Back

Computer Vision

Computer Vision is an advancing discipline that merges artificial intelligence with image processing.

Explore courses
Register Now
Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Computer Vision is an advancing discipline that merges artificial intelligence with image processing.

  2. This course provides an overview of essential concepts, methodologies, and tools that empower computers to analyze and understand visual information.

  3. Participants will engage in practical exercises using leading libraries such as OpenCV, TensorFlow, and PyTorch, covering a range of topics from image recognition to object detection.

  4. The curriculum balances theoretical knowledge with hands-on experience, equipping learners to create practical applications in fields like healthcare, self-driving cars, and augmented reality.

  5. Key areas of focus include the fundamentals of image processing, techniques for feature extraction, methods for object detection and recognition, the role of deep learning in computer vision, and the development of real-world projects.

  6. This course is tailored for both beginners and those with intermediate skills who possess a foundation in programming and basic mathematics.

CERTIFICATION:

  1. Upon finishing the course, participants will be awarded a certificate of completion that highlights their expertise in various areas, including image processing and computer vision principles, practical application with OpenCV, TensorFlow, and PyTorch, and the creation of comprehensive computer vision solutions.

  2. This certification is recognized in the industry and can significantly improve career opportunities in sectors like artificial intelligence, robotics, and software development.

  3. To obtain the certification, participants must complete all course modules and quizzes, submit a capstone project that showcases their applied skills, and achieve a minimum score of 70% on the final assessment.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Grasp the essential concepts of computer vision and image processing.

  2. Utilize well-known libraries such as OpenCV, TensorFlow, and PyTorch to address computer vision challenges.

  3. Create algorithms for various tasks, including object detection, image classification, and facial recognition.

  4. Employ deep learning methods to improve computer vision applications.

  5. Conceive and execute practical projects in areas such as healthcare, autonomous systems, and augmented reality.

  6. Evaluate and enhance the performance of computer vision models.

  7. Remain informed about the latest advancements and technologies in the computer vision sector.

Enroll Now

Course Curriculum

Introduction to Computer Vision
  1. What is Computer Vision? Applications in industries
  2. Understanding how machines interpret visual data
  3. Difference between traditional computer vision and deep learning approaches
  4. Overview of image processing and object recognition
Image Fundamentals
  1. Understanding pixels, resolution, and color spaces (RGB, Grayscale, HSV)
  2. Image file formats and their characteristics
  3. Basic image transformations: Scaling, rotation, cropping, and resizing
  4. Image histograms and contrast adjustments
Image Processing Techniques
  1. Filtering techniques: Smoothing, sharpening, edge detection (Sobel, Canny)
  2. Morphological operations: Dilation, erosion, opening, and closing
  3. Thresholding techniques: Binary, adaptive, and Otsu’s thresholding
  4. Image segmentation: Region growing, clustering, and watershed algorithm
Feature Detection and Extraction
  1. Corner detection (Harris, FAST) and feature descriptors (SIFT, SURF, ORB)
  2. Contour detection and shape analysis
  3. Keypoint matching and object tracking
  4. Applications in object recognition and augmented reality
Working with OpenCV
  1. Introduction to OpenCV library and installation
  2. Loading, displaying, and saving images and videos
  3. Basic image manipulations using OpenCV functions
  4. Drawing shapes, adding text, and handling user inputs
Deep Learning for Computer Vision
  1. Introduction to convolutional neural networks (CNNs)
  2. Layers in CNN: Convolution, pooling, fully connected, and activation functions
  3. Pre-trained models: VGG, ResNet, MobileNet
  4. Transfer learning and fine-tuning for custom tasks
Object Detection and Recognition
  1. Techniques: Sliding window, YOLO, SSD, Faster R-CNN
  2. Real-time object detection using YOLO or SSD models
  3. Applications in face detection, license plate recognition, and more
Image Classification and Segmentation
  1. Image classification using CNNs
  2. Semantic segmentation using U-Net, DeepLab, or Mask R-CNN
  3. Instance segmentation vs semantic segmentation

Training Features

Hands-On Labs with OpenCV

Practical exercises using OpenCV for image processing, feature detection, and object recognition.

Deep Learning Implementation

Training convolutional neural networks using TensorFlow or PyTorch for various computer vision tasks.

Real-World Projects

End-to-end projects like facial recognition systems, autonomous vehicle vision modules, or object trackers.

Pre-Trained Models and Customization

Utilize pre-trained models for complex tasks and fine-tune them for specific datasets.

Access to Datasets

Work with popular datasets such as CIFAR-10, ImageNet, COCO, and custom datasets for hands-on practice.

Certification and Portfolio Building

Receive a certificate of completion and guidance on creating a portfolio showcasing your computer vision projects.

Get in Touch

    Our Relevant Courses list