Brain Tumour Detection and Classification Using MATLAB Code

Pattern Classification一书中的MATLAB代码，提供了经典的各种算法的MATLAB实现。

This is a Harris corner detection program written in MATLAB. You can give it a try to detect keypoints in images based on the Harris corner detection method.

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。

图像强度值由反照率分量和阴影分量确定。反照率分量描述了地球表面不同物体的物理性质，土地覆盖类别因其固有的物理材料而彼此不同。因此，我们恢复高光谱图像的内在反照率特征以利用空间语义信息。然后，我们使用支持向量机（SVM）对恢复的固有反照率高光谱图像进行分类。SVM尝试最大化最小边距以实现良好的泛化性能。实验结果表明，在视觉质量和三个定量指标方面，具有内在反照率特征方法的SVM比最先进的方法实现了更好的分类性能。如果您使用这些代码，请引用论文： @文章{ZhanJEI2017july,作者= {詹、坤和王、海波和谢、元歌和张、楚彤和敏、玉芳}， title = {用于高光谱图像分类的反照率恢复}，杂志= {电子影像杂志},年= {2017},体积= {26}，数字= {4}，页数= {043010}， doi = { http://dx.doi.org

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。

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。

该项目提供了基于梯度下降的softmax回归实现，专注于MNIST数据分类。此外，还包含多个领域的Matlab仿真代码，涵盖智能优化算法、神经网络预测、信号处理等应用。

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。

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.