You will delve into Adversarial Networks to generate the most realistic data thanks to this 100% online university degree”

Computer Vision is a field of Machine Learning of great importance to most technology companies. This technology allows both computers and systems to extract meaningful information from digital images, videos and even other visual inputs. Among its many benefits is the increase in the level of precision during manufacturing processes and the elimination of human error. Therefore, these instruments guarantee the highest quality of products while facilitating the resolution of problems during production. 

In view of this reality, TECH is developing a professional master’s degree that will address Computer Vision in detail. Designed by experts in the field, the curriculum will delve into 3D image processing. In this regard, the program will offer students the most advanced processing software to visualize the data. The syllabus will also focus on Deep Learning analysis, given its relevance in dealing with large and complex data sets. This will allow graduates to enrich their usual work procedures with state-of-the-art algorithms and models. In addition, the teaching materials will provide a wide range of Computer Vision techniques using different frameworks (among which Keras, Tensorflow v2 Pytorch). 

As for the format of this university degree, it is based on a 100% online methodology. All that is required is that graduates have an electronic device with Internet access (such as a computer, cell phone or tablet) to access the Virtual Campus. There they will find a library full of multimedia resources with which they will strengthen their knowledge in a dynamic way. It should be noted that TECH employs the innovative Relearningmethodology in all its programs, which will allow students to assimilate knowledge in a natural way, reinforced with audiovisual resources to ensure that it lasts in memory and over time.

You will specialize in a key area of future technology that will immediately advance your career”

This professional master’s degree in Computer Vision 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 they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out 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

Looking to specialize in Evaluation Metrics? Achieve it with this program in just 12 months"

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 effectively handle Deep Learning to solve the most complex problems"

You will have access to a learning system based on repetition, with natural and progressive teaching throughout the entire syllabus"

Through this university degree, students will acquire a comprehensive approach to Computer Vision. In this way, graduates will keep abreast of the latest developments in this field.  They will also acquire new skills to develop their professional work using the most advanced tools of Machine Learning.  This will allow them to execute algorithms to create real solutions and innovate in various booming industries, such as video games or cyberspace.  

Update your knowledge in Computer Vision through innovative multimedia content"

General Objectives

• Obtain an overview of the devices and hardware used in the computer vision world
• Analyze the different fields in which vision is applied
• Identify where the technological advances in vision are at the moment
• Assess what is being researched and what the next few years hold
• Establish a solid foundation in the understanding of digital image processing algorithms and techniques
• Assess fundamental computer vision techniques
• Analyze advanced image processing techniques
• Introducing the open 3D library
• Analyze the advantages and difficulties of working in 3D instead of 2D
• Introduce neural networks and examine how they work
• Analyze metrics for proper learning
• Analyze existing metrics and tools
• Examine the pipeline of an image classification network
• Analyze semantic segmentation neural networks and their metrics

Specific Objectives

Module 1. Computer Vision

• Establish how the human vision system works and how an image is digitized
• Analyze the evolution of computer vision
• Evaluate image acquisition techniques
• Generate specialized knowledge about illumination systems as an important factor when processing an image
• Specify what optical systems exist and evaluate their use
• Examine the 3D vision systems and how these systems provide depth to images
• Develop the different existing systems outside the field visible to the human eye

Module 2. Applications and State-of-the-Art

• Analyze the use of computer vision in industrial applications
• Determine how vision is applied in the autonomous vehicle revolution
• Analyze images in content analysis
• Develop Deep Learning algorithms for medical analysis and Machine Learning algorithms for operating room assistance
• Analyze the use of vision in commercial applications
• Determine how robots have eyes thanks to artificial vision and how it is applied in space travel
• Establish what augmented reality is and fields of use
• Analyze the Cloud Computing revolution
• Present the State of the Art and what the coming years have in store for us

Module 3. Digital Image Processing

• Examine commercial and open-source digital image processing libraries
• Determine what a digital image is and evaluate the fundamental operations to be able to work with them
• Introduce image filters
• Analyze the importance and use of histograms
• Present tools to modify images pixel by pixel
• Propose image segmentation tools
• Analyze morphological operations and their applications
• Determine the methodology in image calibration
• Evaluate methods for segmenting images with conventional vision

Module 4. Advanced Digital Image Processing

• Examine advanced digital image processing filters
• Determine contour extraction and analysis tools
• Analyze object search algorithms
• Demonstrate how to work with calibrated images
• Analyze mathematical techniques for geometry analysis
• Evaluate different options in image compositing
• Develop user interface

Module 5. 3D Image Processing

• Examine a 3D image
• Analyze the software used for 3D data processing
• Developing open3D
• Determine the relevant data in a 3D image
• Demonstrate visualization tools
• Establish denoising filters
• Propose Geometric Calculation tools
• Analyze object detection methodologies
• Evaluate triangulation and scene reconstruction methods

Module 6. Deep Learning

• Analyze the families that make up the artificial intelligence world
• Compile the main Frameworks of Deep Learning
• Define neural networks
• Present the learning methods of neural networks
• Fundamentals of cost functions
• Establish the most important activation functions
• Examine regularization and normalization techniques
• Develop optimization methods
• Introduce initialization methods

Module 7. Convolutional Neural Networks and Image Classification

• Generate specialized knowledge on convolutional neural networks
• Establish evaluation metrics
• Analyze the performance of CNNs for image classification
• Evaluate Data Augmentation
• Propose techniques to avoid Overfitting
• Examine different architectures
• Compile inference methods

Module 8. Object Detection

• Analyze how object detection networks work
• Examine traditional methods
• Determine evaluation metrics
• Identify the main datasets used in the marketplace
• Propose architectures of the Two Stage Object Detector type
• Analyze Fine Tuning Methods
• Examine different Single Shoot type architectures
• Establish object tracking algorithms
• Apply detection and tracking of people

Module 9. Image Segmentation with Deep Learning

• Analyze how semantic segmentation networks work
• Evaluate traditional methods
• Examine evaluation metrics and different architectures
• Examine video domains and cloud points
• Apply theoretical concepts through various examples

Module 10. Advanced Image Segmentation and Advanced Computer Vision Techniques

• Generate specialized knowledge on the handling of tools
• Examine Semantic Segmentation in medicine
• Identify the structure of a segmentation project
• Analyze Autoencoders
• Develop Adversarial Generative Networks

You will learn valuable lessons through real cases in simulated learning environments”