Deep Learning in Computer Vision

Code Regarding projects for Deep Learning in Computer Vision can be found next

1. Optical Flow With RANSAC algorithm for video Analysis

Studying and measuring metrics to analyze the information content of video sequences. This is achieved through the analysis of Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and Entropy graphs, which give information regarding the images sequences given consecutive frames.
Coding optical flow estimation methods using global motion estimations and by visualizing the distribution of the energy of residual motion.

2. Fully Connected Neural Networks from Scratch on MNIST Dataset

Implenting basic Neural Networks from Scratch for binary and multiclass classification on MNIST Dataset with different networks depth and width .

3. Fully Connected Neural Networks with Keras on MNIST Dataset

Implementing basic Neural Networks for binary and multiclass classification based on an one vs all approach, and all vs all approach on binary and multiclass classification respectively on MNIST Dataset with a pre-existing library to build sequential fully connected networks. Investigating on the effectiveness and behavior of the architecture after changing three main parameters

1. Batch Sizes
2. Activation Functions
3. Optimizers

4. Convolutional Neural Networks with Keras on MNIST and CIFAR10 Datasets

Implementing one, two and three layers deep CNNs with a sequential Keras model for multiclass classification on MNIST and CIFAR10 Datasets. A comparisson between a fully connected layered network and CNNs is posed. Transfer learning from RESNET model is performed for CIFAR10 classification.

5. Basic Generative Adversial Networks on MNIST and CIFAR10 Datasets

Implementation of GANs based on only fully connected layers for MNIST dataset, on both Fully connected and CNNs for MNIST and CIFAR10 Datasets