基于损坏的谷粒百分比的米级图像处理技术的探索 这项工作是COMP9517计算机视觉主题@UNSW 2020的一部分 我们执行了以下任务（有关详细信息，请参阅报告）： 任务1： 实施Iso数据强度阈值处理以在给定的水稻图像（灰度图像）中分离背景和前景。图像具有双峰像素强度直方图。 任务2： 实现两遍连通分量标记算法，对每个图像中的米粒进行计数。 任务3： 从Task2中获得的连接的组件标签中，使用最小像素面积作为阈值来计算受损稻粒的百分比。 档案： Report.pdf：有关实施过程和意见的详细信息 my_program.py：我的python实现 Input_image：测试图像-rice_img1.png，rice_img2.png，rice_img6.png，rice_img7.png 输出：所有输出均保存在此文件夹中

斯蒂加诺 ，一个纯Python隐写模块。 隐写术是写隐藏消息的艺术和科学，这种方式使发送者和预期的接收者之外的任何人都不会怀疑消息的存在，这是一种通过隐蔽的安全方式。 因此，Stegano提供的功能仅隐藏消息，而不进行加密。 隐写术通常与加密一起使用。 对于报告问题，请在此处访问跟踪器： : 安装 $ poetry install stegano 您将能够在Python程序中使用Stegano。 如果仅要将Stegano安装为命令行工具，请执行以下操作： $ pipx install stegano pipx将Python软件包提供的脚本（系统范围内的脚本）安装到单独的vi

digital image processing paper 来源：http://www.google.com.hk/url?q=http://www.wamis.org/agm/pubs/agm8/Paper-5.pdf&sa=U&ei=oFwRTb-2FIeyvwO84vzUDQ&ved=0CA8QFjAA&usg=AFQjCNEkthb1F3AhSkYoDR_XolBFx-Z6Bg

大学计算机xuanxiu课程：image processing and computer vision 课程 个人作业以及源代码

利于制作图像处理的软件，更好的理解图像处理的过程。

绘图机器人V3 关于 Drawing Bot是一款免费的开源软件，可将绘图仪/绘图仪/ 3D打印机的图像转换为线条图。 它还可以用作视觉艺术家的应用程序，以从图像/视频创建风格化的线条图。 它适用于Windows，Mac和Linux。 特征 带有实时图形预览的高级用户界面 多种路径查找算法-可配置以创建独特的绘图样式 自动路径优化，可实现更快的绘图-线简化，合并，过滤，排序 笔设置：可配置的颜色/笔划宽度/分布粗细/混合模式-非常适合多层绘图。 60多个图像滤镜用于更改输入 自动CMYK分离 用户可配置的绘图区域，具有填充/缩放模式 原始颜色/灰度采样专用笔 预设：可以保存/导入/导出，以便与其他用户共享不同的样式 导出可以按/笔或按/绘图导出为多种文件类型 批处理：自动转换整个图像文件夹。 GCode-可配置的工程图区域，XYZ偏移/自动归位。 支持的文件类型 Import For

项目：建立交通标志识别程序 该项目 该项目的目标/步骤如下： 加载数据集 探索，总结和可视化数据集 设计，训练和测试模型架构 使用模型对新图像进行预测 分析新图像的softmax概率 用书面报告总结结果 依存关系 该项目要求： tensorflow-gpu == 1.7.0 scipy == 1.0.0 matplotlib == 2.0.0 numpy == 1.14.2 opencv-contrib-python == 3.4.0.12 sklearn == 0.18.2 数据集探索 数据集摘要 。 加载数据集和基本摘要 加载数据集后，我得到以下摘要信息： 训练例数：34799 测试例数：12630 验证示例数：4410 图像形状为：（32 32，3） 类数标签：43 探索性可视化 该图像网格表示从训练集中每个类别中选择的一个随机图像 分配 现在，我们将探索分

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.