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This is for my computer graphics final project. I use OpenGL to construct simple 3D dimension objects.
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<h1>综述文章的撰写思路</h1>
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<h1>Enterprise Application Development Notes</h1>
<h2>Some ideas about the course</h2>
<p>这门课程更加侧重于实践的应用开发，需要通过实践的方式加深对相关技术和技巧的理解。自己需要在设计开发的时候多去思考每一个组件如此设计的原因，自己多去思考理解才能加深对相关内容的理解。</p>
<h2>Notes</h2>
<h3>Javaweb的入门</h3>
<h4>JSP的底层原理</h4>
<p>JSP->Java文件->（编译）->Java.class->web</p>
<blockquote>
<p>H5直接写到writer当中，Java是通过response直接返回html的标签</p>
</blockquote>
<h4>Servlet的基本原理</h4>
<p>Servlet的3种初始化方式，其中有2种需要在web.xml当中进行配置，一种是通过@WebServlet标签进行</p>
<h4>单例模式</h4>
<p>单例模式的初始化有3种方式，其中的两种中一种是饿汉式，一种是懒汉式。</p>
<blockquote>
<p>饿汉式：Java在加载类的时候即完成初始化</p>
</blockquote>
<blockquote>
<p>懒汉式：使用方法的时候才进行初始化——可能会存在一定的线程安全问题</p>
</blockquote>
<h4>其他</h4>
<p>早期前后端通常是放在一起，常见的有Javaweb、MVC</p>
<p>后来逐渐出现了前后端分离</p>
<h4>请求转发和重定向</h4>
<blockquote>
<p>常见的操作码：<br>成功 200</p>
<p>不可预知 500</p>
<p>找不到 404</p>
<p>重定向 301</p>
</blockquote>
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<h1>Computer Graphics Notes</h1>
<h2>多边形扫描转化算法和区域填充算法</h2>
<h3>多边形扫描转换算法</h3>
<p>能够充分利用相似性，但是硬件实现困难</p>
<p>逐点判断:射线交点 圆弧<br>射线交点:奇数判断为内部 偶数判断为外部 注意需要考虑奇异点的情形</p>
<p><img src="C:\Users\DELL\Desktop\Blog\blog\source\pics\pic1.jpg" alt="奇异点的类型"></p>
<p>可以将逐点判断扩展为区域填充–可以直接使用交点的组合判断内部或者外部 使用快速增量可以在相邻扫描线求交点<br>$$<br>y_{i+1} = y_{i} + 1 \<br>x_{i+1} = x_{i} + 1/k<br>$$<br>ET表（Edge table）–分类边表<br>AEL表（Activity Edge Link）–活动边表</p>
<h4>对应的Data Structure</h4>
<p><img src="C:\Users\DELL\Desktop\Blog\blog\source\pics\pic2.jpg"></p>
<p>ET表：</p>
<p>ymax用来判断是否需要剔除该条线段</p>
<p>xmin用来表示最低点的横坐标</p>
<p>delta表示1/k</p>
<p>AEL表：（AEL和当前边的交点数一定是偶数）</p>
<p>ymax用来判断是否需要剔除该条线段</p>
<p>xmin用来表示当前扫描线与边的交点</p>
<p>delta表示1/k</p>
<h3>边填充算法</h3>
<p>使用正负相消实现填充，外部穿过偶数次点，内部穿过奇数次点</p>
<h3>区域填充算法</h3>
<p>区域填充算法有内区域表示和边界表示两种表示方式</p>
<p>区域填充算法经典的有简单种子填充算法和扫描线种子填充算法</p>
<p>两种连通区域：</p>
<p>四连通区域：有时不能通过狭窄的区域</p>
<p>八连通区域</p>
<p><img src="C:\Users\DELL\Desktop\Blog\blog\source\pics\pic3.jpg"></p>
<p>内区域表示（四点+递归）</p>
<figure class="highlight c"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br></pre></td><td class="code"><pre><span class="line"><span class="type">int</span> <span class="title function_">FloodFill4</span><span class="params">(<span class="type">int</span> x, <span class="type">int</span> y, <span class="type">int</span> oldColors, <span class="type">int</span> newColors)</span> {</span><br><span class="line"> <span class="keyword">if</span>(getPixel(x,y) == oldColors) {</span><br><span class="line"> setPixel(x, y, oldColors, newColors);</span><br><span class="line"> <span class="comment">//下面递归调用上下左右的4个点</span></span><br><span