ntp3105 / Computer-Vision

This repo holds my knowledge, resources gathered and projects done so far in Computer vision domain. It includes opencv, image generation processing, deep learning in Vision models.

Computer-Vision

This repo holds my knowledge, resources gathered and projects done so far in Computer vision domain. It includes opencv, image generation processing, deep learning in Vision models.

Computer vision is a field of artificial intelligence and computer science that focuses on enabling computers to interpret and understand visual information from the real world. It involves developing algorithms and techniques that allow computers to analyze and process digital images and videos, extract meaningful insights, and make decisions based on visual input.

Key aspects of computer vision include:

1. Image Processing: Manipulating and enhancing digital images to extract useful information or to improve their quality. This may involve techniques such as filtering, edge detection, and feature extraction.

2. Object Detection and Recognition: Identifying and localizing objects within an image or video stream. This can range from simple tasks such as detecting faces or text to more complex tasks such as recognizing specific objects or scenes.

3. Image Classification: Categorizing images into predefined classes or categories based on their visual content. This is commonly used in applications such as content-based image retrieval and automatic image tagging.

4. Semantic Segmentation: Partitioning an image into regions or segments and assigning semantic labels to each segment. This allows for fine-grained understanding of the contents of an image, such as identifying individual objects and their boundaries.

5. 3D Reconstruction: Estimating the three-dimensional structure of objects or scenes from one or more images or video frames. This is useful for applications such as virtual reality, augmented reality, and robotics.

6. Motion Analysis: Analyzing the movement of objects or the camera itself within a video sequence. This includes tasks such as object tracking, optical flow estimation, and activity recognition.

Computer vision has a wide range of applications across various industries, including healthcare (medical image analysis), automotive (autonomous driving), surveillance and security, agriculture, retail, and entertainment. Advances in deep learning and convolutional neural networks (CNNs) have significantly advanced the capabilities of computer vision systems, enabling them to achieve human-level performance on many visual recognition tasks.

The curriculum of a computer vision course typically covers a range of topics related to the theory, algorithms, and applications of computer vision. While specific courses may vary in their content and depth, here is a general outline of the curriculum for a computer vision course:

1. Introduction to Computer Vision:

   • Overview of computer vision and its applications.
   • History and evolution of computer vision.
   • Basic concepts and terminology in computer vision.

2. Image Formation and Representation:

   • Image formation process.
   • Digital image representation (pixels, color models).
   • Image enhancement and preprocessing techniques.

3. Image Filtering and Feature Extraction:

   • Spatial domain filtering (convolution, Gaussian filtering).
   • Frequency domain filtering (Fourier transform).
   • Edge detection (Sobel, Canny edge detector).
   • Corner detection (Harris corner detector).
   • Blob detection (Laplacian of Gaussian).

4. Image Segmentation:

   • Thresholding methods.
   • Region-based segmentation (watershed algorithm).
   • Contour detection and extraction.

5. Object Detection and Recognition:

   • Object detection techniques (Haar cascades, HOG).
   • Local feature detection and description (SIFT, SURF, ORB).
   • Deep learning-based object detection (YOLO, SSD).

6. Multiple View Geometry and 3D Reconstruction:

   • Camera models and calibration.
   • Epipolar geometry and stereo vision.
   • Structure from motion (SFM) and 3D reconstruction.

7. Motion Analysis and Tracking:

   • Optical flow estimation.
   • Object tracking algorithms (Kalman filter, MeanShift, CAMShift).

8. Deep Learning for Computer Vision:

   • Convolutional Neural Networks (CNNs).
   • Transfer learning and fine-tuning pre-trained models.
   • Object recognition, segmentation, and detection using CNNs.

9. Applications of Computer Vision:

   • Image classification and retrieval.
   • Face detection and recognition.
   • Biomedical image analysis.
   • Autonomous vehicles and robotics.
   • Augmented reality and virtual reality.

10. Hands-on Projects and Practical Applications:

    • Implementing computer vision algorithms using programming languages such as Python and libraries like OpenCV.
    • Working on real-world computer vision projects and applications.
    • Experimenting with datasets and evaluating performance metrics.

This curriculum provides a comprehensive overview of the fundamental concepts, techniques, and applications of computer vision, equipping students with the knowledge and skills to tackle a wide range of challenges in the field. Depending on the course level (introductory, intermediate, advanced) and the institution offering the course, the depth and complexity of each topic may vary.