ComputerVission

Project 1

• Apply filtering techniques(gaussian filter, bilateral filter)
• Object detection(Sobel, Hough transform)
• Masking: merge, blur
• Morphological Operations (Dilataion, Erosion, Closing)

Project 2

For the complete notebook visit: cv-ga1.ipynb

This project is divided into two task:

1. Craft and Extract face Features from four celebrities using SIFT and PCA detector
2. Train a model (kNN, SVM, RandomForest, CNN) using supervised learning on a dataset of 80 labelled images
   1. For testing the accuracy of the model a dataset of 1136 unlabelled images where provided
   2. Kaggle was used to run a competition among several models

The Dataset

Actor Face	Label	Num Imgs
Jesse Eisenberg	1	30
Michael Cera (who arguably looks similar to Jesse Eisenberg)	0	10
Mila Kunis	2	30
Sarah Hyland (who arguably looks similar to Mila Kunis)	0	10

Feature Extractors

SIFT

• Visual Bag of Words are used to create fingerprints of each picture
• kMeans clustering is used to narrow down the features extracted from each face
• Implement t-SNE plots to reduce dimensionality of set and fine tune hyperparamters of SIFT extractor

PCA

• Reduce the dimensionality of the feature space by projecting the data onto a lower-dimension

• Use eigenfaces to reconstruct an actor's face using PCA features extractor

  

Results

The Ensemble method implemented use a combination of kNN, SVM, and CNN to predict the label of an image. The model shows positive results achieving the following accuracies:

Models	Training Set	Test Set
Ensembles	0.86	0.81
kNN	0.83	0.66
SVM	0.75	0.76
CNN	0.89	0.78

It is concluded that a combination of models using majority vote over the labels using hard constraint yields an accuracy that is optimally. Put into words, 8 out of 10 times we are able to recognise/distinguish who actor is who.

Project 3

[Update: 24 May 2023] - Project Done. Documentation will follow soon