搜索资源列表
mfcc
- MFCC,Mel频率倒谱系数的缩写。Mel频率是基于人耳听觉特性提出来的,它与Hz频率成非线性对应关系。Mel频率倒谱系数(MFCC)则是利用它们之间的这种关系,计算得到的Hz频谱特征,MFCC已经广泛地应用在语音识别领域。(MFCC, Mel frequency cepstrum coefficient abbreviation. The frequency of Mel is based on the auditory characteristics of human ear. It is
chepai
- 该课题是车牌识别设计,含有人机交互式界面GUI,可以识别多省份车牌,换车牌调试即可。(This topic is the design of license plate recognition. It contains human-computer interactive interface GUI, which can recognize the license plates of many provinces and debug the license plate.)
tf-pose-estimation-master
- OpenPose人体姿态识别项目是美国卡耐基梅隆大学(CMU)基于卷积神经网络和监督学习并以caffe为框架开发的开源库。可以实现人体动作、面部表情、手指运动等姿态估计。适用于单人和多人,具有极好的鲁棒性。是世界上首个基于深度学习的实时多人二维姿态估计应用,基于它的实例如雨后春笋般涌现。人体姿态估计技术在体育健身、动作采集、3D试衣、舆情监测等领域具有广阔的应用前景,人们更加熟悉的应用就是抖音尬舞机(OpenPost Human Attitude Recognition Project is a
基于Kinect的连线小游戏软件
- 实现了手势识别连线,人机交互,是学习Kinect体感的基础入门代码(Implemented gesture recognition connection, human-computer interaction, is the basic entry code for learning Kinect sense)
ltc-master
- 人体动作在呈现复杂的时空序列;需要对其进行分类;长时间网络对人体动作识别(Human Action Recognition Based on Long Time Network)
MATLAB神经网络手写数字识别(GUI,论文)
- 本课题为基于MATLAB的BP神经网络手写数字识别系统。带有GUI人机交互式界面。读入测试图片,通过截取某个数字,进行预处理,经过bp网络训练,得出识别的结果。可经过二次改造成识别中文汉字,英文字符等课题。(This project is based on Matlab bp neural network Handwritten digit recognition system. With GUI human-computer interactive interface. Read in the
deeppose-master
- 使用深度学习网络处理人体关节点定位的人体姿态识别(Human pose recognition)
pytorch-openpose-master
- 本例程是我研究生阶段做的一个小项目,该项目用pytorch的深度学习框架来进行人体姿态识别,能够实现头部和身体的骨架识别!图像处理方面加入了OpenCV包进行相关的处理,希望能帮助大家!(175/5000 This routine is a small project that I did in the graduate stage. The project uses pytorch's deep learning framework to recognize human body postu
MATLAB课堂考勤(GUI)
- MATLAB课堂考勤(GUI) 该课题为基于MATLAB pca的人脸考勤系统。可以从一副图像中找出多人人脸,分割,计算人数,然后提前制作好这些人的人脸库,进行逐一识别是谁,是不是库内人脸,如是,具体是谁,如果不是,那提示库外人脸。具有友好的人机交互界面,还可以二次开发成摄像的,但是摄像头误差可能会有点。识别流程为:读取图像,人脸定位,人数统计,人脸分割,人脸识别,库内外判别。(The subject is face attendance system based on MATLAB PCA.
深度学习mtcnn
- 用市面上的摄像头,可以实现实时人脸识别功能。(The algorithm model of facenet face recognition is obtained through deep learning, and the backbone network of feature extraction is concept-resnetv1, which is developed from concept network and RESNET, with more channels and n