexa.com/topsites) to get online rankings.
4. Train the classifier created in 1 with the websites
Resources Available:
Recommended Reading (at
least one title):
http://tensorflow.org/
http://tensorflow.org/tutorials
http://tensorflow.org/get_started/os_setup.md
http://phantomjs.org/screen-capture.html
Prerequisite Knowledge
Required:
Machine Learning
Indication of any Ethical Issues
and How these will be Tackled
No ethical issues
(if applicable):
_____________________________
________________________
Signature (Supervisor)
Date
_____________________________
________________________
Signature (Student)
Date