资源列表
ant
- 基本蚁群算法在TSP问题中的应用，Basic Ant Colony Algorithm for TSP-Basic Ant Colony Algorithm for TSP
Automatic-People-Counting-
- 提出了智能视频监控中基于机器学习的自动人数统计系统。。该系统通过机器学习的方法对视频序列中人的头肩部位进行准确地检测。克服了传统检测方法如连通域分析和简单模板匹配的不足。-Automatic people counting system based on machine learning in intelligent video surveillance. . The system through machine learning methods to accurately detect the
Estimation-crowd-density
- 本文提出了一种基于小波变换与灰度共生矩阵的人群密度特征提取方法，进而利用支撑向量机实现人群密度级别的估 计。实验结果表明本文提出的方法是可行的。-In this paper, based on population density characteristics of the wavelet transform and GLCM extraction method, and then using the support vector machine to estimate the crowd
MATLAB-M-FILES
- 数值分析例题，包括欧拉法、龙格-库塔法、牛顿拉夫逊算法、牛顿-斯柯特和高斯消元法-Gaussian Elimination Row Operations Newton Raphson Newton-Cotes integration Euler s method Runga-Kutta gaussjordan
Studysurvey-on-movingobjectdetection
- 学术论文：运动目标检测是当前研究热点之一,被广泛地应用于计算机视觉、视频处理等领域。将 运动目标检测的三种常用方法进行对比,总结其各自的适用性及局限性-Papers: moving target detection is one of the current research focus is widely used in the field of computer vision, video processing. Compared three common methods of movi
min_span
- minimum spaning tr-minimum spaning tree
Effectual-Method-for-Crowd-Counting
- 对固定镜头下视频序列中运动人体的检测和跟踪方法进行研究，利用灰度图像差分双向投影信息检测人体目标，提出一种基于统 计运动区域几何特征固定比例的分割算法，使用最近邻匹配方法对人体进行跟踪。-Video sequences in the detection and tracking of the movement of the human body to study under the fixed lens, bi-directional projector information using
SignalRecovery
- 压缩感知的信号恢复算法集合，包括ISD_v1.1，l1magic-1.1和sparsify_0_5-Compressed sensing signal recovery algorithms collections, including ISD_v1.1, l1magic-1.1 and sparsify_0_5
design
- 低通Chebyshev 1型滤波器，通带边界频率为1500Hz，通带波纹小于3dB，阻带边界频率2000Hz，阻带衰减为40dB，采样频率为8000Hz-Low-pass Chebyshev 1 filter passband edge frequency of 1500Hz, less than 3dB passband ripple, stopband edge frequency 2000Hz, stop-band attenuation of 40 dB, the sampling fr
MATLABDate
- 课后习题数字信号处理的MATLAB实现Date代码-Homework digital signal processing in MATLAB Date code
liblinear-1.7
- 用于解决机器学习中分类问题，里面包含python，matlab和octave的接口及示例。-LIBLINEAR is a simple package for solving large-scale regularized linear classification.
PSO
- 该工具箱将PSO算法的核心部分封装起来，提供给用户的为算法的可调参数，用户只需要定义好自己需要优化的函数并设置好函数自变量的取值范围、每步迭代允许的最大变化量等，即可自行优化-The toolkit will be a core part of the PSO algorithm package available to users of the adjustable parameters of the algorithm, the user only needs to define their