Pinned Repositories
AmhOCR
Tesseract Powered Windows Desktop OCR Application With Multiple Pre/Post Processing GUI
Emotion-Detection-in-Videos
The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.
EmotiW2017
facial-expression-recognition
Facial expression recognition deep learning examples
FacialExpressionRecognition
Fer2013 Facial Expression Recognition Keras
GeneReport
Matlab
Matlab Code
OpenIE-Spider
Extract Information from web corpus using Open Information Extraction.
QASystemOnMedicalKG
A tutorial and implement of disease centered Medical knowledge graph and qa system based on it。知识图谱构建，自动问答，基于kg的自动问答。以疾病为中心的一定规模医药领域知识图谱，并以该知识图谱完成自动问答与分析服务。
ThinkBayes2
Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.
liuhengguang's Repositories
liuhengguang/GeneReport
liuhengguang/QASystemOnMedicalKG
A tutorial and implement of disease centered Medical knowledge graph and qa system based on it。知识图谱构建，自动问答，基于kg的自动问答。以疾病为中心的一定规模医药领域知识图谱，并以该知识图谱完成自动问答与分析服务。
liuhengguang/ThinkBayes2
Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.
liuhengguang/AmhOCR
Tesseract Powered Windows Desktop OCR Application With Multiple Pre/Post Processing GUI
liuhengguang/calamari
Line based ATR Engine based on OCRopy
liuhengguang/chineseocr_lite
超轻量级中文ocr，支持竖排文字识别, 支持ncnn推理 , psenet(8.5M) + crnn(6.3M) + anglenet(1.5M) 总模型仅17M
liuhengguang/deeplearningbook-chinese
Deep Learning Book Chinese Translation
liuhengguang/facenet
Face recognition using Tensorflow
liuhengguang/GitTest
liuhengguang/HelloWPFAppC
liuhengguang/Jerry-Taro-Demo
Taro 实现的小程序商城的购物车功能、小程序分享图片功能
liuhengguang/light-weight-refinenet
Light-Weight RefineNet for Real-Time Semantic Segmentation
liuhengguang/mall-app-web
mall-shop项目建造了一个前后端分离的电商小程序项目
liuhengguang/named_entity_recognition
中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）
liuhengguang/ncnn
ncnn is a high-performance neural network inference framework optimized for the mobile platform
liuhengguang/query-server
Query Server Search Engines https://query-server.herokuapp.com
liuhengguang/requests
Python HTTP Requests for Humans™ ✨🍰✨
liuhengguang/scikit-learn
scikit-learn: machine learning in Python
liuhengguang/SSD-Tensorflow
Single Shot MultiBox Detector in TensorFlow
liuhengguang/table-ocr
liuhengguang/taro-ele
taro3.0 仿饿了么, H5,微信小程序 跨端
liuhengguang/taro-ele-serve
仿饿了么 eggjs后端
liuhengguang/taro-shop
taro 小程序商城
liuhengguang/tesseract
A .Net wrapper for tesseract-ocr
liuhengguang/tesseract-1
Tesseract Open Source OCR Engine (main repository)
liuhengguang/TFSegmentation
RTSeg: Real-time Semantic Segmentation Comparative Study
liuhengguang/UMA
Code for the paper "Generalizing Hand Segmentation in Egocentric Videos with Uncertainty-Guided Model Adaptation"
liuhengguang/weixin-java-demo-springmvc
A wechat mp and pay demo based on WxJava and springmvc.
liuhengguang/WpfDesigner
The WPF Designer from SharpDevelop
liuhengguang/wx-bootapplication
微信小程序的后台接口，整合了包含了redis，通过redis存储token，微信授权登陆，微信支付、数据库使用MySQL。