A visão computacional é um campo que inclui métodos para adquirir, processar, analisar e entender imagens. Os cursos de treinamento ao vivo da Computer Vision demonstram, através da discussão interativa e prática prática, os fundamentos da Visão Computacional, à medida que os participantes passam pela criação de aplicativos simples de Visão Computacional. O treinamento em Visão Computacional está disponível em vários formatos, incluindo treinamento ao vivo no local e treinamento online ao vivo e interativo. O treinamento ao vivo no local pode ser realizado nas instalações do cliente no Brasil ou nos centros de treinamento locais NobleProg no Brasil. O treinamento ao vivo remoto é realizado por meio de uma área de trabalho remota e interativa.
NobleProg -- Seu Provedor de Treinamento Local
Declaração de Clientes
Eu realmente gostei da abordagem prática.
Kevin De Cuyper
Curso: Computer Vision with OpenCV
Machine Translated
O uso fácil da funcionalidade VideoCapture para adquirir imagens de vídeo da câmera do laptop.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Curso: Computer Vision with OpenCV
Machine Translated
Eu gostei das recomendações dadas pelo treinador sobre como usar as ferramentas. Isso é algo que não pode ser obtido da internet e é muito útil.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Curso: Computer Vision with OpenCV
Machine Translated
Eu gostei das recomendações dadas pelo treinador sobre como usar as ferramentas. Isso é algo que não pode ser obtido da internet e é muito útil.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Curso: Computer Vision with OpenCV
Machine Translated
Foi fácil de seguir.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Curso: Computer Vision with OpenCV
Machine Translated
O treinador era muito conhecedor e muito aberto ao feedback sobre qual ritmo passar pelo conteúdo e os tópicos que cobrimos. Ganhei muito com o treinamento e sinto que agora tenho uma boa compreensão da manipulação de imagem e algumas técnicas para construir um bom conjunto de treinamento para um problema de classificação de imagem.
Anthea King - WesCEF
Curso: Computer Vision with Python
Machine Translated
Nossos Clientes
.png "iberoamericana de hidrocarburos (ihsa)")
.jpg "european southern observatory (eso)")
_ireland.gif "environmental protection agency (epa) ireland")
Subcategorias Visão Computacional
Programas do curso Visão Computacional
Nome do Curso
Duração
Visão geral
Nome do Curso
Duração
Visão geral
14 horas
Visão geral
SimpleCV is an open source framework — meaning that it is a collection of libraries and software that you can use to develop vision applications. It lets you work with the images or video streams that come from webcams, Kinects, FireWire and IP cameras, or mobile phones. It’s helps you build software to make your various technologies not only see the world, but understand it too.
Audience
This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.
Audience
This course is directed at engineers and developers seeking to develop computer vision applications with SimpleCV.
21 horas
Visão geral
Caffe é uma framework de deep learning construída com os modelos de expressão, velocidade e modularidade em mente. Este curso explora a aplicação do Caffe como uma framework para o reconhecimento de imagem.
Depois de completar o curso os participantes poderão:
- Entender a estrutura do Caffe e os mecanismos de despacho
- Monitorar a qualidade do código, debugging etc...
Depois de completar o curso os participantes poderão:
- Entender a estrutura do Caffe e os mecanismos de despacho
- Monitorar a qualidade do código, debugging etc...
14 horas
Visão geral
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.
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.
14 horas
Visão geral
Computer Vision is a field that involves automatically extracting, analyzing, and understanding useful information from digital media. Python is a high-level programming language famous for its clear syntax and code readibility.
In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python.
By the end of this training, participants will be able to:
- Understand the basics of Computer Vision
- Use Python to implement Computer Vision tasks
- Build their own face, object, and motion detection systems
Audience
- Python programmers interested in Computer Vision
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python.
By the end of this training, participants will be able to:
- Understand the basics of Computer Vision
- Use Python to implement Computer Vision tasks
- Build their own face, object, and motion detection systems
Audience
- Python programmers interested in Computer Vision
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 horas
Visão geral
This instructor-led, live training in Brasil (online or onsite) is aimed at developers who wish to build a self-driving car using deep learning techniques.
