Pinned Repositories
algorithm
algorithms
Minimal examples of data structures and algorithms in Python
audio-visual-integration
Cognitive processes for interpreting audio-visual scenes
awesome-java-leetcode
:crown: LeetCode of algorithms with java solution(updating).
bashrc
Common usable configuration files for bash
depth_clustering
:taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor.
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.
ext3DLBP
Extended three-dimensional rotation invariant local binary patterns (LBP)
fastfusion
Volumetric 3D Mapping in Real-Time on a CPU
hacker
python绝技:运用python成为顶级黑客 这本书的源码
Glenzh's Repositories
Glenzh/algorithm
Glenzh/algorithms
Minimal examples of data structures and algorithms in Python
Glenzh/audio-visual-integration
Cognitive processes for interpreting audio-visual scenes
Glenzh/awesome-java-leetcode
:crown: LeetCode of algorithms with java solution(updating).
Glenzh/depth_clustering
:taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor.
Glenzh/ext3DLBP
Extended three-dimensional rotation invariant local binary patterns (LBP)
Glenzh/fastfusion
Volumetric 3D Mapping in Real-Time on a CPU
Glenzh/hacker
python绝技:运用python成为顶级黑客 这本书的源码
Glenzh/holistic_scene_parsing
Code for ECCV 2018 paper - Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image
Glenzh/HomeCredit
Glenzh/interview_python
关于Python的面试题
Glenzh/Java-Interview
👨🎓 Java 相关知识点
Glenzh/leetcode-solutions
My solutions for all (~350) leetcode problems, including premium.
Glenzh/MLAlgorithms
Minimal and clean examples of machine learning algorithms
Glenzh/models
Models and examples built with TensorFlow
Glenzh/muduo
A C++ non-blocking network library for multi-threaded server in Linux
Glenzh/MyVimrc
Glenzh/opencv_contrib
Repository for OpenCV's extra modules
Glenzh/openpose
OpenPose: A Real-Time Multi-Person Keypoint Detection And Multi-Threading C++ Library
Glenzh/ORB_SLAM
A Versatile and Accurate Monocular SLAM
Glenzh/ORB_SLAM2
Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities
Glenzh/python-doc
translate python documents to Chinese for convenient reference 简而言之，这里用来存放那些Python文档君们，并且尽力将其翻译成中文~~
Glenzh/python-gems
Beautifully constructed python scripts
Glenzh/slam
An implementation of particle filtering algorithm for simultaneous localization and mapping (SLAM) in autonomous robots.
Glenzh/slambook
Glenzh/testgit
Glenzh/testgit2
Glenzh/The-Art-Of-Programming-By-July
本项目曾冲到全球第一，干货集锦见本页面最底部，另完整精致的纸质版《编程之法：面试和算法心得》已在京东/当当上销售
Glenzh/vim-galore
:mortar_board: All things Vim!
Glenzh/wtfpython
A collection of surprising Python snippets and lesser-known features.