Computer Vision
A. What is Computer Vision (CV)?
Computer Vision is a field of Artificial Intelligence that enables computers to see, analyze, and understand images or videos just like humans do.
In simple words, computer vision allows machines to:
- Identify objects in a photo
- Detect faces
- Read handwritten text
- Understand gestures and movement
B. Why is Computer Vision Important?
Computer vision is widely used in real-world applications like:
- Face recognition in mobile phones
- Self-driving cars
- Quality checks in factories
- Medical image analysis
- CCTV surveillance
C. How Do Computers See Images?
1. Image as Data
- Computers do not “see” pictures the way humans do.
- An image is stored as a grid of pixels.
- Each pixel has a color value represented by numbers.
D. RGB Color Model
RGB stands for Red, Green, Blue – the three primary colors of light.
- Every image is made up of tiny pixels.
- Each pixel has three color values: R, G, and B.
- The combination of these values decides the color of that pixel.
Example:
- Pixel value: (255, 0, 0) → Pure Red
- Pixel value: (0, 255, 0) → Pure Green
- Pixel value: (0, 0, 255) → Pure Blue
- Pixel value: (0, 0, 0) → Black
- Pixel value: (255, 255, 255) → White
E. Understanding Image Structure
- A color image is stored in a 3D array:
- Rows × Columns × 3 (RGB channels)
- A grayscale image has only one value per pixel:
- Rows × Columns × 1
Example:
A 100×100 image will have:
- 10,000 pixels
- Each pixel has 3 values (R, G, B)
F. Common Computer Vision Tasks
| Task | Description |
|---|---|
| Image Classification | Identifying what object is in an image (e.g., dog or cat) |
| Object Detection | Finding where objects are in an image (e.g., box around a face) |
| Face Recognition | Identifying specific people by their faces |
| Image Segmentation | Coloring each pixel based on the object it belongs to |
| OCR (Optical Character Recognition) | Reading text from images (e.g., scanned documents) |
G. Real-Life Examples of CV Applications
| Application | Description |
|---|---|
| Google Photos | Detects and groups faces automatically |
| Traffic Cameras | Detect over-speeding vehicles or number plates |
| Facebook/Instagram | Detect faces and suggest tags |
| Healthcare | Analyzing X-rays and MRI scans |
| E-commerce | Visual search of products by uploading pictures |
H. Tools Used in CV (Introductory Overview)
1. OpenCV
- Open-source Python library for image processing and computer vision.
- Helps in reading, editing, and analyzing images.
2. Python Libraries Used in CV
| Library | Purpose |
|---|---|
| OpenCV | Computer vision and image tools |
| NumPy | Works with image arrays |
| Matplotlib | Display images and graphs |
Basic OpenCV Example (For teacher demonstration only):
pythonCopyEditimport cv2
img = cv2.imread('image.jpg') # Load image
cv2.imshow('Output', img) # Show image
cv2.waitKey(0) # Wait until key is pressed
cv2.destroyAllWindows()
I. Activity Suggestion for Students
Activity 1: Show an image of an object (e.g., a dog, a tree, or a pen).
Ask students:
- What do you see?
- How would you describe this image in numbers to a computer?
Activity 2: Show how RGB values change in a paint tool or photo editor.
Activity 3: Explain how filters on mobile apps work using color changes.
J. Limitations and Challenges in CV
| Limitation | Description |
|---|---|
| Lighting issues | Poor lighting can affect object detection |
| Camera quality | Low resolution may miss details |
| Occlusion | Objects may be partially hidden |
| Bias in training data | If the AI model is trained on limited images, it may not work well on others |
K. Keywords to Remember
| Term | Description |
|---|---|
| Computer Vision | A field of AI that teaches computers to see and understand images |
| RGB | Red, Green, Blue color model used to store image data |
| Pixel | Smallest unit of an image |
| Image Classification | Identifying what is in an image |
| Object Detection | Locating objects in an image |
| OpenCV | A Python library for image processing |
L. Summary of the Unit
- Computer vision helps machines “see” and understand images.
- Images are made of pixels, and each pixel has RGB values.
- CV can be used for tasks like face detection, object detection, and text recognition.
- It is widely used in phones, self-driving cars, security, and medical fields.
- Python and libraries like OpenCV are used to perform basic CV tasks.