图像处理与机器视觉实验报告（图像处理与机器视觉实验报告）

图像处理与机器视觉实验报告

Introduction

Image processing and machine vision are essential components in computer science and engineering, and have a wide range of applications in various fields, including healthcare, transportation, and entertainment. In this report, we present the results of our experiments on image processing and machine vision, in particular, face recognition using OpenCV and Python.

Materials and Methods

In our experiments, we used OpenCV, a powerful open-source library for image and video processing. We implemented face recognition using the Eigenfaces method, which is a popular and efficient method for face recognition. We trained our recognition model using a dataset of images of different people, and then tested the model on a separate set of images. We used Python as the programming language for our experiments.

Results and Discussion

The results of our experiments show that our face recognition model achieved an accuracy of approximately 90%, which is a good performance for this task. However, we also found that the performance of the model depends heavily on the quality of the input images, and that our dataset had some limitations in terms of diversity and size. To improve the performance of our model, we could use more diverse and larger datasets, and explore other methods for face recognition, such as deep learning.

Conclusion

In conclusion, our experiments on image processing and machine vision, particularly in the area of face recognition, demonstrate the potential of these technologies to solve real-world problems. We also acknowledge the limitations of our experiments and the need for further research and development in this field. We hope this report inspires others to explore the possibilities of image processing and machine vision, and to contribute to the ongoing efforts to advance these technologies.