Learn Computer Vision for image representation, feature engineering, image preprocessing, analysis, application & trend
Description
Take the next step in your career as Computer Vision professionals! Whether you’re an up-and-coming computer vision engineer, an experienced image analyst, aspiring machine learning specialist in computer vision, or budding AI researcher in visual technology, this course is an opportunity to sharpen your image processing and analytical capabilities, increase your efficiency for professional growth, and make a positive and lasting impact in the field of Computer Vision.
With this course as your guide, you learn how to:
● All the fundamental functions and skills required for Computer Vision.
● Transform knowledge of Computer Vision applications and techniques, image representation and feature engineering, image analysis and preprocessing, object detection and image segmentation.
● Get access to recommended templates and formats for details related to Computer Vision applications and techniques.
● Learn from informative case studies, gaining insights into Computer Vision applications and techniques for various scenarios. Understand how the International Monetary Fund, monetary policy, and fiscal policy impact advancements in Computer Vision, with practical forms and frameworks.
● Learn from informative case studies, gaining insights into Computer Vision applications and techniques for various scenarios. Understand how the International Monetary Fund, monetary policy, and fiscal policy impact advancements in Computer Vision, with practical forms and frameworks.
The Frameworks of the Course
Engaging video lectures, case studies, assessments, downloadable resources, and interactive exercises. This course is designed to explore the field of Computer Vision, covering various chapters and units. You'll delve into image representation, feature engineering, image classification, object detection, image segmentation, image preprocessing, image analysis, image recognition, image generation, image captioning, visual question answering, advanced Computer Vision topics, and future trends.
The socio-cultural environment module using Computer Vision techniques delves into sentiment analysis and opinion mining, image captioning and visual question answering, and object detection and image segmentation in the context of India's socio-cultural landscape. It also applies Computer Vision to explore image preprocessing and analysis, image recognition, object detection, image segmentation, and advanced topics in Computer Vision. You'll gain insight into Computer Vision-driven analysis of sentiment analysis and opinion mining, image captioning and visual question answering, and object detection and image segmentation. Furthermore, the content discusses Computer Vision-based insights into Computer Vision applications and future trends, along with a capstone project in Computer Vision.
The course includes multiple global Computer Vision projects, resources like formats, templates, worksheets, reading materials, quizzes, self-assessment, film study, and assignments to nurture and upgrade your global Computer Vision knowledge in detail.
Course Content:
Part 1
Introduction and Study Plan
● Introduction and know your Instructor
● Study Plan and Structure of the Course
1. Introduction to Computer Vision
1.1.1 Overview of Computer Vision
1.1.2 Key Components of Computer Vision
1.2.3 Pattern Recognition
1.1.4 Technique and Algorithms
1.1.5 Challenges in Computer Vision
1.1.6 Basic of Image Processing with Python
1.1.7 Key Libraries for image processing in Python
1.1.8 Basic Image Operation
1.1.8 Continuation of Basic Image Operation
1.1.8 Continuation of Basic Image Operation
2. Image Representation and Feature Extraction
2.1.1 Image Representation and Feature Extraction
2.1.1 Continuation of image Representation and Feature Extraction
2.1.2 Corner Detection
2.1.3 HOG(Histogram of Oriented Gradients)
3. Image Segmentation
3.1.1 Image Segmentation
3.1.2 Types of image Segmentation
3.1.3 Technique and Implementations
3.1.4 K-Means Clustering
3.1.5 Watershed Algorithm
3.1.6 Summary
4. Object Detection
4.1.1 Object Detection
4.1.2 Key Concepts in Object Detection
4.1.3 Implementing Object Detection with Pre trained Models
4.1.4 YOLO(You only Look Once)
4.1.5 Faster R-CNN with TensorFlow
4.1.6 Summary
5. Image Classification
5.1.1 Image Classification
5.1.2 Key Components in image Classification
5.1.3 Implementing image Classification
5.1.4 Deep learning Methods
6. Image Recognition and Scene Understanding
6.1.1 Image Recognition and Scene Understanding
6.1.2 Key Concepts
6.1.3 Implementations
6.1.4 Scene Understanding with Semantic Segmentation
6.1.5 Instance Segmentation with Mask R-CNN
6.1.6 Scene Classification with RNN and CNN
6.1.6 Continuation of Scene Classification with RNN and CNN
7. Object Tracking
7.1.1 Object Tracking
7.1.2 Key Concepts
7.1.3 KLT Tracker with OpenCV
7.1.4 Deep SORT with YAOLOv4 for Detection
8. Image Generation and Image-to-Image Translation
8.1.1 Image Generation and image to Image Translation
8.1.2 key concepts
8.1.3 Implementations
8.1.4 Image to Image Translation with Pix2Pix
8.1.5 Cycle gan for Unpaired Image to Image Translation
8.1.5 Continuation of Cycle gan for Unpaired Image to Image Translation
9. Advanced Topics in Computer Vision
9.1.1 Advanced Topics in Computer Vision
9.1.1 Continuation of Advanced Topics in Computer Vision
9.1.1 Continuation of Advanced Topics in Computer Vision
10. Computer Vision Applications and Future Trends
10.1.1 Computer Vision Applications and Future Trends
10.1.2 Application
10.1.3 Future Trends
10.1.3 Continuation of Future Trends
11. Capstone Project
11.1.1 Capstone Project
11.1.2 Project Title Real-world Object Detection and Classification System
11.1.3 Project Tasks
11.1.3 Continuation of project Tasks
11.1.4 Project Deliverables
11.1.5 Project Evaluation
11.1.6 Conclusion
Part 3
Assignments
Debes tener en cuenta que los cupones duran maximo 4 dias o hasta agotar 1000 inscripciones,pero puede vencer en cualquier momento. Obten el curso con cupon haciendo clic en el siguiente boton:
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