- Image classification: Build an image classifier that can distinguish between different types of objects, such as cars, bicycles, and people. This can be done using techniques such as convolutional neural networks (CNNs).
- Object detection: Create a program that can detect objects within an image and draw bounding boxes around them. This can be done using techniques such as Haar cascades or deep learning-based models.
- Face detection: Build a program that can detect faces within an image or a video stream. This can be done using techniques such as Haar cascades, HOG+SVM, or deep learning-based models.
- Image segmentation: Create a program that can separate an image into different regions based on their visual properties, such as color or texture. This can be done using techniques such as k-means clustering, graph cuts, or deep learning-based models.
- Image filtering: Implement different types of filters, such as blur, sharpen, edge detection, and noise reduction, to enhance or modify an image. This can be done using techniques such as convolution.
- Optical character recognition (OCR): Build a program that can recognize text within an image and convert it into machine-readable text. This can be done using techniques such as Tesseract OCR.
- Lane detection: Create a program that can detect the lanes on a road from a video stream. This can be done using techniques such as Hough transforms or deep learning-based models.
- Object tracking: Build a program that can track objects across frames in a video stream. This can be done using techniques such as Kalman filters or particle filters.
These projects will give you hands-on experience with different computer vision techniques and algorithms, and help you develop a deeper understanding of the subject.