Apply segmentation to your computer vision projects and achieve success by developing powerful tools with numerous applications in fields such as healthcare" 

Of the many techniques and procedures included in the field of computer vision, segmentation is one of the most important. It makes it possible to differentiate objects, tissues or materials based on the identification of pixel types, separating the elements and facilitating their analysis. This has applications in areas such as traffic, detecting lanes and signs, in satellite images or in medical diagnostics. 

For this reason, this Postgraduate certificate in Segmentation with Deep Learning in Computer Vision is so important, as it prepares professionals to master one of the most promising fields within artificial intelligence and machine learning. Therefore, this program will delve into issues such as semantic segmentation and instantiated segmentation, cost functions or video segmentation, among others. 

All this, following an innovative 100% online teaching methodology that will allow students to choose the time and place to study, while being guided by a high-level teaching staff and enjoying multimedia resources such as interactive summaries, master classes, explanatory videos and practical exercises. 

Segmentation is fundamental in the field of computer vision: specialize now with this Postgraduate certificate"

This Postgraduate certificate in Segmentation with Deep Learning in Computer Vision contains the most complete and up-to-date academic program on the market. Its most important features are:

• 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 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

TECH Global University's innovative teaching methodology will allow you to balance your work with your studies. Don't think twice and enroll" 

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 student will be assisted by an innovative interactive video system created by renowned and experienced experts.   

In this educational program, you will delve into the many applications of computer vision segmentation"

Join deep learning with segmentation and develop powerful computer vision projects"

This Postgraduate certificate in Segmentation with Deep Learning in Computer Vision contains 1 module, subdivided into 10 units. Students will be able to delve into issues such as semantic segmentation and instantiated segmentation, evaluation metrics, cost functions, self-organizing maps, semantic segmentation applying deep learning or segmentation in video and point clouds, among many others. 

Integrate the principles of deep learning with the best segmentation techniques thanks to what you will learn throughout this Postgraduate certificate"  

Module 1. Image Segmentation with Deep Learning

1.1. Object Detection and Segmentation

1.1.1. Semantic Segmentation

 1.1.1.1. Semantic Segmentation Use Cases

1.1.2. Instantiated Segmentation

 1.1.2.1. Instantiated Segmentation Use Cases

1.2. Evaluation Metrics

1.2.1. Similarities with Other Methods
1.2.2. Pixel Accuracy
1.2.3. Dice Coefficient (F1 Score)

1.3. Cost Functions

1.3.1. Dice Loss
1.3.2. Focal Loss
1.3.3. Tversky Loss
1.3.4. Other Functions

1.4. Traditional Segmentation Methods

1.4.1. Threshold Application with Otsu and Riddlen
1.4.2. Self-Organizing maps
1.4.3. GMM-EM Algorithm

1.5. Semantic Segmentation Applying Deep Learning: FCN

1.5.1. FCN
1.5.2. Architecture
1.5.3. FCN Applications

1.6. Semantic Segmentation Applying Deep Learning: U-NET

1.6.1. U-NET
1.6.2. Architecture
1.6.3. U-NET Application

1.7. Semantic Segmentation Applying Deep Learning: Deep Lab

1.7.1. Deep Lab
1.7.2. Architecture
1.7.3. Deep Lab Application

1.8. Instantiated Segmentation Applying Deep Learning: Mask RCNN

1.8.1. RCNN Mask
1.8.2. Architecture
1.8.3. Application of a Mask RCNN

1.9. Video Segmentation

1.9.1. STFCN
1.9.2. Semantic Video CNNs
1.9.3. Clockwork Convnets
1.9.4. Low-Latency

1.10. Point Cloud Segmentation

1.10.1. The Point Cloud
1.10.2. PointNet
1.10.3. A-CNN

You won't find a more comprehensive and in-depth course on segmentation with deep learning. Enroll and check it out"