课程性质：分理论教学和实践教学两部分，理论课时24学时，实践课时8学时。 

     主要教学内容： 

      本课程系统介绍图像处理与内容分析的基本理论和主要应用技术，主要分为图像处理基础、图像增强滤波、图像复原与重建、图像表示与描述、目标检测与识别五个模块。

图像处理基础：讲述成像原理、取样与重建、颜色空间变换、图像质量评价。 

图像增强变换：灰度变换、空间域滤波、频率域滤波。 

图像复原与重建：非局部去噪、双边滤波、同态滤波。 

图像表示与描述：SIFT，HOG和LBP特征提取、特征描述。 

目标检测与识别：人脸检测与识别、运动目标检测。 

      课程教学目标：本课程重点教授图像处理与内容技术发展历程中具有代表性的且实用价值高的方法和技术，通过学习这些方法和技术，学生们能运用相关知识及基础理论，识别和判断复杂图像处理和内容分析问题中的关键环节；并找到正确解决这些问题的算法集合；最终能代码实现相关算法，同时整理口头报告对复杂的工程问题、可行的解决途径及其应用优势和局限性进行阐述。

The course consists of two parts, theoretical teaching and practical teaching, with 24 hours of theory and 8 hours of practice.

It systematically introduces the basic theories and major application techniques of image processing and content analysis. Five modules are included: Image Processing Fundamentals, Image Enhancement and Filtering, Image Restoration and Reconstruction, Image Representation and Description, and Object Detection and Recognition.

Module I image processing fundamentals: Imaging principles, sampling and reconstruction, color space transformation, image quality assessment. 

Module II image enhancement filtering: gray scale transformation, spatial-domain filtering, frequency-domain filtering. 

Module III image restoration and reconstruction: Non-local denoising, Bilateral filtering and homomorphic filtering.

Module IV image representation and description: SIFT, HOG and LBP feature descriptors. 

Module V object detection and recognition: Face detection and recognition, Moving object detection. 

This course primarily aims to educate students on the representative and highly valuable techniques and methods in the domain of image processing and content analysis. By studying these techniques and methods, students will acquire the necessary knowledge and fundamental theories to identify and assess crucial aspects of intricate image processing and content analysis problems. Moreover, they will learn to formulate algorithmic solutions for these problems and proficiently implement them in code. Additionally, students will develop the presentation ability to elucidate complex engineering problems, feasible approaches for resolving them, as well as the advantages and limitations associated with their applications.

1. 通过平时作业实现指定场景下经典算法的代码实现，以及通过大作业自选实际应用场景并完成算法流程设计与代码实现，培养学生能运用图像处理与内容分析课程的相关知识及科学原理，并具备识别和判断复杂工程问题中关键环节的能力；

2.通过平时作业报告撰写、大作业展示、大作业学术报告撰写和口头汇报，培养正确表达复杂工程问题的能力。