Online or onsite, instructor-led live Image Analysis training courses in Rio de Janeiro.
Rio de Janeiro - Candelaria
Edifício Candelaria Corporate, 65 Candelaria Street, 16th Floor, Rio de Janeiro, Brazil, 20091-020
The Candelaria Corporate Center occupies the 16th floor it is located in the city's largest commercial district. It is also c...
The Candelaria Corporate Center occupies the 16th floor it is located in the city's largest commercial district. It is also close to every kind of business amenity from banking to hotels, and is a short walk from the railway station, as well as being close to the Santos Dumont domestic airport.
Rio de Janeiro, Lagoa Rodrigo de Freitas - Humaita
Rua Humaita, Rio de Janeiro, Brazil, 22261-005
Regus is located in the 6th and 7th floor of Lagoa Corporate Building, Humaitá and Lagoa Area, Rio de Janeiro. It is the onl...
Regus is located in the 6th and 7th floor of Lagoa Corporate Building, Humaitá and Lagoa Area, Rio de Janeiro. It is the only corporate building in Lagoa Rodrigo de Freitas area. Next to the Rebouças Tunnel, going north zone and close to Gavea region with easy access to west zone/Barra da Tijuca.
The region is served by several bus lines, as well as being easily accessible by fast-moving roads and large corridors. The building has a unique view from Lagoa Rodrigo de Freitas (Lagoon), and is close to banks, nice restaurants, hotels, botanic garden, bike lanes, acquatic sports and public transportation. It offers:
Fully equipped offices, ready to start working
Coworking area and business lounges fully equipped
Meeting rooms
Regus IT high speed internet access
Administrative support on request
Major transport link with major bus lines
Break-out areas
Concierge services at the reception of the building
Major public transportations from main city hubs
Secure parking with valet service
Fiji is an open-source image processing package that bundles ImageJ (an image processing program for scientific multidimensional images) and a number of plugins for scientific image analysis.
In this instructor-led, live training, participants will learn how to use the Fiji distribution and its underlying ImageJ program to create an image analysis application.
By the end of this training, participants will be able to:
Use Fiji's advanced programming features and software components to extend ImageJ
Stitch large 3d images from overlapping tiles
Automatically update a Fiji installation on startup using the integrated update system
Select from a broad selection of scripting languages to build custom image analysis solutions
Use Fiji's powerful libraries, such as ImgLib on large bioimage datasets
Deploy their application and collaborate with other scientists on similar projects
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
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.
PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu.
In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.
By the end of this training, participants will be able to:
Set up and configure PaddlePaddle
Set up a Convolutional Neural Network (CNN) for image recognition and object detection
Set up a Recurrent Neural Network (RNN) for sentiment analysis
Set up deep learning on recommendation systems to help users find answers
Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.
Format of the Course
This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
Scilab is a well-developed, free, and open-source high-level language for scientific data manipulation. Used for statistics, graphics and animation, simulation, signal processing, physics, optimization, and more, its central data structure is the matrix, simplifying many types of problems compared to alternatives such as FORTRAN and C derivatives. It is compatible with languages such as C, Java, and Python, making it suitable as for use as a supplement to existing systems.
In this instructor-led training, participants will learn the advantages of Scilab compared to alternatives like Matlab, the basics of the Scilab syntax as well as some advanced functions, and interface with other widely used languages, depending on demand. The course will conclude with a brief project focusing on image processing.
By the end of this training, participants will have a grasp of the basic functions and some advanced functions of Scilab, and have the resources to continue expanding their knowledge.
Audience
Data scientists and engineers, especially with interest in image processing and facial recognition
Format of the course
Part lecture, part discussion, exercises and intensive hands-on practice, with a final project
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Testimonials (1)
The quantity of exercises performed. Help from the trainer on each problem encountered during the exercises. He clarifies the process for us without giving the result.
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