Python for Computer Vision Practice Exam

Python for Computer Vision Practice Exam

Python for Computer Vision is the use of Python programming language to enable computers to interpret, analyze, and understand images and videos. It allows machines to recognize objects, detect patterns, and even understand scenes in images, similar to how humans perceive the world. Python’s simplicity, combined with powerful libraries like OpenCV, PIL, and TensorFlow, makes it a preferred choice for building computer vision applications.

Learning Python for Computer Vision helps developers create applications that can automate visual tasks, from facial recognition and object detection to self-driving car navigation and industrial inspection. This certification teaches candidates how to process images, extract features, apply machine learning techniques, and implement real-world computer vision solutions efficiently.

Who should take the Exam?

This exam is ideal for:

• AI/ML Developers 
• Computer Vision Engineers 
• Data Scientists 
• Software Engineers 
• Students & Professionals 

Skills Required

• Python programming.
• Linear algebra and probability.
• Machine learning concepts.
• Logical thinking and problem-solving skills.
• Digital image fundamentals.

Knowledge Gained

• Image processing with Python libraries (OpenCV, PIL).
• Feature extraction and object recognition techniques.
• Motion detection and video analysis.
• Applying machine learning models to visual data.
• Building real-world applications like facial recognition or automation tools.
• Optimization of computer vision algorithms for performance.

Course Outline

The Python for Computer Vision Exam covers the following topics - 

1.    Introduction to Computer Vision

• Definition and applications
• Importance of computer vision
• Python as a tool for vision tasks

2. Python Basics for Computer Vision

• Python libraries (OpenCV, NumPy, PIL)
• Setting up the environment
• Reading and displaying images

3. Image Processing Techniques

• Image filtering and enhancement
• Grayscale conversion and thresholding
• Histogram equalization

4. Geometric Transformations

• Image resizing, rotation, and cropping
• Affine and perspective transformations
• Contour detection and analysis

5. Feature Detection and Matching

• Edge detection (Canny, Sobel)
• Corner detection (Harris, Shi-Tomasi)
• Keypoints and descriptors (SIFT, SURF, ORB)

6. Object Detection and Recognition

• Haar cascades for face detection
• YOLO and SSD object detectors
• Template matching

7. Video Processing

• Capturing video streams
• Motion detection and tracking
• Background subtraction

8. Machine Learning for Computer Vision

• Image classification using ML models
• Training simple CNNs for image recognition
• Using pre-trained models

9. Deep Learning for Vision Applications

• Introduction to CNN architectures
• Transfer learning and fine-tuning
• Deploying deep learning models

10. Advanced Topics

• Optical character recognition (OCR)
• Gesture and pose detection
• 3D vision and depth estimation

11. Performance Optimization

• Efficient image processing techniques
• Parallel processing with Python
• Reducing computation time

12. Real-World Projects

• Facial recognition system
• Object tracking application
• Automated inspection system

13. Future Trends in Computer Vision

• AI-powered vision in robotics
• Augmented reality and virtual reality applications
• Emerging tools and libraries