You will effectively handle 3D image processing and visualization software thanks to this 100% digital Postgraduate diploma"

Within Artificial Intelligence, several branches emerge such as robotics, machine learning or computer vision. The latter is gaining more and more prominence in society, as it allows computers to extract valuable information in multiple formats (such as images, videos and other inputs). However, to benefit from this processing, professionals need to have a solid understanding of the particularities of this technique applied to 2D and 3D images. They must also have advanced skills to properly handle software for visualization and metrology.

This is how this Postgraduate diploma in 2D and 3D Image Processing was born. This university program will provide developers with the most modern tools to efficiently carry out this essential task in the field of artificial vision.  To this end, the syllabus will delve into the Fourier Analysis technique so that students can decompose signals and functions into their frequency components. In addition, the syllabus will explore other essential aspects such as object segmentation or the library for 3D data processing. The teaching materials will also focus on the calibration process in a 2D camera/robot system, so that graduates can ensure that these intelligent systems can interact effectively with the visual environment.

On the other hand, TECH will provide its students with a 100% online Virtual Campus, adapted to the needs of working professionals who want to advance in their careers. In addition, it will employ the  Relearning methodology, based on the repetition of key concepts to fix knowledge and facilitate learning. In this way, the combination of flexibility and a robust pedagogical approach makes it highly accessible. All of this will be accessible remotely, from any portable device, or can be downloaded for offline consultation.

An academic institution that adapts to you and designs a program that will allow you to reconcile your daily activities with a quality program"

This Postgraduate diploma in 2D and 3D Image Processing 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 delve into the recognition of human emotions from images to identify trends and assess audience sentiments"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this educational 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 specialize in image composition to improve contours with this program of only 450 hours"

With the innovative Relearning methodology, you will enjoy dynamic learning to meet all your professional goals"

This university program is structured in 3 specialized modules in which you will explore the most advanced for solving problems such as image segmentation, morphological operations or code reading. The syllabus will delve into digital image processing, emphasizing aspects such as pixel relationships and geometric transformations. In addition, the didactic contents will delve into the tracking of objects with conventional vision using avant-garde techniques, among which Optical flow stands out. In this way, professionals will have at their disposal state-of-the-art tools to carry out their work successfully.

You will delve into the Triangulation of Depth Mapping to enable your robots to make informed and safe decisions in real time"

Module 1. Digital Image Processing

1.1. Computer Vision Development Environment

1.1.1. Computer Vision Libraries
1.1.2. Programming Environment
1.1.3. Visualization Tools

1.2. Digital image Processing

1.2.1. Relationships Between Pixels
1.2.2. Image Operations
1.2.3. Geometric Transformations

1.3. Pixel Operations

1.3.1. Histogram
1.3.2. Histogram Transformations
1.3.3. Operations on Color Images

1.4. Logical and Arithmetic Operations

1.4.1. Addition and Subtraction
1.4.2. Product and Division
1.4.3. And/Nand
1.4.4. Or/Nor
1.4.5. Xor/Xnor

1.5. Filters

1.5.1. Masks and Convolution
1.5.2. Linear Filtering
1.5.3. Non-Linear Filtering
1.5.4. Fourier Analysis

1.6. Morphological Operations

1.6.1. Erosion and Dilation
1.6.2. Closing and Opening
1.6.3. Top_hat and Black hat
1.6.4. Contour Detection
1.6.5. Skeleton
1.6.6. Hole Filling
1.6.7. Convex Hull

1.7. Image Analysis Tools

1.7.1. Edge Detection
1.7.2. Detection of Blobs
1.7.3. Dimensional Control
1.7.4. Color Inspection

1.8. Object Segmentation

1.8.1. Image Segmentation
1.8.2. Classical Segmentation Techniques
1.8.3. Real Applications

1.9. Image Calibration

1.9.1. Image Calibration
1.9.2. Methods of Calibration
1.9.3. Calibration Process in a 2D Camera/Robot System

1.10. Image Processing in a Real Environment

1.10.1. Problem Analysis
1.10.2. Image Processing
1.10.3. Feature Extraction
1.10.4. Final Results

Module 2. Advanced Digital Image Processing

2.1. Optical Character Recognition (OCR)

2.1.1. Image Pre-Processing
2.1.2. Text Detection
2.1.3. Text Recognition

2.2. Code Reading

2.2.1. 1D Codes
2.2.2. 2D Codes
2.2.3. Applications

2.3. Pattern Search

2.3.1. Pattern Search
2.3.2. Patterns Based on Gray Level
2.3.3. Patterns Based on Contours
2.3.4. Patterns Based on Geometric Shapes
2.3.5. Other Techniques

2.4. Object Tracking with Conventional Vision

2.4.1. Background Extraction
2.4.2. Meanshift
2.4.3. Camshift
2.4.4. Optical Flow

2.5. Facial Recognition

2.5.1. Facial Landmark Detection
2.5.2. Applications
2.5.3. Facial Recognition
2.5.4. Emotion Recognition

2.6. Panoramic and Alignment

2.6.1. Stitching
2.6.2. Image Composition
2.6.3. Photomontage

2.7. High Dynamic Range (HDR) and Photometric Stereo

2.7.1. Increasing the Dynamic Range
2.7.2. Image Compositing for Contour Enhancement
2.7.3. Techniques for the Use of Dynamic Applications

2.8. Image Compression

2.8.1. Image Compression
2.8.2. Types of Compressors
2.8.3. Image Compression Techniques

2.9. Video Processing

2.9.1. Image Sequences
2.9.2. Video Formats and Codecs
2.9.3. Reading a Video
2.9.4. Frame Processing

2.10. Real Application of Image Processing

2.10.1. Problem Analysis
2.10.2. Image Processing
2.10.3. Feature Extraction
2.10.4. Final Results

Module 3. 3D Image Processing

3.1. 3D Imaging

3.1.1. 3D Imaging
3.1.2. 3d Image Processing Software and Visualizations
3.1.3. Metrology Software

3.2. Open3D

3.2.1. Library for 3D Data Processing
3.2.2. Features
3.2.3. Installation and Use

3.3. The Data

3.3.1. Depth Maps in 2D Image
3.3.2. Pointclouds
3.3.3. Normal
3.3.4. Surfaces

3.4. Visualization

3.4.1. Data Visualization
3.4.2. Controls
3.4.3. Web Display

3.5. Filters

3.5.1. Distance Between Points, Eliminate Outliers
3.5.2. High Pass Filter
3.5.3. Downsampling

3.6. Geometry and Feature Extraction

3.6.1. Extraction of a Profile
3.6.2. Depth Measurement
3.6.3. Volume
3.6.4. 3D Geometric Shapes
3.6.5. Shots
3.6.6. Projection of a Point
3.6.7. Geometric Distances
3.6.8. Kd Tree
3.6.9. 3D Features

3.7. Registration and Meshing

3.7.1. Concatenation
3.7.2. ICP
3.7.3. Rzansac 3D

3.8. 3D Object Recognition

3.8.1. Searching for an Object in the 3d Scene
3.8.2. Segmentation
3.8.3. Bin Picking

3.9. Surface Analysis

3.9.1. Smoothing
3.9.2. Orientable Surfaces
3.9.3. Octree

3.10. Triangulation

3.10.1. From Mesh to Point Cloud
3.10.2. Depth Map Triangulation
3.10.3. Triangulation of Unordered Point Clouds

A unique training experience, key and decisive to boost your professional development"