车辆的检测和计数在智能交通系统中具有重要作用，特别是在交通管理中。 交通问题已成为城市规划者多年来面临的最大问题。 更准确地检测移动车辆，几种计算机视觉技术，车辆计数是通过使用虚拟检测区域来完成的。 交通分析将计算每个任意时间段内某个区域内的车辆数量并对车辆进行分类。 但是移动车辆及其检测、跟踪和计数对于监控、规划和控制交通流量非常重要。 通过分析摄像机记录的交通流序列视频，结合虚拟检测器和斑点跟踪技术应用基于视频的解决方案技术，YOLO是必要的。 通过这项技术，我们将 Open CV 应用于实时视频应用。 这些方法帮助我们对移动的车辆进行检测、跟踪、计数和分类。

对于医学图像的融合，使用深度学习生成对抗网络

一个跨平台的库，可计算快速，准确的SIFT图像特征。 libsiftfast提供Octave / Matlab脚本，命令行界面和python界面（siftfastpy）。 使用SIMD指令和OpenMP进行了优化。

adt-bundle-windows的system-images系统镜像，版本是21

这是一个Android图片轮播的控件，里面用一个自定义空间封装了图片轮播的效果，对于学习自定义控件的人很有帮助，代码也相对简单

Multiscale Transforms with Application to Image Processing 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

使用OpenCV和CNN进行图像分割 使用OpenCV（和深度学习）进行图像分割

This exploratory paper quests for a stochastic and context sensitive grammar of images. The grammar should achieve the following four objectives and thus serves as a unified framework of representation, learning, and recognition for a large number of object categories. (i) The grammar represents both the hierarchical decompositions from scenes, to objects, parts, primitives and pixels by terminal and non-terminal nodes and the contexts for spatial and functional relations by horizontal links between the nodes. It formulates each object category as the set of all possible valid configurations produced by the grammar. (ii) The grammar is embodied in a simple And–Or graph representation where each Or-node points to alternative sub-configurations and an And-node is decomposed into a number of components. This representation supports recursive top-down/bottom-up procedures for image parsing under the Bayesian framework and make it convenient to scale up in complexity. Given an input image, the image parsing task constructs a most probable parse graph on-the-fly as the output interpretation and this parse graph is a subgraph of the And–Or graph after * Song-Chun Zhu is also affiliated with the Lotus Hill Research Institute, China. making choice on the Or-nodes. (iii) A probabilistic model is defined on this And–Or graph representation to account for the natural occurrence frequency of objects and parts as well as their relations. This model is learned from a relatively small training set per category and then sampled to synthesize a large number of configurations to cover novel object instances in the test set. This generalization capability is mostly missing in discriminative machine learning methods and can largely improve recognition performance in experiments. (iv) To fill the well-known semantic gap between symbols and raw signals, the grammar includes a series of visual dictionaries and organizes them through graph composition. At the bottom-level the dictionary is a set of image primitives each having a number of anchor points with open bonds to link with other primitives. These primitives can be combined to form larger and larger graph structures for parts and objects. The ambiguities in inferring local primitives shall be resolved through top-down computation using larger structures. Finally these primitives forms a primal sketch representation which will generate the input image with every pixels explained. The proposal grammar integrates three prominent representations in the literature: stochastic grammars for composition, Markov (or graphical) models for contexts, and sparse coding with primitives (wavelets). It also combines the structure-based and appearance based methods in the vision literature. Finally the paper presents three case studies to illustrate the proposed grammar.

image Analogies 论文, 图片处理, 图片处理, 图片处理

关于代码 这是“ Zhao，C.，Zhang，J.，Ma，S.，Fan，X.，Zhang，Y.，＆Gao，W.（2017）。”的matlab实现。表示和量化约束优先。IEEE视频技术电路和系统交易，第27（10），2057-2071页。” 用法 只需运行文件Demo_SSRQC_Deblocking.m 。 引用这项工作 如果使用此代码，请引用以下论文。 @article{zhao2017reducing, title={Reducing image compression artifacts by structural sparse representation and quantization constraint prior}, author={Zhao, Chen and Zhang, Jian and Ma, Siwei and Fan, Xiaopeng and