HOG SVM Matlab Code-Sliding Window Face Detection

本程序可以进行人脸检测，并且用矩形框出人脸位置。适合初学者使用，是个比较好的工具。

智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真。

使用Matlab实现Otsu算法进行老鼠检测的代码。该方法通过图像处理技术，自动确定图像的最佳阈值，以分割老鼠与背景。

人脸图像特征提取 MATLAB 代码 - ML_internship_projects：您好，我叫 Kulendu Kashyap Chakraborty，目前是古瓦哈提 GIMT 大学一年级 CSE 本科生。我是 Cosmic Skills 的暑期实习生，参加机器学习课程。这是一个很棒的课程，因此，在项目完成后，现在是提交项目的时间。对于项目，我在将代码文件转换为 .rar 格式时遇到问题，因此我决定创建此存储库并在邮件中共享连续项目的链接。项目清单： 1. 项目名称：字符识别项目 项目目的：该项目的目的是开发一种将图像作为输入并从中提取字符（字母、数字、符号）的工具。图像可以是手写文档或打印文档。它可以用作打印记录中数据输入的一种形式。开发工具：该项目基于机器学习，我们可以提供许多数据集作为软件工具的输入，这些数据将被机器识别并从中获取相似的模式。我们可以将 MATLAB 或 Octave 用作此产品的构建工具，但建议 Octave 处于初始状态，因为它免费且易于使用。项目输出图像：结论：该项目完成了字符分类和图像处理技术的学习。

To run this code, you will need to check out and compile Ripser. Start by running the following commands: git clone --recursive https://github.com/ctralie/Math412S2017.git Enter the ripser directory and run the following commands: make ripser make ripser-coeff Once these steps are complete, you can run the provided examples: TDA.py: Code to compute persistent homology using Ripser, including basic command-line wrapping and plotting functionality. SlidingWindow.py: Code for calculating sliding window embeddings for one-dimensional signals, using cos(t) + cos(pi t) as an example, and reporting persistence H1 and H2. This also shows how to compute PCA. HorseWhinnies.py: Quantifying the periodicity and quasi-periodicity of audio segments from the file horsewhinnie.wav. MusicSpeech.py: Running TDA on audio novelty features to discover rhythmic periodicity in music. The example compares travel.wav and speech.wav.

MATLAB小波图像边缘检测源码，实现了利用小波对图像边缘检测。含实验报告。

MATLAB图像分割肿瘤代码，脑肿瘤检测与分类。此源代码根据患者的MRI扫描检测脑肿瘤区域，然后通过MATLAB进行的机器学习将其分为良性和恶性类型。 %Source Code clc %% Input [I,path]=uigetfile('.jpg;.png','select a input image'); str=strcat(path,I); s=imread(str); %% Filter num_iter = 10; delta_t = 1/7; kappa = 15; option = 2; disp('Preprocessing image please wait . . .'); inp = anisodiff(s,num_iter,delta_t,kappa,option); inp = uint8(inp); inp=imresize(inp,[256,256]); if size(inp,3)>1 inp=rgb2gray(inp);

This project demonstrates a MATLAB implementation for fruit detection in 3D LiDAR point clouds using the Velodyne VLP-16 LiDAR sensor (Velodyne LIDAR Inc., San Jose, CA, USA). The dataset contains 3D point clouds of 11 Fuji apple trees and corresponding fruit position annotations. The implementation uses a Support Vector Machine (SVM) classifier for fruit detection. Setup and Requirements Clone the Code: To begin, clone the repository using the command:git clone https://github.com/GRAP-UdL-AT/fruit_detection_in_LiDAR_pointClouds.git Data Preparation: Create a folder named “data” in the directory where the code is saved. Inside this folder, store the ground truth data and point cloud data in subdirectories named “AllTrees_Groundtruth” and “AllTrees_pcloud”, respectively. Prerequisites: Ensure you have the following installed in MATLAB: MATLAB R2018 (other versions not tested) Computer Vision System Toolbox Statistics and Machine Learning Toolbox Dataset: The LFuji-air dataset is stored in the /data folder. This dataset provides the necessary LiDAR data for training and evaluation. Cross-validation: Perform cross-validation to evaluate the performance of the model in detecting fruit within the LiDAR point clouds. Notes The implementation is optimized for MATLAB R2018 and tested specifically on this version. The project utilizes a dataset that includes both ground truth fruit positions and point cloud data for training the detection model.

MATLAB 寻峰代码 - ASE: 染色质变异项目的代码 该代码实现了MATLAB中进行寻峰的功能，主要用于处理染色质变异项目中的数据分析。通过此代码，可以自动化地识别和提取数据中的峰值，帮助进一步分析染色质变异。代码结构清晰，适用于各种数据集，并提供了可调的参数来优化峰值检测的精度和效果。 核心功能: 自动化峰值检测 支持多种数据格式 提供详细的调参功能 示例代码： % Peak detection example [peaks, locs] = findpeaks(data); % 处理结果 通过此功能，可以为复杂的染色质变异数据分析提供快速、可靠的处理手段。