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class="dot"></span> <a class="category-link" href="/tags/Notes/">Notes</a> <span hidden itemprop="about" itemscope itemtype="http://schema.org/Thing"><a href="/tags/Notes/" itemprop="url"><span itemprop="name">Notes</span></a></span></div></div></div></div><div class="post-wrapper"><div class="post post-v3 white-box reveal shadow floatable blur" itemscope itemtype="http://schema.org/Article"><link itemprop="mainEntityOfPage" href="https://blog.mhuig.top/p/f0765214/"><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="Magicland"></span><span hidden itemprop="post" itemscope itemtype="http://schema.org/CreativeWork"><meta itemprop="name" content="特征向量和特征值的几何本质"><meta itemprop="description" content="本文探讨特征值(λ)与特征向量(x)的定义(Ax=λx)，阐释其几何本质为空间向量线性变换中保持方向的缩放（旋转与伸缩），物理意义为矩阵作用下向量仅拉伸/压缩λ倍，介绍传统求解步骤（计算特征多项式、求解特征值、解方程组）及基于子矩阵特征值编码原矩阵隐藏信息的新公式|v_i,j|²计算特征向量元素的方法。"></span><div class="headimg-div"> <a class="headimg-a" 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itemprop="url"><span itemprop="name">Notes</span></a></span></div></div></div></div><div class="post-wrapper"><div class="post post-v3 white-box reveal shadow floatable blur" itemscope itemtype="http://schema.org/Article"><link itemprop="mainEntityOfPage" href="https://blog.mhuig.top/p/483290b5/"><span hidden itemprop="publisher" itemscope itemtype="http://schema.org/Organization"><meta itemprop="name" content="Magicland"></span><span hidden itemprop="post" itemscope itemtype="http://schema.org/CreativeWork"><meta itemprop="name" content="特征和分类器"><meta itemprop="description" content="本文系统阐述计算机视觉中特征与分类器的核心原理。特征作为图像独特属性，需满足同类样本相似、异类样本差异的特性，分为全局与局部两类，后者（如HOG、SIFT）可有效处理遮挡。分类器基于特征向量实现图像分类，但受噪声、样本量等因素限制难以完美。文中详述HOG通过梯度方向直方图编码对象形状，SIFT通过尺度空间极值检测实现尺度旋转不变性，为视觉识别提供关键技术支撑。"></span><div class="headimg-div"> <a class="headimg-a" href="/p/483290b5/"><img itemprop="image" class="headimg lazyload" alt="特征和分类器" src="https://static.mhuig.top/npm/imbox@0.0.15/c/66.webp" class="lazyload" 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