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Teaching:

Pattern Recognition System: semester 1, 2017-2018

Group 30441/2: Wednesday - 11-14, room 106 Dorobantilor

Group 30442/2: Wednesday - 14-17, room 106 Dorobantilor

Attendance and Marks

Attendance

Laboratory topics

Week Title Eng Ro Data
1 Least Mean Squares Line Fitting pdf pdf zip
2 RANSAC Line Fitting pdf pdf zip
3 Hough Transform Line Fitting pdf pdf zip
4 Distance Transform pdf pdf zip
5 Statistical Data Analysis pdf pdf zip
6 K-means Clustering pdf pdf zip
7 Principal Component Analysis pdf pdf zip
8 K-Nearest Neighbor Classifier pdf pdf zip
9 Naive Bayes Classifier pdf pdf zip
10 Perceptron Algorithm for Linear Classifier pdf pdf zip
11 AdaBoost with Decision Stumps pdf pdf zip
12 Support Vector Machine pdf pdf zip

Projects:

Possible topics:
  1. Pedestrian detection in intensity images by means of simple classifiers (Bayesian, Perceptron, AdaBoost)
  2. Gesture recognition by means of a Bayesian approach
  3. Face detection in grayscale / color images
  4. Signature recognition
  5. Pedestrian detection in thermal images by means of simple classifiers (Bayesian, Perceptron, AdaBoost)
  6. Handwritten character recognition
  7. Face recognition in grayscale / color images
  8. Pedestrian detection in intensity images by means of hierarchical template matching.
  9. Pedestrian detection in thermal images by means of hierarchical template matching.
  10. Traffic sign detection and recognition in color images.
  11. Coin recognition in color images.
  12. Banknote recognition in color images.
  13. Indoor vs outdoor scene classification.
  14. Image segmentation of landscape and urban scenes.

Project evaluation

The final mark of the project will be formed by combining individual marks for the following evaluation items. The weights of each evaluation item are given below.
  1. Functionality - 30%
  2. Documentation - 10%
  3. Code structure - 10%
    • Functionality, documentation and code structure will be evaluated by the teacher in the next 7 days after you present the project.
  4. Semester activity - 40%
  5. Final presentation - 10% (includes running the project and presenting the documentation during the project presentation session
You will create an archive having the form: groupNb_firstName_lastName.zip Where The archive will contain:
  1. A folder named src where you will put the code of the project.
  2. A text file named README where you will write how the program is to be run, and what special constrains and dependencies are needed for running the project. (Eg. Write if you have used Diblook, or OpenCV or if there are any images needed for running the project - for example datasets containing cars, or if you have any configuration files that you have used etc).
  3. A folder named doc that will contain the Documentation (.doc or .pdf or .ppt)
  4. Optionally - a folder named resources where you will place other resources needed for running the project (for example images, libraries etc). If you have used openCV just mention this in the README file, there is no need to copy it in the resources directory.