Optimize your practice with the most innovative strategies in Web Computer Vision thanks to this 100% online program"

Image segmentation with Deep Learning has led to significant advances in fields such as robotics, medicine or security. The main reason is that these systems make it possible to automate complex tasks and analyze large volumes of data in a short period of time. Therefore, experts gain a better understanding thanks to accurate images of the objects of interest. However, in order to enjoy its multiple benefits, it is essential that professionals acquire new skills and incorporate the latest advances in this area into their usual procedures. 

For this reason, TECH implements a Postgraduate diploma that will delve into Advanced Web Computer Vision Techniques. Designed by experts in this field, the curriculum will delve into 3D image processing, using the most innovative software for the visualization of materials. The syllabus will also focus on photo segmentation methods using Deep Learning Moreover, students will examine in detail the Semantic Segmentation Project to develop systems that require an accurate understanding of digital images. It should be noted that the academic itinerary will include the analysis of real case studies and exercises aimed at raising students' competencies.  

Regarding the methodology of the program, it is taught 100% online. In this sense, the only thing students will need is an electronic device with Internet access to enter the Virtual Campus and enjoy the most dynamic didactic content. In addition, TECH uses an innovative pedagogical system:Relearning. This consists of repeating the key contents in a natural way, so that students can learn progressively. Undoubtedly, this is an excellent opportunity for professionals to get a complete update through a university program that adapts to the real needs of experts. 

You will have full mastery of Generative Adversarial Networks and create high quality multimedia content”

This Postgraduate diploma in Advanced Web-Based Computer Vision Techniques contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in computer science and computer vision
• The graphic, schematic and practical contents with which it is conceived scientific and practical information on those disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies 
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

You will access the most effective databases to solve general segmentation problems and evaluate algorithms effectively”

The program’s teaching staff includes professionals from the industry who contribute their work experience to this program, as well as renowned specialists from leading societies and prestigious universities. 

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations. 

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.  

You will be highly qualified to handle the various segmentation tools using different frameworks"

The Relearning system will lead you to advance in a much more agile way through image segmentation with Deep Learning"

This Postgraduate diploma will provide students with a holistic approach to Advanced Web-Based Computer Vision Techniques Through 3 specialized modules, students will delve into the most effective 3D image processing software.  In tune with this, the curriculum will delve into various semantic segmentation techniques applying Deep Learning. This will allow graduates to obtain a detailed and accurate understanding of the contents of an image.  In addition, the curriculum will offer a wide range of libraries for 3D Data Processing, which will facilitate data processing and manipulation.  

Looking to increase your decision-making confidence? Get it by updating your knowledge through this revolutionary university program”

Module 1. 3D Image Processing

1.1. 3D Imaging

1.1.1. 3D Imaging
1.1.2. 3D Image Processing Software and Visualizations
1.1.3. Metrology Software

1.2. Open3D

1.2.1. Library for 3D Data Processing
1.2.2. Features
1.2.3. Installation and Use

1.3. The Data

1.3.1. Depth Maps in 2D Image
1.3.2. Pointclouds
1.3.3. Normal
1.3.4. Surfaces

1.4. Visualization

1.4.1. Data Visualization
1.4.2. Controls
1.4.3. Web Display

1.5. Filters

1.5.1. Distance Between Points, Eliminate Outliers
1.5.2. High Pass Filter
1.5.3. Downsampling

1.6. Geometry and Feature Extraction

1.6.1. Extraction of a Profile
1.6.2. Depth Measurement
1.6.3. Volume
1.6.4. 3D Geometric Shapes
1.6.5. Shots
1.6.6. Projection of a Point
1.6.7. Geometric Distances
1.6.8. Kd Tree
1.6.9. 3D Features

