Overview

DescriptionTake 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 CourseEngaging 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 1Introduction and Study Plan● Introduction and know your Instructor● Study Plan and Structure of the Course1. Introduction to Computer Vision1.1.1 Overview of Computer Vision1.1.2 Key Components of Computer Vision1.2.3 Pattern Recognition1.1.4 Technique and Algorithms1.1.5 Challenges in Computer Vision1.1.6 Basic of Image Processing with Python1.1.7 Key Libraries for image processing in Python1.1.8 Basic Image Operation1.1.8 Continuation of Basic Image Operation1.1.8 Continuation of Basic Image Operation2. Image Representation and Feature Extraction2.1.1 Image Representation and Feature Extraction2.1.1 Continuation of image Representation and Feature Extraction2.1.2 Corner Detection2.1.3 HOG(Histogram of Oriented Gradients)3. Image Segmentation3.1.1 Image Segmentation3.1.2 Types of image Segmentation3.1.3 Technique and Implementations3.1.4 K-Means Clustering3.1.5 Watershed Algorithm3.1.6 Summary4. Object Detection4.1.1 Object Detection4.1.2 Key Concepts in Object Detection4.1.3 Implementing Object Detection with Pre trained Models4.1.4 YOLO(You only Look Once)4.1.5 Faster R-CNN with TensorFlow4.1.6 Summary5. Image Classification5.1.1 Image Classification5.1.2 Key Components in image Classification5.1.3 Implementing image Classification5.1.4 Deep learning Methods6. Image Recognition and Scene Understanding6.1.1 Image Recognition and Scene Understanding6.1.2 Key Concepts6.1.3 Implementations6.1.4 Scene Understanding with Semantic Segmentation6.1.5 Instance Segmentation with Mask R-CNN6.1.6 Scene Classification with RNN and CNN6.1.6 Continuation of Scene Classification with RNN and CNN7. Object Tracking7.1.1 Object Tracking7.1.2 Key Concepts7.1.3 KLT Tracker with OpenCV7.1.4 Deep SORT with YAOLOv4 for Detection8. Image Generation and Image-to-Image Translation8.1.1 Image Generation and image to Image Translation8.1.2 key concepts8.1.3 Implementations8.1.4 Image to Image Translation with Pix2Pix8.1.5 Cycle gan for Unpaired Image to Image Translation8.1.5 Continuation of Cycle gan for Unpaired Image to Image Translation9. Advanced Topics in Computer Vision9.1.1 Advanced Topics in Computer Vision9.1.1 Continuation of Advanced Topics in Computer Vision9.1.1 Continuation of Advanced Topics in Computer Vision10. Computer Vision Applications and Future Trends10.1.1 Computer Vision Applications and Future Trends10.1.2 Application10.1.3 Future Trends10.1.3 Continuation of Future Trends11. Capstone Project11.1.1 Capstone Project11.1.2 Project Title Real-world Object Detection and Classification System11.1.3 Project Tasks11.1.3 Continuation of project Tasks11.1.4 Project Deliverables11.1.5 Project Evaluation11.1.6 ConclusionPart 3Assignments

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