Computer Vision

Computer vision, a fascinating subfield of artificial intelligence, focuses on enabling computers to understand and interpret visual information. In our IT courses, we delve into the principles and techniques that underpin computer vision. Students learn about image processing, feature extraction, object detection and recognition, image segmentation, and more. They explore algorithms and models such as convolutional neural networks (CNNs) and deep learning architectures to tackle complex visual tasks. Computer vision has diverse applications across industries, including autonomous vehicles, medical imaging, surveillance systems, augmented reality, and facial recognition. By studying computer vision in our IT courses, students gain the knowledge and skills to develop innovative solutions and contribute to this rapidly advancing field.

Skills You Will Gain

Image Processing

CNN

Image Classification

Object Detection

Object Tracking

Image Segmantation

Image Generation (Gen AI)

Pytorch

Video Analytics

This course includes

Syllabus Overview

  • Introduction to Python:
    • Overview of Python and its key features.
    • Installation and setup of Python and development environment.
    • Basics of running Python scripts and interactive mode.
  • Python Syntax and Data Types:
    • Variables, data types (numbers, strings, lists, tuples, dictionaries), and type conversion.
    • Operators (arithmetic, assignment, comparison, logical) and expressions.
    • Control flow statements: if-else, loops (for, while), and conditional expressions.
  • Functions and Modules:
    • Defining and using functions, parameters, and return values.
    • Built-in functions and modules, importing and using external modules.
    • Scope and namespaces.Introduction to Python:
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  • Introduction to Image Processing and Pillow:
    • Overview of image processing and its applications in computer vision.
    • Introduction to the Pillow library for image manipulation and processing.
    • Installing and setting up Pillow in Python.
    • Basics of reading, displaying, and saving images with Pillow.
  • Image Filtering and Enhancement:
    • Applying various image filters (e.g., blur, sharpen, edge detection) using Pillow.
    • Enhancing image quality through techniques like contrast adjustment and histogram equalization.
    • Removing noise from images using denoising algorithms.
    • Converting images to different color spaces (e.g., grayscale, RGB, HSV).
  • Image Transformation and Geometric Operations:
    • Resizing, cropping, and rotating images with Pillow.
    • Performing perspective transforms and affine transformations.
    • Image warping and morphing techniques.
    • Applying geometric operations like translation and scaling.
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  • Introduction to Image Processing and Computer Vision:

    • Overview of image processing and computer vision and their applications.
    • Introduction to the OpenCV and Pillow libraries for image manipulation and processing.
    • Installing and setting up OpenCV and Pillow in Python.
    • Basics of reading, displaying, and saving images with OpenCV and Pillow.
  • Image Filtering and Enhancement with OpenCV and Pillow:

    • Applying various image filters (e.g., blur, sharpen, edge detection) using OpenCV and Pillow.
    • Enhancing image quality through techniques like contrast adjustment and histogram equalization.
    • Removing noise from images using denoising algorithms in OpenCV and Pillow.
    • Converting images to different color spaces using OpenCV and Pillow.
  • Image Transformation and Geometric Operations with OpenCV and Pillow:

    • Resizing, cropping, and rotating images with OpenCV and Pillow.
    • Performing perspective transforms and affine transformations using OpenCV and Pillow.
    • Applying geometric operations like translation and scaling with OpenCV and Pillow.
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  • Introduction to Neural Networks:

    • Overview of neural networks and their applications in computer vision.
    • Understanding the basic structure of neural networks: neurons, layers, and activations.
    • Introduction to feedforward neural networks and their learning process.
    • Basics of gradient descent and backpropagation algorithms.
  • Convolutional Neural Networks (CNNs):

    • Understanding the architecture and components of CNNs.
    • Convolutional layers, pooling layers, and activation functions in CNNs.
    • Implementing convolutional operations and pooling in CNNs.
    • Training and fine-tuning CNNs for image classification tasks.
  • Transfer Learning and Pre-trained Models:

    • Introduction to transfer learning and its benefits in computer vision.
    • Utilizing pre-trained CNN models (e.g., VGG, ResNet, Inception) for image classification.
    • Fine-tuning pre-trained models on specific datasets.
    • Extracting and using features from pre-trained models for other computer vision tasks.
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  • Introduction to Convolutional Neural Networks:

    • Overview of CNNs and their applications in computer vision.
    • Understanding the key components of a CNN: convolutional layers, pooling layers, and activation functions.
    • Introduction to filter kernels and their role in feature extraction.
    • Basics of forward propagation in CNNs.
  • Convolutional Operations in CNNs:

