Computer Vision Challenge: Traditional vs Machine Learning Techniques
Welcome to the Computer Vision Challenge! In this exercise, we aim to explore your understanding of traditional computer vision techniques as well as machine learning methods. Your insights will help us understand your approach to solving complex computer vision problems.
Computer vision is a rapidly evolving field with an array of methods and techniques. The choice between traditional methods like edge detection with OpenCV and modern machine learning techniques can significantly impact the efficiency and effectiveness of your solution. We're interested in understanding your thought process when deciding between these two approaches.
Please provide detailed answers to the following questions:
Traditional Computer Vision:
- Explain how the Canny edge detection algorithm works. What are the main steps involved in this algorithm?
- How does histogram equalization enhance the contrast of images? What could be a potential downside of using this technique?
- Describe the process of template matching in image processing. When would it be beneficial to use this method, and when might it not be suitable?
- Explain the difference between geometric and photometric image transformations. Provide an example where each might be used.
Machine Learning Techniques:
- What is the role of convolutional neural networks (CNNs) in image classification? How do they differ from traditional neural networks?
- Explain how the use of pooling layers in a CNN contributes to the model's ability to recognize objects regardless of their position in the image.
- Describe the concept of transfer learning in the context of deep learning for computer vision. When would it be advantageous to use pre-trained models?
- Explain the difference between object detection and image segmentation. What types of neural network architectures are commonly used for each task?
Your answers should be detailed and well-structured. Here's an example of how you might answer the first question:
"The Canny edge detection algorithm works in several steps. First, it applies a Gaussian filter to smooth the image and reduce noise. Then, it finds the intensity gradients of the image. Non-maximum suppression is then applied to get rid of spurious response to edge detection. The algorithm then applies a double threshold to determine potential edges. Finally, it tracks edges through the image to ensure that they are connected."
Create a new repository on your GitHub account to submit your solutions. Include a text file or markdown file with your answers, and a README.md file explaining any additional thoughts or comments you have.
We're interested not just in your answers, but in your thought process and understanding of these concepts. Please commit your initial thoughts and edits as you refine your answers, with clear and informative commit messages.
Good luck! We look forward to reviewing your responses.