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.
This instructor-led, live training in 台灣 (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.
This instructor-led, live training in 台灣 (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.
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
This instructor-led, live training in 台灣 (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.