class="line"> FloodFill4(x<span class="number">-1</span>, y, oldColors, newColors);</span><br><span class="line"> FloodFill4(x+<span class="number">1</span>, y, oldColors, newColors);</span><br><span class="line"> FloodFill4(x, y+<span class="number">1</span>, oldColors, newColors);</span><br><span class="line"> FloodFill4(x, y<span class="number">-1</span>, oldColors, newColors);</span><br><span class="line"> }</span><br><span class="line">}</span><br></pre></td></tr></table></figure>
<p>注：以上为伪代码</p>
<blockquote>
<p>思考：如何将递归改为非递归</p>
</blockquote>
<p>使用系统栈的方式可以将递归改为非递归</p>
<p>系统栈容量的限制为1M</p>
<p>进行浮点数表示：</p>
<figure class="highlight c"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="built_in">fabs</span>(A-B) < eps</span><br></pre></td></tr></table></figure>
<p>进行整数表示：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">A == B</span><br></pre></td></tr></table></figure>
<p>扫描线种子填充算法的特殊情形：<br>若有多段，每一段都需要入栈</p>
<p><img src="C:\Users\DELL\Desktop\Blog\blog\source\pics\pic4.jpg"></p>
<p>若出现以上的特殊情形，则需要将1和2两个点都入栈</p>
<h3>Idea:</h3>
为什么需要ET?
<blockquote>
<p>建立边表有序，方便后续进行排序</p>
</blockquote>
<p>为什么需要AEL,AEL和ET之间有什么区别?</p>
<p>在进行画面刷新的时候，如何实现ET表不需要重新建立?</p>
<p>思考多边形内部如何填充图案?</p>
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<a href="/2023/10/09/Algorithm%20Notes/" class="article-date">
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<h1>Algorithm Notes</h1>
<p>感谢授课老师刘征老师,年过半百仍然在三尺讲台辛勤耕耘</p>
<h2>递归与分治</h2>
<p>这部分内容是上一个月学习的内容，有时间补充一下</p>
<h2>动态规划</h2>
<p>动态规划是根据当前状态确定最优解的一种方法</p>
<h4>动态规划的基本性质</h4>
<p>最优子结构性质 重叠子问题性质 最优化问题 无后效性</p>
<p>无后效性的深入理解：</p>
<p>1.当前的值只由<code>过去的值</code>决定，而不能由未来的值决定</p>
<p>2.一旦求解，就不能发生变化</p>
<p>3.后续子问题不会影响先前子问题</p>
<h4>记忆化递归</h4>
<p>定义一个状态矩阵，把计算过的子问题的解记录下来</p>
<p>遇到一个问题时，先查询是否已经求解且有记忆</p>
<p>若有，则有限求解记忆值</p>
<p>否则，无限求解并记忆</p>
<h4>Notes</h4>
<p>子问题的最优解是父问题最优解的必要条件—->需要综合子问题求解父问题</p>
<p>子问题不是相互独立的</p>
<p><code>dp[M][N]</code>以空间换时间，可以避免重复计算</p>
<p>由于重叠子问题的特点决定了不能仅使用分治的方法求解</p>
<p>子问题的最优解只依赖于当前状态（马尔可夫过程）</p>
<p><strong>辨析递归和递推：</strong></p>
<p>递推：人为划定边界，自底向上递推</p>
<p>递归：递归下降找边界，自下向上合并到根问题</p>
<p>子问题的改性定义</p>
<p>dp[i] [j]:是存储/记忆(i,j)的最优解</p>
<p>动态规划需要确定起点和方向</p>
<p>动态规划不是调用函数，而是利用已经求解的值进行求解</p>
<p>最优解是标量解，最优解的构成是矢量解</p>
<p>状态的2个基本名词：<br>state:表示普通状态（多个要素的状态）</p>
<p>status：表示标志性状态</p>
<p>记忆化递归—-是动态规划的初级版本，可以避免重复的递归</p>
<p><strong>辨析记忆化递归和动态规划</strong></p>
<p>记忆化递归：2个递归调用来填充未知元素，需要进行判断</p>
<p>动态规划：2个已知元素来填充未知元素，不需要进行判断</p>
<p>数列是无限的，数组是有限的</p>
<h3>经典问题</h3>
<h4>矩阵连乘问题</h4>
<h4>最长公共子序列</h4>
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<a href="/2023/10/08/4K-shadow/" class="article-date">
<time datetime="2023-10-08T14:41:18.458Z" itemprop="datePublished">2023-10-08</time>
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<h1>4K-Shadow代码配置问题总结</h1>