By the end of this training, participants will be able to:
- Use Keras to build and train a convolutional neural network.
- Use computer vision techniques to identify lanes in an autonomos driving project.
- Train a deep learning model to differentiate traffic signs.
- Simulate a fully autonomous car.
By the end of this training, participants will be able to:
- Use Keras to build and train a convolutional neural network.
- Use computer vision techniques to identify lanes in an autonomos driving project.
- Train a deep learning model to differentiate traffic signs.
- Simulate a fully autonomous car.
14 horas
Visão geral
This instructor-led, live training in Brasil (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Build, train, and deploy deep learning models to analyze live video feeds.
- Identify, track, segment and predict different objects within video frames.
- Optimize object detection and tracking models.
- Deploy an intelligent video analytics (IVA) application.
By the end of this training, participants will be able to:
- Install and configure the necessary development environment, software and libraries to begin developing.
- Build, train, and deploy deep learning models to analyze live video feeds.
- Identify, track, segment and predict different objects within video frames.
- Optimize object detection and tracking models.
- Deploy an intelligent video analytics (IVA) application.
7 horas
Visão geral
This instructor-led, live training in Brasil (online or onsite) is aimed at back-end developers and data scientists who wish to incorporate pre-trained YOLO models into their enterprise-driven programs and implement cost-effective components for object-detection.
By the end of this training, participants will be able to:
- Install and configure the necessary tools and libraries required in object detection using YOLO.
- Customize Python command-line applications that operate based on YOLO pre-trained models.
- Implement the framework of pre-trained YOLO models for various computer vision projects.
- Convert existing datasets for object detection into YOLO format.
- Understand the fundamental concepts of the YOLO algorithm for computer vision and/or deep learning.
By the end of this training, participants will be able to:
- Install and configure the necessary tools and libraries required in object detection using YOLO.
- Customize Python command-line applications that operate based on YOLO pre-trained models.
- Implement the framework of pre-trained YOLO models for various computer vision projects.
- Convert existing datasets for object detection into YOLO format.
- Understand the fundamental concepts of the YOLO algorithm for computer vision and/or deep learning.
14 horas
Visão geral
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.
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.
28 horas
Visão geral
OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms.
Audience
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
Audience
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
21 horas
Visão geral
This instructor-led, live training introduces the software, hardware, and step-by-step process needed to build a facial recognition system from scratch. Facial Recognition is also known as Face Recognition.
The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc.
By the end of this training, participants will be able to:
- Install Linux, OpenCV and other software utilities and libraries on a Rasberry Pi.
- Configure OpenCV to capture and detect facial images.
- Understand the various options for packaging a Rasberry Pi system for use in real-world environments.
- Adapt the system for a variety of use cases, including surveillance, identity verification, etc.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- Other hardware and software options include: Arduino, OpenFace, Windows, etc. If you wish to use any of these, please contact us to arrange.
The hardware used in this lab includes Rasberry Pi, a camera module, servos (optional), etc. Participants are responsible for purchasing these components themselves. The software used includes OpenCV, Linux, Python, etc.
By the end of this training, participants will be able to:
- Install Linux, OpenCV and other software utilities and libraries on a Rasberry Pi.
- Configure OpenCV to capture and detect facial images.
- Understand the various options for packaging a Rasberry Pi system for use in real-world environments.
- Adapt the system for a variety of use cases, including surveillance, identity verification, etc.
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- Other hardware and software options include: Arduino, OpenFace, Windows, etc. If you wish to use any of these, please contact us to arrange.
14 horas
Visão geral
This instructor-led, live training in Brasil (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
Próximos Cursos de Visão Computacional
18-08-2021 09:30
21 horas
25-08-2021 09:30
21 horas
31-08-2021 09:30
14 horas
31-08-2021 09:30
14 horas
16-09-2021 09:30
14 horas
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