Tag: Image Processing

Course content

• Welcome
• Setting the stage
• Image Processing and Convolutional Neural Networks
• Natural Language Processing
• Conclusion

Course content

• Introduction
• OCR with Tesseract
• Techniques for image pre-processing
• OCR with EAST for natural scenes
• Training a custom OCR
• Natural scenarios with EasyOCR
• OCR in videos
• Project 1: Searching for specific terms
• Project 2: Scanner + OCR
• Project 3: License plate reading
• Extra content 1: artificial neural networks
• Extra content 2: convolutional neural networks
• Final remarks

Duration

14 hours (usually 2 days including breaks)

Requirements

• Basic understanding of image and video processing.
• Java programming experience.

Audience

• Software developers wishing to utilize a rich, plug-in based open-source framework to create image and video processing applications

Overview

Marvin is an extensible, cross-platform, open-source image and video processing framework developed in Java.  Developers can use Marvin to manipulate images, extract features from images for classification tasks, generate figures algorithmically, process video file datasets, and set up unit test automation.

Some of Marvin’s video applications include filtering, augmented reality, object tracking and motion detection.

In this instructor-led, live course participants will learn the principles of image and video analysis and utilize the Marvin Framework and its image processing algorithms to construct their own application.

Format of the Course

• The basic principles of image analysis, video analysis and the Marvin Framework are first introduced. Students are given project-based tasks which allow them to practice the concepts learned. By the end of the class, participants will have developed their own application using the Marvin Framework and libraries.

Course Outline

Introduction to Marvin

Downloading and Installing Marvin

Setting up an Eclipse Development Environment

The Three Layers of the Marvin Architecture

• Framework
• Plug-ins
• Applications

Components and Libraries

Image Processing in Marvin

Video Processing in Marvin

Multi-Threading in Marvin

Unit Testing in Marvin

Working with MarvinEditor

Creating an Application with Marvin

Working with Plug-ins

Testing the Application

Video Applications

• Video filtering
• Image subtraction and combination
• Tracking
• Face features detection
• Real time tracking of multiple blobs
• Partial shape matching
• Skin-colored pixels detection

Using Marvin Framework for Test Automation

Extending the Framework

Contributing to the Project

Summary and Conclusion

Convolutional Neural Networks

Image Processing

Advance Deep Learning Techniques

Regularization, Normalization

Transfer Learning

Requirements

• Machine Learning
• Linear Regression and Classification
• Matrix Calculus, Probability
• Deep Learning basis: Multi perceptron, optimization
• Python, PyTorch

Description

Dear friend, welcome to the course “Modern Deep Convolutional Neural Networks”! I tried to do my best in order to share my practical experience in Deep Learning and Computer vision with you.

The course consists of 4 blocks:

1. Introduction section, where I remind you, what is Linear layers, SGD, and how to train Deep Networks.
2. Convolution section, where we discuss convolutions, it’s parameters, advantages and disadvantages.
3. Regularization and normalization section, where I share with you useful tips and tricks in Deep Learning.
4. Fine tuning, transfer learning, modern datasets and architectures

If you don’t understand something, feel free to ask equations. I will answer you directly or will make a video explanation.

Prerequisites:

• Matrix calculus, Linear Algebra, Probability theory and Statistics
• Basics of Machine Learning: Regularization, Linear Regression and Classification,
• Basics of Deep Learning: Linear layers, SGD,  Multi-layer perceptron
• Python, Basics of PyTorch

Who this course is for:

• Who knows a bit about neural networks
• Who wants to enrich their Deep Learning and Image Processing knowledge
• Who wants to study advanced techniques and practices

Course content