<p>本篇博客用来总结自己在复现、配置4K-Shadow这篇文章的代码的时候遇到的问题，以及自己的一些解决方法。4K-Shadow对应的文章是ICCV2023的“<strong>High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net</strong>”。</p>
<h2>复现代码的过程当中遇到的问题</h2>
<p>1.我希望绘制一个条形图，并保存为EPS格式的文件。代码中使用了<code>plt.style.use('Solarize_Light2')</code>来应用Matplotlib的样式</p>
<ul>
<li><input disabled="" type="checkbox"> 由于Solarize_Light2不支持EPS格式，同时存在透明度的问题，因此我做出的修改是替换为支持EPS格式的文件。</li>
</ul>
<h2>配置代码的过程当中遇到的问题</h2>
<p>现在代码需要完成的任务是调整model的架构。对于原有的model需要进行修改，替换为自己方法的model，这个是这一步自己需要实现的功能。</p>
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<time datetime="2023-10-05T10:26:02.457Z" itemprop="datePublished">2023-10-05</time>
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<h1>Ultra-high-resolution image segmentation总结</h1>
<h2>超高分辨率图像分割的最新的一些文献</h2>
<p>1.CascadePSP(CVPR2020):提出了一种可以生成更加精确mask的方法</p>
<p>2.GLNet(CVPR2019)：提出了全局分支进行降采样，局部分支进行裁剪分割，并将二者进行融合的方法</p>
<p>3.GRNet(CVPR2020):提出了GRNet，包括PPN(补丁建议子网络)，将建议斑块和琐碎补丁区分开来</p>
<p>4.From Contexts to Locality: Ultra-high Resolution Image Segmentation via Locality-aware Contextual Correlation(ICCV2021):将分割结果和上下文信息结合起来，能够生成高质量的mask</p>
<p>5.UHRSNet：提出了全局和局部的融合以及局部和局部的融合，</p>
<p>6.WicoNet:适用于HRrsi图像的语义分割，通过上下文转换器来学习上下文的相关性</p>
<p>7.ISDNet：使用了浅层网络和深层网络，能够融合浅层特征和深层特征</p>
<p>8.CRM：将特征图和细化目标对齐，并聚合特征以此来补充细节</p>
<h2>图像分割的金字塔方法的代表文献</h2>
<h2>Document Shadow Removal的代表文献</h2>
<p>1.BEDSR-Net:the first deep network specifically designed for document image shadow removal</p>
<p>2.Water-filling:estimate the shading artifacts, and use Lambertian surface model</p>
<p>3.Removing the shadows from Images of Documents:correct the image with the shadow map to produce the final unshadowed output</p>
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<time datetime="2023-10-05T08:28:00.357Z" itemprop="datePublished">2023-10-05</time>
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<h1>CVPR投稿的格式要求</h1>
<p>以下为CVPR最新的投稿格式要求：</p>
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<article
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<a href="/2023/10/04/%E6%A0%91%E8%8E%93%E6%B4%BE%E4%BC%A0%E6%84%9F%E5%99%A8%E7%9A%84%E5%BC%80%E5%8F%91/" class="article-date">
<time datetime="2023-10-04T06:27:29.055Z" itemprop="datePublished">2023-10-04</time>
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<h1>树莓派传感器/微信小程序的开发</h1>
<p>这个小开发是为了完成大学生创新创业训练项目而做的。自己自学了树莓派传感器开发，微信小程序制作，远程服务器的使用以及如何实现前后端的交互。</p>
<h2>微信小程序的开发</h2>
<p>自己主要负责的任务是微信小程序的开发，微信小程序开发的语言和之前学习过的前端三大件有比较高的相似性，因为在之前的基础上，自己能够做到更好的上手开始项目的开发。</p>
<p>后续将继续讲述项目开发的一些经历和感受。</p>
<p>参考的一些博客：</p>
<p><a href="[微信开发者工具文件配置及结构_微信开发工具构建最多300个文件-CSDN博客](https://blog.csdn.net/qq_42012782/article/details/113189291)">微信小程序代码的基本结构</a></p>
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