INTRODUCTION TO COMPUTER VISION BASICS.pptx

Certainly! Computer vision (CV) is a field of artificial intelligence (AI) and computer science that enables machines to interpret and understand the visual world through digital images or videos. It seeks to replicate human vision capabilities, enabling computers to extract, process, and analyze information from visual data like photographs or videos.

### Key Concepts in Computer Vision

1. **Image Processing:** Fundamental to CV, image processing involves manipulating images to enhance them for analysis. Techniques include filtering, noise reduction, and image enhancement.

2. **Feature Detection and Description:** CV algorithms identify key points, edges, or corners in images (features) and describe them mathematically. This enables pattern recognition and comparison.

3. **Object Recognition:** CV algorithms classify objects in images, often using deep learning models like Convolutional Neural Networks (CNNs). These models learn hierarchical representations of features.

4. **Segmentation:** This divides images into meaningful regions or segments to simplify analysis. Techniques include semantic segmentation (labeling each pixel) and instance segmentation (identifying each object instance).

5. **Motion Analysis:** CV tracks object movements in videos, enabling applications like surveillance, action recognition, and video summarization.

6. **3D Reconstruction:** It involves reconstructing the three-dimensional structure of objects or scenes from multiple 2D images or video frames. Applications include virtual reality (VR), augmented reality (AR), and robotics.

7. **Applications:** CV finds application in various fields:
- **Healthcare:** Medical imaging for diagnosis and treatment planning.
- **Automotive:** Autonomous vehicles use CV for object detection and navigation.
- **Retail:** CV enables facial recognition for security and customer analytics.
- **Entertainment:** Augmented reality (AR) and virtual reality (VR) applications.
- **Manufacturing:** Quality control and defect detection in production lines.

### Techniques and Algorithms in Computer Vision

1. **Deep Learning:** CNNs dominate CV due to their ability to learn hierarchical representations of features from data. Architectures like ResNet, VGG, and MobileNet are popular for different applications.

2. **Feature Extraction:** Methods like Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) detect and describe distinctive features for matching and recognition.

3. **Object Detection:** Algorithms like R-CNN, Fast R-CNN, and YOLO (You Only Look Once) detect and localize objects within images, crucial for applications like autonomous driving and surveillance.

4. **Semantic Segmentation:** Techniques like U-Net and Mask R-CNN segment images into different classes or objects, enabling detailed understanding of image content.

5. **Optical Flow:** This tracks object movement frame by frame in video sequences, vital for action recognition a

1. UNIT-2 Image-to-Image Mapping In this presentation, we will explore the world of image-to-image mapping. This technique allows us to manipulate images in various ways, such as stitching panoramas, correcting distortions, and creating special effects. We will delve into three key subtopics: homographies , warping images, and creating panoramas

2. Homographies 2 Homography : A mathematical transformation that maps corresponding points between two images. Useful for tasks like image registration and stitching panoramas. Assumes a planar relationship between the images. Homography is a fundamental concept in image-to-image mapping. It allows us to establish a relationship between two images by mapping corresponding points. This transformation assumes a planar relationship, meaning the objects in the images are roughly flat. Homography plays a crucial role in tasks like image registration, where we align multiple images, and stitching panoramas, where we combine multiple images into a wider view

3. Warping Images Warping: The process of manipulating an image by applying a geometric transformation. Homography can be used to warp images for various purposes. Can be used for correcting distortions, creating special effects, and image registration.

4. Image warping is the process of applying a geometric transformation to manipulate an image. Homography , which we discussed in the previous slide, is a powerful tool for warping images. We can use warping for various purposes, such as correcting distortions caused by camera lenses, creating special effects like morphing or bending objects, and image registration, where we align images that might be slightly skewed or rotated

5. Creating Panoramas 5 Panoramas: Images that capture a wider field of view than a single camera image can provide. Created by stitching together multiple overlapping images. Homography plays a crucial role in aligning and warping images for seamless stitching. Panoramas are a fantastic way to capture breathtaking landscapes or expansive scenes. They are created by stitching together multiple overlapping images. Homography plays a vital role in this process. We use homography to estimate the transformation between the images, allowing for proper alignment and warping before seamlessly stitching them together to create a stunning panorama.

6. Models and Augmented Reality Models and augmented reality (AR) converge. We will delve into the fundamental concepts behind camera models, calibration, pose estimation, and how they all come together to create the magic of AR. Buckle up and get ready to see the real world enhanced by the power of 3D models

7. Pinhole Camera Model A simplified mathematical model that represents how acamera captures an image Light rays travel through a small opening (pinhole) and project an inverted image onto a flat plane (sensor) Explains the relationship between 3D world points and theircorresponding 2D image points

8. The pinhole camera model is the foundation of understanding how cameras work. Imagine a dark box with a tiny hole in one side. Light rays from the outside world pass through the hole and project an inverted image of the scene onto the opposite wall. This is essentially how a camera captures an image. The pinhole camera model helps us understand the mathematical relationship between points in the 3D world and their corresponding locations in the 2D image captured by the camera.

9. Camera Calibration The process of correcting for distortions and imperfections in a camera lens Improves the accuracy of measurements made from camera images Calibration patterns like checkerboards or circles are used to estimate intrinsic camera parameters Intrinsic parameters include focal length, principal point, and distortion coefficients

10. Camera lenses are not perfect. They can introduce distortions like barrel or pincushion effect, which can affect the accuracy of measurements made from camera images. Camera calibration is the process of correcting for these distortions. We use special calibration patterns, like checkerboards or circles, to capture images from different viewpoints. By analyzing these images, we can estimate the intrinsic parameters of the camera, such as focal length, principal point, and distortion coefficients. These parameters are then used to "un-distort" the image and improve its accuracy.

11. Pose Estimation from Planes and Markers Estimating the pose (position and orientation) of a camera or object in 3D space Used to track the movement of a camera or project virtual objects onto real-world surfaces Can be achieved using plane detection algorithms or by identifying known markers in the scene Plane detection algorithms identify flat surfaces in the camera image Marker-based tracking uses unique patterns (e.g., QR codes) to determine the camera pose

12. Pose estimation is a crucial step in AR applications. It allows us to determine the precise position and orientation of a camera or object in 3D space. This information is essential for tracking camera movement and for accurately placing virtual objects onto real-world surfaces. There are two main approaches for pose estimation: plane detection and marker-based tracking. Plane detection algorithms analyze the camera image to identify flat surfaces like walls, floors, or tables. Marker-based tracking, on the other hand, relies on detecting pre-defined markers (like QR codes) in the scene. These markers act as reference points that allow the system to determine the camera pose relative to their known location and orientation.

13. Augmented Reality 13 Technology that superimposes computer-generated information onto the user's view of the real world Combines the real and virtual worlds in a seamless and interactive experience Widely used in various applications like gaming, education, retail, and manufacturing AR can be implemented through various devices like smartphones, tablets, and dedicated AR headsets

14. Augmented reality (AR) is a technology that seamlessly blends the real and virtual worlds. It overlays computer-generated information onto the user's view of the real world, creating an interactive and immersive experience. Imagine seeing furniture virtually placed in your living room before you buy it, or viewing historical landmarks come alive with interactive 3D models on your smartphone