Search - classification by nearest neighbor

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[Graph Recognize] SpPCA DL : 0: 利用Sub-pattern PCA在Yale人脸库上进行人脸识别的matlab源代码，子模式主成分分析首先对原始图像分块，然后对相同位置的子图像分别建立子图像集，在每一个子图像集内使用PCA方法提取特征，建立子空间。对待识别图像，经相同分块后，分别将子图像向对应的子空间投影，提取特征。最后根据最近邻原则进行分类。-Sub-pattern PCA use in the Yale face database for face recognition on the matlab source code, sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the establishment of sub-image set, in each sub-image Set the use of PCA to extract the features, the establishment of sub-space. Treatment to identify images, by the same block, the respective sub-image to the corresponding sub-space projection, feature extraction. Finally, according to the principle of nearest neighbor classification.
Date : 2025-12-16 Size : 2kb User : 章格
[Graph Recognize] Character_Recognition DL : 0: 本程序主要参照论文，《基于OpenCV的脱机手写字符识别技术》实现了，对于手写阿拉伯数字的识别工作。识别工作分为三大步骤：预处理，特征提取，分类识别。预处理过程主要找到图像的ROI部分子图像并进行大小的归一化处理，特征提取将图像转化为特征向量，分类识别采用k-近邻分类方法进行分类处理，最后根据分类结果完成识别工作。程序采用Microsoft Visual Studio 2010与OpenCV2.4.4在Windows 7-64位旗舰版系统下开发完成。并在Windows xp-32位系统下测试可用。(This procedure mainly refers to the paper, "OpenCV based offline handwritten character recognition technology" to achieve the recognition of handwritten Arabia digital work. The recognition work is divided into three major steps: preprocessing, feature extraction, classification and recognition. The pretreatment process is mainly to find ROI sub images of the image and normalized by the size of the feature extraction image into feature vector classification using k- nearest neighbor classification processing, according to the classification results to complete the identification work. Procedures using Microsoft, Visual, Studio 2010 and OpenCV2.4.4 in Windows 7-64 bit ultimate system development completed. And in Windows xp-32 bit system test available.)
Date : 2025-12-16 Size : 22.49mb User : Kas_Zhao
[Graph Recognize] 基于ASM和K近邻算法的人脸脸型分类_张倩 DL : 0: 针对人脸特征分类问题，提出一种基于主动形状模型(ASM)和 K 近邻算法的人脸脸型分类方法。将 Hausdorff 距离作为 K 近邻算法的距离函数，利用 ASM 算法提取待测图像的特征点，对点集进行归一化后计算人脸轮廓特征点与样本库中所有样本点集的 Hausdorff距离，根据该距离值，通过 K 近邻算法实现待测图像的脸型分类。实验结果证明，该方法分类正确率高、速度快、易于实现。(Aiming at the problem of face feature classification, this paper proposes a new face classification algorithm based on Active Shape Model(ASM) and K-nearest neighbor algorithm. It extracts feature points of face by ASM algorithm, normalizes all feature points, and computes Hausdorff distance between feature points and every sample of each class. The face is classified by K-nearest neighbor algorithm with the Hausdorff distance computed. Experimental results show that the algorithm has high classification accuracy and speed, and it is easy to realize.)
Date : 2025-12-16 Size : 1.28mb User : 夜湮