1.7. Registration and Meshing

1.7.1. Concatenation
1.7.2. ICP
1.7.3. Ransac 3D

1.8. 3D Object Recognition

1.8.1. Searching for an Object in the 3D Scene
1.8.2. Segmentation
1.8.3. Bin Picking

1.9. Surface Analysis

1.9.1. Smoothing
1.9.2. Orientable Surfaces
1.9.3. Octree

1.10. Triangulation

1.10.1. From Mesh to Point Cloud
1.10.2. Depth Map Triangulation
1.10.3. Triangulation of unordered Point Clouds

Module 2. Image Segmentation with Deep Learning

2.1. Object Detection and Segmentation

2.1.1. Semantic Segmentation

2.1.1.1. Semantic Segmentation Use Cases

2.1.2. Instantiated Segmentation

2.1.2.1. Instantiated Segmentation Use Cases

2.2. Evaluation Metrics

2.2.1. Similarities with Other Methods
2.2.2. Pixel Accuracy
2.2.3. Dice Coefficient (F1 Score)

2.3. Cost Functions

2.3.1. Dice Loss
2.3.2. Focal Loss
2.3.3. Tversky Loss
2.3.4. Other Functions

2.4. Traditional Segmentation Methods

2.4.1. Threshold Application with Otsu and Riddlen
2.4.2. Self-Organized Maps
2.4.3. GMM-EM Algorithm

2.5. Semantic Segmentation Applying Deep Learning: FCN

2.5.1. FCN
2.5.2. Architecture
2.5.3. FCN Applications

2.6. Semantic Segmentation Applying Deep Learning: U-NET

2.6.1. U-NET
2.6.2. Architecture
2.6.3. U-NET Application

2.7. Semantic Segmentation Applying Deep Learning: Deep Lab

2.7.1. Deep Lab
2.7.2. Architecture
2.7.3. Deep Lab Application

2.8. Instantiated Segmentation Applying Deep Learning: Mask RCNN

2.8.1. Mask RCNN
2.8.2. Architecture
2.8.3. Application of a RCNN Mask 

2.9. Video Segmentation

2.9.1. STFCN
2.9.2. Semantic Video CNNs
2.9.3. Clockwork Convnets
2.9.4. Low-Latency

2.10. Point Cloud Segmentation

2.10.1. The Point Cloud
2.10.2. PointNet
2.10.3. A-CNN

Module 3. Advanced Image Segmentation and Advanced Computer Vision Techniques

3.1. Database for General Segmentation Problems

3.1.1. Pascal Context
3.1.2. CelebAMask-HQ
3.1.3. Cityscapes Dataset
3.1.4. CCP Dataset

3.2. Semantic Segmentation in Medicine

3.2.1. Semantic Segmentation in Medicine
3.2.2. Datasets for Medical Problems
3.2.3. Practical Applications

3.3. Annotation Tools

3.3.1. Computer Vision Annotation Tool
3.3.2. LabelMe
3.3.3. Other Tools

3.4. Segmentation Tools Using Different Frameworks

3.4.1. Keras
3.4.2. Tensorflow v2
3.4.3. Pytorch
3.4.4. Others

3.5. Semantic Segmentation Project. The Data, Phase 1

3.5.1. Problem Analysis
3.5.2. Input Source for Data
3.5.3. Data Analysis
3.5.4. Data Preparation

3.6. Semantic Segmentation Project. Training, Phase 2

3.6.1. Algorithm Selection
3.6.2. Education
3.6.3. Assessment

3.7. Semantic Segmentation Project. Results, Phase 3

3.7.1. Fine Tuning
3.7.2. Presentation of The Solution
3.7.3. Conclusions

3.8. Autoencoders

3.8.1. Autoencoders
3.8.2. Autoencoder Architecture
3.8.3. Noise Elimination Autoencoders
3.8.4. Automatic Coloring Autoencoder

3.9. Generative Adversarial Networks (GANs)

3.9.1. Generative Adversarial Networks (GANs)
3.9.2. DCGAN Architecture
3.9.3. Conditional GAN Architecture

3.10. Enhanced Generative Adversarial Networks

3.10.1. Overview of the Problem
3.10.2. WGAN
3.10.3. LSGAN
3.10.4. ACGAN

You will have access to a collection of multimedia materials in multiple audiovisual formats that will strengthen your learning with dynamism”