Artificial Intelligence Advance | Computer Vision and Image Intelligence by Diljeet Singh | Learn Smarter
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Computer Vision and Image Intelligence

Advanced techniques in computer vision empower machines to understand visual data through methods like deep learning in image classification and object detection. This chapter outlines various core tasks such as image segmentation and generation, along with their applications in real-world scenarios. It emphasizes the significance of convolutional neural networks and transfer learning in driving innovation across diverse fields, including healthcare and security.

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Sections

  • 1

    Overview Of Computer Vision Tasks

    This section introduces the fundamental tasks in computer vision, including image classification, object detection, image segmentation, and image generation.

    Learning Practice

  • 1.1

    Task Description

    This section outlines various core tasks in computer vision, including image classification, object detection, segmentation, and image generation.

    Learning Practice

  • 1.1.1

    Image Classification

    This section covers the fundamentals of image classification in computer vision, highlighting its definition, significance, and the techniques used to implement it.

    Learning Practice

  • 1.1.2

    Object Detection

    Object detection enables the identification and localization of multiple objects within an image using advanced algorithms.

    Learning Practice

  • 1.1.3

    Image Segmentation

    Image segmentation is a crucial computer vision task that involves classifying each pixel in an image to identify and differentiate various objects and backgrounds.

    Learning Practice

  • 1.1.4

    Image Generation

    This section covers the concept of image generation using advanced techniques like GANs and diffusion models.

    Learning Practice

  • 2

    Deep Learning For Image Classification

    This section covers the fundamentals of using deep learning, particularly Convolutional Neural Networks (CNNs), for image classification tasks.

    Learning Practice

  • 2.1

    Key Concepts

    This section covers the foundational concepts of deep learning and key techniques used in computer vision.

    Learning Practice

  • 2.2

    Popular Datasets

    This section introduces the most widely-used datasets in computer vision tasks, emphasizing their importance in training and evaluating models.

    Learning Practice

  • 3

    Object Detection And Localization

    This section covers various algorithms and methodologies utilized in object detection and localization within images.

    Learning Practice

  • 3.1

    Algorithm Use

    This section discusses various algorithms used in computer vision for tasks such as object detection and localization.

    Learning Practice

  • 3.1.1

    R-Cnn / Fast R-Cnn

    R-CNN and Fast R-CNN are pivotal algorithms for object detection, combining region proposals with classification.

    Learning Practice

  • 3.1.2

    Yolo

    YOLO (You Only Look Once) is a popular algorithm for real-time object detection in computer vision.

    Learning Practice

  • 3.1.3

    Ssd

    The SSD (Single Shot Detector) is an efficient object detection framework that allows for fast and accurate detection of multiple objects in images.

    Learning Practice

  • 3.1.4

    Faster R-Cnn

    Faster R-CNN is an advanced object detection framework that combines region proposal networks with convolutional neural networks for efficient object detection.

    Learning Practice

  • 4

    Image Segmentation

    Image segmentation involves classifying each pixel in an image into distinct object categories.

    Learning Practice

  • 4.1

    Semantic Segmentation

    Semantic segmentation classifies each pixel of an image into different categories, enhancing the understanding of visual data.

    Learning Practice

  • 4.2

    Instance Segmentation

    Instance segmentation focuses on identifying and differentiating individual objects within an image, elevating the tasks of traditional image segmentation.

    Learning Practice

  • 4.3

    Popular Models

    This section explores various prevalent models used in image segmentation, focusing on their unique functionalities and applications.

    Learning Practice

  • 5

    Image Generation And Enhancement

    This section covers advanced techniques in image generation and enhancement, highlighting GANs, style transfer, super resolution, and diffusion models.

    Learning Practice

  • 5.1

    Gans

    GANs (Generative Adversarial Networks) are a class of deep learning models designed to generate realistic images from random noise.

    Learning Practice

  • 5.2

    Style Transfer

    Style Transfer is a technique that applies the visual appearance of one image to the content of another image, combining artistic styles with content.

    Learning Practice

  • 5.3

    Super Resolution

    Super Resolution techniques enhance image quality by increasing resolution and detail.

    Learning Practice

  • 5.4

    Diffusion Models

    Diffusion models are a cutting-edge technique in image generation that utilize stepwise processes to transform input noise or text into realistic images.

    Learning Practice

  • 6

    Tools, Libraries, And Frameworks

    This section highlights essential tools, libraries, and frameworks crucial for implementing computer vision tasks.

    Learning Practice

  • 6.1

    Opencv

    OpenCV is a widely used library for computer vision applications focusing on image processing techniques.

    Learning Practice

  • 6.2

    Tensorflow / Pytorch

    This section focuses on the prominent deep learning libraries TensorFlow and PyTorch, outlining their functionalities and typical use cases.

    Learning Practice

  • 6.3

    Detectron2, Mmdetection

    This section introduces Detectron2 and MMDetection, two powerful libraries for object detection tasks.

    Learning Practice

  • 6.4

    Labelimg, Roboflow

    LabelImg and Roboflow are powerful tools used for annotating images in machine learning workflows, facilitating the training of computer vision models.

    Learning Practice

  • 7

    Real-World Applications

    This section discusses the practical applications of computer vision across various industries.

    Learning Practice

  • 7.1

    Healthcare

    This section discusses the transformative role of computer vision in healthcare, particularly its applications in medical diagnostics.

    Learning Practice

  • 7.2

    Autonomous Vehicles

    This section covers the role of computer vision in enabling autonomous vehicles to navigate and interpret their environment, focusing on key technologies and applications.

    Learning Practice

  • 7.3

    Retail

    This section addresses the integration of computer vision in retail settings, emphasizing its applications in automated checkout and shelf monitoring.

    Learning Practice

  • 7.4

    Security

    This section highlights the applications of computer vision in security, focusing on facial recognition and surveillance analytics.

    Learning Practice

  • 7.5

    Agriculture

    This section discusses the application of computer vision in agriculture, highlighting technologies for crop monitoring and pest detection.

    Learning Practice

  • 8

    Chapter Summary

    This section summarizes the key concepts of Computer Vision, focusing on its applications and the significance of advanced techniques.

    Learning Practice

  • 8.1

    Summary Points

    Learning

References

Chapter 5_ Computer Vision and Image Intelligence.pdf

Class Notes

Memorization

What we have learnt

  • Computer vision enables mac...
  • CNNs are the backbone of mo...
  • Object detection and segmen...

Final Test

Revision Tests