对Harris算法的理解 程序的目标和功能 明确编程框架和模块功能 编写各模块子程序

为提升人体姿态估计在移动终端设备上的运行速度与实时性，提出一种改进的人体关键点检测算法。通过将 Mobilenetv2轻量级主干网络与深度可分离卷积模块相结合加速特征提取过程，使用精炼网络进行多尺度人体关键点预测，并利用融合网络整合多个尺度的预测结果得到最终人体关键点检测结果。实验结果表明，与传统CPM算法相比，该算法在网络模型参数量和浮点运算量明显减少的情况下PCKh@05仅下降01个百分点，具有较高的检测精度和较好的实时性。

做过修正的分辨率重建源码。基于SRCNN神经网络，MATLAB源码

基于图像掩模和击中击不中变换的优化边缘提取算法.pdf

鱼眼图像有效区域扫描算法，提取鱼眼图像！逐行逐列扫描算法，包含测试图片和得到的结果图。

1. PDF格式的PPT只读文档 2. 针孔相机模型 3. 畸变模型 4. 双目相机模型 5. RGB-D 相机模型

包含图像直方图代码，图像滤波去噪、图像边缘检测的matlab算法及实验报告

利用opencv、dilb、等第三方库，编写了一个人脸合成的C++程序，能生成中间图像及变化视频文件

【主要内容】暗通道先验去雾算法及其完美优化代码及图片效果【适合人群】数据开发【质量保障】任何问题私信我

In fine art, especially painting, humans havemastered the skill to create unique visual experiences through composing a complex interplay between the con- tent and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. How- ever, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to sepa- rate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the strik- ing similarities between performance-optimised artificial neural networks and biological vision,our work offers a path forward to an algorithmic under- standing of how humans create and perceive artistic imagery.