Select Your Favourite
Category And Start Learning.

( 0 Review )

Computer Vision Crash Course


( 0 Review )

Course Level


Total Hour


Video Tutorials


Course content


Month 1: Foundations of Computer Vision

Introduction to Computer Vision and its applications
Image Processing Basics (filters, transformations, and enhancements)
Image Segmentation and Object Detection
Feature Extraction and Descriptors (SIFT, SURF, ORB)

Month 2: Deep Learning for Computer Vision

Month 3: Advanced Topics in Computer Vision

About Course

Computer vision refers to the field of study and application of algorithms and techniques that enable computers to interpret and understand visual information from images or videos. It involves processing and analyzing digital visual data to extract meaningful insights and make intelligent decisions. Computer vision encompasses a range of tasks, including image classification, object detection, facial recognition, image segmentation, and more. By mimicking human visual perception, computer vision technology enables machines to recognize and interpret visual content, leading to applications in various industries such as autonomous vehicles, robotics, surveillance, healthcare, and augmented reality.


What Will You Learn?

  • Introduction to Computer Vision: Understand the basics of computer vision, its applications, and its importance in various fields.
  • Image Processing: Learn techniques for image enhancement, noise reduction, and filtering to preprocess images for further analysis.
  • Image Representation and Feature Extraction: Explore methods to represent and extract relevant features from images, such as histograms, texture analysis, and edge detection.
  • Image Classification: Understand the fundamentals of image classification and learn algorithms like support vector machines (SVM) and deep learning-based approaches for classifying images into different categories.
  • Object Detection and Tracking: Study techniques for detecting and localizing objects within images or videos, including methods like Haar cascades, histogram of oriented gradients (HOG), and deep learning-based object detectors like Faster R-CNN and YOLO.
  • Image Segmentation: Learn algorithms for partitioning images into meaningful regions, such as thresholding, region-based segmentation, and advanced methods like U-Net and DeepLab for semantic segmentation.
  • Feature Matching and Correspondence: Explore techniques for finding correspondences between features in different images, including point-based matching, feature descriptors, and robust estimation methods like RANSAC.
  • 3D Computer Vision: Introduction to 3D reconstruction, stereo vision, and depth estimation techniques to understand the 3D structure of scenes from multiple images.
  • Deep Learning for Computer Vision: Gain knowledge of deep learning frameworks and architectures like convolutional neural networks (CNNs) for various computer vision tasks.
  • Applications of Computer Vision: Discover real-world applications of computer vision, including facial recognition, object tracking, augmented reality, autonomous vehicles, medical imaging, and surveillance systems.
  • Hands-on Projects: Work on practical projects to apply the concepts learned and gain hands-on experience in solving computer vision problems using programming languages and libraries like Python and OpenCV.
  • Ethical Considerations: Understand the ethical implications of computer vision technologies, including privacy concerns, biases in algorithms, and responsible use of AI-powered visual systems.

Material Includes

  • Live Classes
  • Assignments and projects


4.44 /5

78 Courses

Student Ratings & Reviews

No Review Yet
No Review Yet
20,999.00 40,000.00

Material Includes

  • Live Classes
  • Assignments and projects

Share Course
Page Link
Share On Social Media

Want to receive push notifications for all major on-site activities?