This chapter will provide an overview of computer vision, its history, and its applications. Students will learn about the importance of computer vision in various industries and the challenges it addresses.

In this chapter, students will learn about various image processing techniques, including filtering, edge detection, and image enhancement. They will gain hands-on experience in applying these techniques to real images.

This chapter will focus on feature extraction methods, such as SIFT, SURF, and SVM. Students will learn how to extract features from images and use them for object recognition and detection.

In this chapter, students will learn about machine learning algorithms used in computer vision, including neural networks, support vector machines, and deep learning. They will gain a deep understanding of how these algorithms are used to recognize patterns in images and videos.

This chapter will focus on object recognition and detection techniques, including template matching, HOG, and R-CNN. Students will learn how to apply these techniques to real-world problems, such as detecting faces in images or recognizing traffic signs.

In this final chapter, students will learn about advanced computer vision techniques, such as scene understanding, semantic segmentation, and object tracking. They will gain hands-on experience in applying these techniques to real-world problems, such as recognizing objects in complex scenes or tracking objects in videos.

In this project, students will apply the concepts and techniques learned throughout the course to develop a computer vision solution for a real-world problem. They will work in teams to design, implement, and present their project to the class.