    • Understanding the convolutional operation and its purpose in CNNs.
    • Implementing convolutional operations using matrix multiplication or element-wise multiplication.
    • Applying different padding techniques (e.g., zero-padding, valid-padding) and their impact on output size.
    • Exploring the concept of stride and its effect on feature map dimensions.
  • Pooling Operations in CNNs:

    • Understanding pooling layers and their role in down-sampling.
    • Implementing max pooling and average pooling operations.
    • Exploring different pool sizes and their impact on feature maps.
    • Handling overlapping and non-overlapping pooling regions.
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    1. Introduction to Convolutional Neural Networks:

      • Overview of CNNs and their applications in computer vision.
      • Understanding the key components of a CNN: convolutional layers, pooling layers, and activation functions.
      • Introduction to filter kernels and their role in feature extraction.
      • Basics of forward propagation in CNNs.
    2. Convolutional Operations in CNNs:

      • Understanding the convolutional operation and its purpose in CNNs.
      • Implementing convolutional operations using matrix multiplication or element-wise multiplication.
      • Applying different padding techniques (e.g., zero-padding, valid-padding) and their impact on output size.
      • Exploring the concept of stride and its effect on feature map dimensions.
    3. Pooling Operations in CNNs:

      • Understanding pooling layers and their role in down-sampling.
      • Implementing max pooling and average pooling operations.
      • Exploring different pool sizes and their impact on feature maps.
      • Handling overlapping and non-overlapping pooling regions.
  • Introduction to Advanced Image Classification Architectures
    Residual Networks (ResNet)
    • Understanding skip connections
    • Benefits of deep residual networks
    • Hands-on implementation of ResNet

    Inception Networks (GoogLeNet)

    • Motivation for inception modules
    • Inception v1, v2, v3, and v4 architectures
    • Implementation of InceptionNet
    DenseNet
    • Dense connectivity in networks
    • Advantages of DenseNet
    • Building and training DenseNet models
    MobileNets
    • Lightweight neural networks for mobile devices
    • Depthwise separable convolutions
    • Implementing MobileNets
  • Introduction to Object Detection
  • Two-Stage Object Detection
    • Region Proposal Networks (R-CNN)
    • Fast R-CNN and Faster R-CNN
    • Implementing Faster R-CNN for object detection
  • Single-Stage Object Detection
    • You Only Look Once (YOLO)
    • Single Shot MultiBox Detector (SSD)
    • Implementing YOLO or SSD for real-time object detection
  • Transfer Learning for Object Detection
    • Fine-tuning pre-trained models for custom datasets
    • Data augmentation strategies
  • Evaluation Metrics for Object Detection
    • Intersection over Union (IoU)
    • Average Precision (AP)
    • Non-maximum Suppression (NMS)
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  • Introduction to GANs
  • GAN Architecture
    • Generator and Discriminator networks
    • Training process and adversarial loss
    • Variants of GANs (DCGAN, WGAN, CGAN)
  • Applications of GANs
    • Image Generation with DCGAN
    • Conditional GANs for image manipulation
    • StyleGAN for high-quality image synthesis
  • Challenges and Ethical Considerations in GANs
    • Mode collapse and instability
    • Ethical concerns in deepfake generation
  • Hands-on GAN Implementation
    • Building and training a GAN model
    • Generating images with your GAN
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    • Introduction to Image Segmentation
    • Types of Image Segmentation
      • Semantic Segmentation
      • Instance Segmentation
      • Panoptic Segmentation
    • U-Net Architecture
      • Architecture and skip connections
      • Applications in biomedical image segmentation
      • Implementing U-Net for image segmentation
    • Mask R-CNN for Instance Segmentation
      • Combining object detection and segmentation
      • Training and inference with Mask R-CNN
    • Evaluation Metrics for Image Segmentation
      • Intersection over Union (IoU)
      • Mean IoU (mIoU)
    • Real-world Applications of Image Segmentation
      • Medical image analysis
      • Autonomous driving and robotics

Transform Your Skills: Enroll Now to Learn AI

Our IT courses provide a comprehensive and immersive learning experience for individuals aspiring to master the field of data science. Through a carefully designed curriculum, students are equipped with the necessary knowledge and practical skills to effectively analyze and extract valuable insights from large and complex datasets. Our courses cover a wide range of topics, including statistics, programming, machine learning, data visualization, and data mining. Students will have hands-on experience with popular tools and technologies used in the industry, such as Python, R, SQL, and Hadoop, enabling them to manipulate, clean, and analyze data efficiently. Furthermore, our experienced instructors and industry experts guide students through real-world case studies and projects, allowing them to apply their learning to solve practical problems. By the end of the program, students will be well-prepared to tackle data science challenges, make data-driven decisions, and contribute meaningfully to the rapidly evolving field of data analytics.

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